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What is data modeling? A Visual Introduction with Examples

In their simplest form, data models are diagrams that show 3 dimensions: 1. what data an organization collects, 2. in which section of the organization it is collected, and 3. how each section’s data relates to others. Data modeling (modelling) is the process of creating those data models.

But for most people, this description isn’t very helpful, and it makes things seem more complex than they really are. I believe data models are easiest to understand in context, so let’s consider a business.

Businesses consist of three fundamental units: suppliers , products , and customers . Each of these units consists of data collected independently . For example, the sales team records customer name, location, and sale amount , whereas the production teams records product name, price, and size .

Businesses usually want to compile this data in a company database , which allows them to analyze it and better serve their customers.

To do so, they develop data models to understand what data they have, where it is, and how it all relates. Only then are they confident enough to put it in database. Since every business is different, data modeling is the process of creating a data model that meets the specific structure of the business in question.

Data models: data tables, data objects, & databases

To understand data models and how to model them, you need to know about data tables , data objects and databases .

A data table is a table of columns and rows in which the left most column is a unique ID (aka primary key ), and the columns to its right are characteristics of that unique ID. Data tables are what most people think of when they hear “data.” For example, look at this data table of vendor data:

A database consists of many data tables either compiled into one big table or stored individually. The reason we consider multiple individual tables as part of one database is that they relate to each other. If not, they’re just different tables in space.

Don’t forget, you can get the free Intro to Data Analysis eBook , which covers data fundamentals (including models, tables, and objects).

The important thing to see here is that one database can also have multiple data models . As we said, a data model is just a combination of data tables that relate to each other. Once you store them together, they’re a model. And since databases can store a huge number of data tables, it can store multiple models. This sounds complicated, but it’s easy to understand with a picture:

Data object is another name for data tables within a database. The reason we use a separate names is that, at more advanced levels, data models contain objects other than tables (bit that’s outside the scope of this article). You will often hear or read data analysts refer to “data objects.” Nine time out of ten, they’re referring to tables, but you should be aware that there are others as you progress as a data analyst.

Types of Data Models with Real-World Example

So, we know that data models are pictorial representation of the contents and relationships of data tables. But there is not a on-size-fits-all data model, especially in a business.

C-level executives don’t want to see the gritty details behind the model. They just want to see the high-level relationships. At the same time, a database analyst wants as much detail as possible to ensure the relationships are correct.

The need for different views has led to three primary types of data models:

• Conceptual Data Models. High-level.
• Logical Data Models. Mid-level.
• Physical Data Models. Low-level.

Though they differ, each of these models consists of at least one of these elements:

• Entities. Entities represent data tables (or more generally, data objects) that contain data relevant for comparison.
• Primary keys . Primary keys are another word for the unique ID of the data table within an entity.
• Attributes . Attributes show additional information stored under each unique ID in an entity.
• Relationships . Relationships are shown by lines and symbols and explain how entities interact. The most common are “one to many,” “one to one,” and “many to many.” Relationships are also referred to as cardinality . The notation for these relationships is called crows foot notation , and it’s very simple. Here are the most important examples:

Real-World Example

Imagine you own a wholesale e-commerce company that sells watches, and its called Batch Watch . Your three business units are vendors, products, and customers. You buy metal and glass from the vendors to build your high-quality watches, then sell them to boutiques and other retail stores. And let me tell you… people love your watches!

Let’s look at conceptual, logical, and physical data models using this example.

Conceptual Data Model

Conceptual models are the most general of the three. You don’t need to be a data analyst to understand them. Conceptual data models show the high-level business units that collect data, but do not show any information about the contents . They sometimes include pictures to more easily communicate their structure.

Using our Batch Watch example, a conceptual model may look as simple as this:

Logical Data Model

Logical models go a step further than conceptual models to show the primary key and attributes within each entity, as well as the relationships between them. Logical data models are the most common type of data model.

To understand logical data models, let’s look at this example of our three entities in Batch Watch to understand their primary keys and attributes. Then we can see how they’re related with crows feet notation.

As you can see, the primary keys (PKs) for each entity are a unique ID of the key component. The attributes under them provide a view on what data is stored within these data tables.

We saw a simplistic view of relationships in the conceptual model, and now we’ll add more detail with the crows feet notation . What will this help us understand? It will show how many primary keys in each entity links into primary keys in the other entities. Remember, the most common of these is “one to many.”

In the case of Batch Watch , each vendor supplies general materials for many of our watches ( product ), OR only one of our watches. This is because one watch requires a special kind of glass. Each product then sells to many retailers .

Using crows feet notation, it looks like this in a data model:

The logical model thus helps us understand that “one and only one” Vendor ID (along with their attributes) links into “one or many” Product IDs. Then, one and only one Product IDs link into many Retailer IDs.

Note: “many to many” relationships do not exist in data models

When analysts learn about crows feet relationships, they often get stuck on the idea of “many to many” relationships. After all, if one PK in Entity A links to many PKs in Entity B, aren’t there already “many” combinations? The answer is yes, “many to many” relationships exist, but they’re already accounted for in multiple “one to many” relationships .

Don’t let this confuse you (most professionals have a hard time explaining it). Just know that in data modeling, we do not use “many to many” relationships. Instead, whenever more than one PK links into other PKs, we simplify. We use two entity relationships of one to many.

From Logical Model to Database

The logical model is great, but it’s difficult to understand without seeing what it looks like once all of the entity’s data tables are linked together in a database .

Imagine we have only two vendors , three products , and four retail partners . Even with so few players, the database becomes complex, and quickly.

Here’s what it would look like using our example. I leave out the attributes in this picture so it’s easier to understand how this database is compiled:

This complexity is arguably the most important reason for using data models. They simplify the relationships between business units and entities so that it’s digestible, and easy to act on. Without them, it would be difficult to work with databases at all!

Why use a data model?

If they’re so complex, why even bother with databases and data models at all? The most obvious reason is that data is key to extracting insights and improving a company’s competitive edge .

When companies ignore data, they miss out on opportunities to understand their operations, markets, and customers better.

Moreover, data is becoming a compliance necessity . Businesses must be able to show how their company operates through data to be compliant with growing government data regulations.

Physical Data Model

Once you understand logical models, physical data models are easy. Physical data model entities are exactly the same as logical model entities, but they add in the types of data that each PK an attribute uses, as well as the number of characters . Here’s an example:

As you can see, the added value of physical models is the detail they provide on data in its tables. Experienced data modelers are able to quickly understand how the data model translates to the database and make decisions based on this knowledge .

Types of data are seemingly endless. The most common types include text , numeric , and boolean (true/false), but they can be as complex as the following list:

• Integer – any number that is not a decimal. Examples include -11, 34, 0, 100.
• Tinyint – an integer, but only numbers from 0 to 255
• Bigint – an integer bigger than 1  trillion
• Float – numbers too big to write out, and the scientific method is needed
• Real – any fixed point on a line
• Date – the date sorted in different forms, including “mm/dd/yyyy” (US), “dd/mm/yyyy” (Europe), “mmmm dd, yyyy”, and “mm-dd-yy” among many more.
• Time – the time of day, broken down as far as milliseconds
• Date time – the date and time value of an event
• Timestamp – stores number of  seconds passes since  1970-01-01 00:00:00’ UTC
• Year – stores years ranging from 1901 to 2155 in two-digit or four-digit ranges
• Char – fixed length of characters, with a maximum of 8,000
• Varchar – max of 8,000 characters like char, but each entry can differ in length (variable)
• Text – similar to varchar, but the maximum is 2GB instead of a specific length
• nchar – fixed length with maximum length of 8,000 characters
• nvarchar – variable length with maximum of 8,000 characters
• ntext – variable length storage, only now the maximum is 1GB rather than a specific length
• binary – fixed length with maximum of 8,000  bytes
• varbinary – variable length storage with maximum bytes, topped at 8,000
• clob – also known as  C haracter  L arge  O bject, is a type of sub-character that carries Unicode texts up to 2GB
• blob – carries big binary objects
• xml – a specific data type that stores XML data. XML stands for extensible markups language, and is common in data bases

Data Modeling tools

To build all three types of data models, you will need a tool. The most important one to have in your toolbox is Microsoft Powerpoint. While it is heavily manual, its available in almost every professional setting. Especially as a a consultant, you need to be flexible.

With that said, the best tools for data models are ERP modeling tools and UML modeling language . They’re common among systems administrators and software engineering, where structures similar to data models are an everyday event.

• Entity Relationship (E-R) Model programs. As you can imagine, structural models are not unique to data models. In fact, the idea of entities and relationships is a driving principle in engineering . That’s where E-R models surfaced, as well as the programs to automate them. These programs are user friendly and require minimal coding skills that you can learn as you go . While a tutorial on E-R technology is outside the scope of this article, you can check out a trial account with Lucidchart for free if you want to get your feet wet. It’s common in big companies.
• UML (Unified Modeling Language). UML is a “coding” language for entity relationship models. I put “coding” in quotes because it’s actually called a mockup language , but the principle is the same. You use a program to write code that becomes the model. For many people, coding separately to build a model that represents an underlying database feels like overkill. For this reason, UML is considered an advanced technique.

Data modeling steps

We’ve talked a lot about types of data models, their content, and why we should use them, but what about actually building one ? What are the steps needed to build a data model, or “do” data modeling?

To answer this question, let’s take the perspective of an external data consultant rather than an internal analyst (since internal specialists can sometimes be biased).

The most important and first step is understanding the organization and its data collection capabilities and desires . Without data, there isn’t much to model.

Then, the consultant must understand the goals of the organization and set up a data collection plan to be approved by business decision-makers. That’s the bird’s eye view.

More specifically, 12 steps to data modeling are:

• Understand what kinds of data analysis and data insights the company is looking for . This is a crucial step. It consists of speaking with decision-makers to better understand
• Identify key business units. This step consists of identifying the most important business units. These are not necessarily departments, as products are a key unit as well. A good test is to ask yourself: which units do business units usually refer to when they ask “why”?
• Perform a data collection audit. This step consists of identifying which business units need data in order to build the business model. In almost all cases, they will be the units identified in step one, but not always. Business units without data collection, or without sufficient data collection, should be noted in a text document. A good test is to ask yourself: what data dimensions will this unit need to answer the “why” questions from business decision-makers?
• Perform a data collection GAP analysis. This step consists of identifying what technical and non-technical changes must be made in order to execute the data collection requirements identified in step 3.
• Build a draft conceptual data model. This step is the first model you build. It’s the conceptual model mentioned earlier in the article .
• Get feedback for this structure from management. In this step you get a critical view on how well your work responds to the decision-maker requirements identified in step 1.
• Adapt to feedback from management. Make changes to you conceptual model based on feedback from decision-makers.
• Build logical data model. This step consists of building a logical model with the information gathered and feedback. We move on from the conception model even without managerial approval because it’s better to adapt progressively than get stuck on the conceptual model.
• Get feedback and make adjustments. Repeat step six, but with the logical model. While decision makers may not want to see PKs and attributes, the conceptual structure remains, and it’s useful to get additional feedback and approval.
• Create physical data model to share with database management teams and BI. Using either an E-R modeling program or UML, build a physical model to share with relevant teams.
• Implement data collection improvements. This step consists of implementing the technical and non-technical requirements identified in step 4’s GAP analysis. You may need to work with external providers for this.
• Build dashboard. Build a dashboard to show the conceptual, logical, and physical models in a user-friendly framework for everyone in the company. Dashboard creation is often possible through the database management system the company stores its data in.

Techniques and best practices

Data modeling best practices include the following items:

• Where possible, use a single Enterprise Resource Planning (ERP) program to ensure ongoing integrity of data collection and modeling process.
• Always document decision-maker requirements to ensure coherence throughout the implementation process.
• Do not use both UML and E-R model programs. Choose one and stick with it. Since data collection and data modeling are an ongoing effort, you want it to keep it as user friendly as possible.

Advantages and disadvantages

While data modeling is an industry standard, it has its disadvantages. We’ve spoken a lot about the good parts, but here’s an overview of the advantages and limitations of data modeling:

Advantages:

• Easy to access
• Creates structure for an organization
• Flexible to the needs of any organization
• Ensures the integrity of data by splitting

Disadvantages:

• Compounding complexity. As company entities and the data stored in them become more complex, so does the data model. In fact, it can become so complex that it looses its simplistic appeal.
• Rigidity . Once a data model is put in place, it is incredibly difficult to modify. This article described the steps to set up a data model, but maintaining and modifying one is another story.
• Dependance with growth . Just like any web of logic, any change to one element has an impact on many others. While the purpose of the model is to limit this risk, as the business units grow, so too does the difficulty of maintaining entity independence.

Data modeling is a must-know technique for any good data analyst. It’s a window into the complex database that hosts any company’s data. While it may seem intimidating at first, you’ll quickly adjust to the logic as you spend more time with different materials.

At AnalystAnswers.com, I believe that data analysis is becoming more and more critical in our digital world. Learning it shouldn’t break the bank, and everyone should have access to understanding the data that’s growing under our fingertips every day.

To learn more about data analysis, check out the Understand Data Analysis tab!

About the Author

Noah is the founder & Editor-in-Chief at AnalystAnswers. He is a transatlantic professional and entrepreneur with 5+ years of corporate finance and data analytics experience, as well as 3+ years in consumer financial products and business software. He started AnalystAnswers to provide aspiring professionals with accessible explanations of otherwise dense finance and data concepts. Noah believes everyone can benefit from an analytical mindset in growing digital world. When he's not busy at work, Noah likes to explore new European cities, exercise, and spend time with friends and family.

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Data modelling: a guide to techniques, models and best practices

Are you looking to streamline your data for better analysis and decision-making data modelling is the critical first step. this practice shapes raw data into a clear structure, optimising not just storage and retrieval, but also comprehension across diverse teams. through this guide, you’ll uncover the key principles of data modelling, explore its various forms, and access the tools to refine your data strategy., key takeaways.

• Data modelling is a strategic process that creates a visual representation of an information system, critical for simplifying, organising, and optimising databases, and supports business analysis and decision-making.
• There are various types of data models, including conceptual, logical, and physical models, each serving different purposes and providing different levels of abstraction, suitable for various audiences within an organisation.
• Data modelling techniques and processes are essential to accurately represent and organise data, with a range of available tools specifically designed to enhance the efficiency and effectiveness of database management and support evolving industry trends.

What is data modelling and why its important?

Data modelling is the process of creating a visual representation of an information system, illustrating the linkages among data points and organisational structures.

It serves as a blueprint for both the structure and the flow of data within an organisation. This visual representation aids in understanding and managing data, but its importance extends far beyond its graphical nature.

At its core, data modelling is about creating a system that not only stores information efficiently but also allows for effective retrieval and innovative use of that data . It helps organisations make sense of complex data landscapes, enabling them to harness the full potential of their information assets.

Furthermore, data modelling is not just a technical exercise; it is a strategic business activity . It supports business analysis and decision-making by providing a clear framework for data collection , storage, and use. It lays the groundwork for data-centric initiatives.

If you want to find out more about how you can use data in your business, take a look at this:

• Data Transformation: the complete guide for effective data management
• Creating a data-driven culture: a roadmap for organizational transformation
• The role of Business Data Analysis in a data-oriented project

What are the different types of data models?

Delving deeper into the labyrinth of data modelling, we encounter three distinct types of models :

• conceptual,
• and physical models.

Each of these models serves different purposes and caters to diverse audiences within a company. From how data is physically stored in a database to the levels of data abstraction, these models pave the way for diverse approaches to data representation and organisation.

Conceptual data modelling

Conceptual data modelling, a type of conceptual model, is akin to painting a broad picture of a company’s data landscape. It sets up and defines business rules and concepts, providing an abstract representation of the conceptual data model necessary to support business requirements without being tied to any specific technology implementation.

This high-level approach focuses on how different data elements interconnect, and the overarching relationships that define the business domain. By doing so, it acts as a bridge between the technical data modelers and stakeholders, translating complex data structures into a language that everyone can understand.

Conceptual data modelling is the cornerstone of a robust data management strategy, paving the way for future growth and adaptation.

Logical data modelling

Logical data modelling is the process of creating a visual representation of the structure of an organisation’s data. Logical data models (LDMs) typically use diagrams to illustrate the relationships between different data entities, attributes, and the rules governing those relationships.

Logical data modelling is a vital step in bridging the gap between abstract concepts and tangible database structures. Its benefits, ranging from improved data quality to enhanced communication and better risk management , make it an indispensable tool for any organisation looking to leverage data effectively.

By investing in logical data modelling, businesses can ensure that their data systems are aligned with their goals, adaptable to change, and capable of supporting complex, data-driven processes.

Physical data modelling

A physical data model is the tangible manifestation of data organisation in the realm of data modelling. Often referred to as a physical model, it outlines the actual implementation of the database, including:

• constraints
• relationships between tables

The design elements in physical data models, such as tables, columns, and relationships, directly influence the efficiency of business intelligence systems by effectively managing data elements.

Creating narrower tables helps to minimise long scans or reads, proving valuable for handling large data volumes or when operating across multiple tables. This is just one of the many ways physical data models contribute to optimal database performance.

Find out more about the data tasks and workflows:

• Data preprocessing: a comprehensive step-by-step guide
• Data classification: the backbone of effective data security
• Data visualisation: unlock insights in your data

Data modelling techniques

A toolbox for data modelling would be incomplete without an array of techniques to tackle diverse data scenarios. These tools include:

• Relational models
• Hierarchical models
• Network models
• Entity-relationship models
• Dimensional models
• Object-oriented models
• Graph models

Relational data modelling

Stepping into the realm of relational data modelling, we encounter a technique that has been ruling the roost since the mid-1990s. In this model, data is organised in a table-like structure where each table represents a specific entity and each row a specific record.

What sets relational data models apart is their ability to depict entities with various relationships, including:

• One-to-many
• Many-to-one
• Many-to-many

The overarching purpose of the relational model is to describe different relationships between data entities, making it a popular choice in many database systems.

Hierarchical data modelling

Hierarchical data modelling arranges data in a tree structure consisting of one root and multiple connected data nodes. Within the model, relationships are represented as single one-to-many relationships between different levels of data.

A practical application of the hierarchical model can be seen in a supermarket where one parent department has several child aisles under it. Though originated in mainframe databases, hierarchical data models are still utilised by businesses today through systems like IMS.

Network data modelling

Unlike hierarchical database models, the network model allows for each record to have multiple parent and child records, thereby forming a more complex structure.

Adopted by the CODASYL Data Base Task Group in 1969, the network model was considered to offer a more natural way to model relationships between entities compared to the hierarchical model. Despite its heyday in the 1970s, the network model continues to influence modern database systems.

Entity relationship data modelling (E-R model)

The Entity-Relationship (ER) Model is a visual storyteller of data structures. It uses a diagrammatic approach to represent the structure of database systems, illustrating the relationships between different entities.

The ER Diagram is composed of entities, attributes, and relationships which are depicted using specialised symbols such as rectangles, ellipses, diamonds, and lines.

Introduced by Peter Chen in 1971, ER Diagrams provide a standardised modelling approach for conceptual database design. They include

• entities (represented by rectangles),
• attributes (classified into key, composite, multivalued, and derived attributes; represented by different shapes)
• and relationships (represented by diamonds).

Dimensional data modelling

Dimensional data modelling aims to simplify data structures for better performance and speed of data retrieval in a data warehouse environment . This modelling technique supports data reading, analysis, and summarisation processes.

While dimensional data models have numerous benefits, they also present certain limitations, such as the need for domain knowledge in designing schemas and challenges in maintaining data integrity during warehouse loading.

Object-oriented data modelling

Object-oriented data modelling (OODM) is a data modelling approach that uses object-oriented concepts to represent data structures and their relationships.

This methodology integrates the principles of object-oriented programming (OOP), where data is encapsulated within objects, and objects are instances of classes that define the attributes and behaviors (methods) they can have.

It provides numerous benefits, including natural mapping to real-world concepts, reusability, flexibility, and improved maintainability.

Graph data modelling

Graph data modelling is like a map of a city, depicting various landmarks (nodes) and the paths (relationships) connecting them. Nodes in a graph data model represent entities with a unique identity, and can contain properties that hold name-value pairs of data.

Relationships in a graph model connect nodes and are directional, representing actions or verbs between entities. Graph databases enforce the rule of no broken links, ensuring relationships always point to existing endpoints.

Properties in a graph model are attributes stored on nodes or relationships, which can answer specific queries about the data.

Data modelling: process and best practices

The data modelling process involves six steps:

• Identifying business entities
• Defining key properties
• Creating a draft ER model
• Identifying data attributes
• Mapping attributes to entities
• Finalising and validating the data model

This iterative nature of the data modelling process allows for continuous improvement and adaptation.

What else is worth noting – data modelers adhere to certain best practices to ensure the overall quality of the database. Data models should emphasise:

• Data completeness
• Traceability
• Consistency

Applying data normalisation techniques can minimise redundancy and enhance flexibility in data models to support evolving business requirements.

Building data models around business processes facilitates easier navigation, evaluation of data, and appropriate placement within the model. To mitigate data bias and improve fairness in model predictions, techniques such as over-sampling, under-sampling, SMOTE, and population adjustment can be implemented.

Selecting the right data modelling tools

A variety of data modelling tools are available, offering broad compatibility and specialised features for SQL, NoSQL, and Cloud databases.

Here are the key steps to consider when selecting the right data modelling tools:

• identify your requirements (scope of modelling, complexity, scale and collaboration needs),
• evaluate key features (e.g. user interface, usability, integrations, customisation, reverse and forward engineering),
• assess performance and scalability,
• consider collaboration features and security,
• evaluate cost and licensing (pricing models and total cost of ownership),
• support and community,
• review case studies and testimonials.

By considering these factors, you can choose a tool that not only meets your current requirements but also scales with your organisation’s evolving needs.

And if you need a consultation or an IT partner for data solutions – do not hesitate to get in touch with us ! Our specialists are prepared to support you in enhancing, controlling, and fully utilising your data resources!

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What is Data Modeling?

May 31, 2019 | 4 Min Read

Author: Michael Nixon

Market News

Data modeling is the process of organizing and mapping data using simplified diagrams, symbols, and text to represent data associations and flow. 

Engineers use these models to develop new software and to update legacy software. Data modeling also ensures the consistency and quality of data. Data modeling differs from database schemas . A schema is a database blueprint while a data model is an overarching design that determines what can exist in the schema.

Benefits of Data Modeling

• Improved accuracy, standardization, consistency, and predictability of data
• Expanded access to actionable insights
• Smoother integration of data systems with less development time
• Faster, less expensive maintenance and updates of software
• Quicker identification of errors and omissions
• Reduced risk
• Better collaboration between teams, including non-developers
• Expedited training and onboarding for anyone accessing data

Types of Approaches

There are four primary approaches to data modeling .  

1. Hierarchical

A hierarchical database model organizes data into tree-like structures with data stored as interconnected records with one-to-many arrangements. Hierarchical database models are standard in XML and GIS.  

2. Relational

A relational data model, AKA a relational model, manages data by providing methodology for specifying data and queries. Most relational data models use SQL for data definition and query language.

3. Entity-relationship

Entity-relationship models use diagrams to portray data and their relationships. Integrated with relational data models, entity-relationship models graphically depict data elements to understand underlying models. 

Graph data models are visualizations of complex relationships within data sets that are limited by a chosen domain.

Types of Data Models

There are three primary types of data models. 

1. Conceptual , defining what data system contains, used to organize, scope, and define business concepts and rules.

2. Logical , defining how a data system should be implemented, used to develop a technical map of rules and data structures.

3. Physical , defining how the data system will be implemented according to the specific use case.

Role of a Modeler

A data modeler maps complex software system designs into easy-to-understand diagrams, using symbols and text to represent proper data flows. Data modelers often build multiple models for the same data to ensure all data flows and processes have been properly mapped. Data modelers work closely with data architects.

Data Modeling versus Database Architecture

Data architecture defines a blueprint for managing data assets by aligning with organizational needs to establish data requirements and designs to meet these requirements.

Database architecture and data modeling align when new systems are integrated into an existing system, as part of the overall architecture. With data modeling, it’s possible to compare data from two systems and integrate smoothly.

Snowflake Data Cloud and Data Modeling

The  Snowflake’s platform is ANSI SQL-compliant, allowing customers to leverage a wide selection of data modeling tools tailored to specific needs and purposes. 

Snowflake has introduced several features enhancing data modeling capabilities. 

Snowpark Enhancements : The Snowpark ML Modeling API , now generally available, allows data modelers to use Python ML frameworks like scikit-learn and XGBoost for feature engineering and model training within Snowflake. This integration simplifies the data modeling process by enabling direct operation on the data stored in Snowflake, reducing the need for data movement.

Advanced Analytics with Snowflake Cortex : The new ML-based functions for forecasting and anomaly detection provide data modelers with powerful tools to perform complex analyses directly through SQL. This simplifies the process of incorporating advanced analytics into data models, making it accessible even to those with limited ML expertise.

Developer Experience with  Snowflake Python API : In public preview, this API enhances Python's integration with Snowflake, making it easier for data modelers to manipulate and interact with data within Snowflake using familiar Python constructs.

Learn more about the Data Cloud , or see Snowflake’s capabilities for yourself. To give it a test drive, sign up for a free trial . 

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What Is Data Modeling? Tips, Examples And Use Cases

May 4, 2023 21 mins read

Data modeling can be considered the foundational stone of data analytics and data science. It gives meaning to the enormous amount of data that organizations produce. It generates an effectively organized representation of the data to assist the organizations with better insights into data understanding and analysis .

The domain of data utilization is vast beyond the limitations of a human. It is being used as a source for personalized social media advertisement, discovering treatments for numerous diseases, and more. The data is readable by software machines but generates significant results with maximized accuracy. It simplifies the data by implementing rational rules assignment.

The task of getting the required data, transforming it into an understandable representation, and using it as needed for the average user is simplified through data modeling. It plays a pivotal role in transforming data into valuable analytics that helps organizations make business strategies and essential decisions in this fast-paced era of transformation.

Data modeling provides in-depth insights into organizations’ daily data despite the process’s complexity. It helps organizations in efficient and innovative business growth.

Data Modeling Definition

Let us understand what data modeling is. So, data modeling conceptualizes the data and relationships among data entities in any sphere. It describes the data structure , organization, storage methods, and constraints of the data.

• Data modeling promotes uniformity in naming, rules, meanings, and security, ultimately improving data analysis. These models represent data conceptually using symbols, text, or diagrams to visualize relationships. The main goal is to make the data available and organized however it is used.
• Data modeling helps store and organize data to fulfill business needs and allow for the processing and retrieving of information of use. Thus, it is a crucial element in designing and developing information systems.

Firstly, data modeling signifies the arrangements of the data that already exist. Then this process proceeds to define the data structure, relationship of entities, and data scope that is reusable and can be encrypted.

Data modeling creates a conceptual representation of data and its relationships to other data within a specific domain. It involves defining the structure, relationships, constraints, and rules of data to understand and organize information meaningfully. So, data modeling conceptualizes the data and relationships among data entities in any sphere. It describes the data structure, organization, storage methods, and constraints of the data.

• Data modeling signifies the data arrangements of the data that already exist. Then this process proceeds to define the data structure, relationship of entities, and data scope that is reusable and can be encrypted.
• Data modeling creates a conceptual representation of data and its relationships to other data within a specific domain. It involves defining the structure, relationships, constraints, and rules of data to understand and organize information meaningfully.

Data modeling is essential in software engineering, database design, and other fields that require the organization and analysis of large amounts of data. It enables developers to create accurate, efficient, and scalable systems by ensuring the data is properly structured, normalized, and stored to support the organization’s business requirements.

Importance of Data Modeling

Data modeling is the stepping stone of the data management process to achieve business objectives and other essential utilization. It is the fundamental phase of the data management process to achieve crucial business objectives and other vital usages that assist in decision-making driven by data analysis.

The following insights can help comprehend the importance of data modeling.

• We may comprehend the data structure, relationships, and limitations by building a data model.
• By making it easier to ensure everyone working on the project is familiar with the data.
• You can avoid uncertainties and inaccuracies.
• Data continuity, reliability, and validity are improved by addressing issues.
• Provides a common language and a framework or schema for better data management practices.
• Processing insights from raw data to discover patterns, trends, and relationships in data.
• Improved data storage efficiency to cancel out useless data.
• Streamlined data retrieval with organized storage.
• Good database schema designs can significantly reduce data redundancy issues.
• Cost efficiency and an increase in system performance due to reduced and optimized data storage.

Steps of the Data Modeling Process

What we select to make a data model depends mainly on the data characteristics and the individual business requirements. The steps of the data modeling process for data engineering include the following:

Step 1: Requirements gathering

Gathering requirements from analysts, developers, and other stakeholders and then realizing how they need the data, how they plan to use it, and any blockers they face regarding the quality or other data specifics.

Step 2: Conceptual data modeling

In this step, you must map entities, attributes, and the relationship among them in a generalized concept of understanding the data.

Step 3: Logical data modeling

The third step of the data modeling process is to develop a logical interpretation of the data entities and the relationship among them. The logical rules definition is also defined in this step.

Step 4: Physical data modeling

A database based on the logical rules defined in the previous step is implemented physically, where attributes are defined with primary and foreign keys of a data entity table.

Types of Data Modeling

Below are the types of data modeling that are being implemented:

1. Conceptual Data Modeling

Data entities are modeled as high-level entities with relationships when using this method. Rather than focusing on specific technologies or implementations, it focuses on business needs.

2. Logical Data Modeling

This type of data modeling focuses on just the high-level view of the data entities and relationships. It has comprehensive data models in which entities, relationships, and attributes are stipulated in detail, along with constraints and implementation rules.

3. Physical Data Modeling

It is the type of data modeling in which the model is defined physically, constituting tables, database objects, data in tables and columns, and indexes defined appropriately. It mainly focuses on the physical storage of data, data access requirements, and other database management.

4. Dimensional Data Modeling

Dimensional data modeling requires data arrangement into ‘facts’ and ‘dimensions.’ Where ‘facts’ mean metrics of interest and ‘dimensions’ mean attributes for facts’ context

5. Object-Oriented Data Modeling

This specific data model is based on realistic scenarios represented as objects and independent attributes, with several relationships in between.

Data Modeling Techniques

Several techniques are used to model data, of which some are and would tell you what is data modeling in general:

1. Entity-relationship Modeling

This technique uses entities and relationships to represent their associations to perform conceptual data modeling. It utilizes subtypes and supertypes to represent hierarchies of entities that share common attributes and distinct properties, cardinality constraints to identify the number of entities that can take part in a relationship and are expressed in the form of symbols, weak entities depend on another entity for existence, recursive relationships that occur when an entity has a relationship with itself and attributes to help describe entities and are their properties.

2. Object-oriented Modeling

Object-oriented data modeling is linked to relational databases and broadly used in software development and data engineering. It represents data as objects with attributes and behaviors, and relationships between objects are defined by inheritance, composition, or association.

3. NoSQL Modeling

NoSQL modeling is a technique that uses non-relational databases to store semi-structured, flexible data in an unstructured format which usually utilizes key-value pairs, documents, or graph structures. Since the database is non-relational, the modeling technique implemented differs from relational database modeling techniques. With column-family modeling, data is usually stored as columns where each column family is a group of relevant columns. With graph modeling, data is usually stored as nodes and edges which represent entities and the relationship between entities, respectively.

4. Unified Modeling Language (UML) Modeling

A data modeling technique that uses visual modeling to describe software systems with diagrams and models and is used for complex data flow modeling and for defining relationships between multiple data entities. Used as a standard to visualize, design, and document systems, it constitutes dynamic diagrams like sequence, class, and use case diagrams used to model data and system behavior. One possible way to extend UML is by using class diagrams and by representing data entities and their attributes.

5. Data Flow Modeling

Data flow among different processes utilizes the data flow modeling technique, constituting different diagrams showing how a process and its sub-processes are interlinked and how the data flows in between.

6. Data Warehousing Modeling

This technique is used to design data warehouses and data marts, which are used for business intelligence and reporting. It involves creating dimensional models that organize data into facts and dimensions and creating a star or snowflake schema that supports efficient querying and reporting.

Each method has its own pros and cons. Ensure that the technique you use is per your project’s requirements and the data available.

Data Modeling Use Cases

Data modeling is used in various industries and contexts to support various business objectives. Some everyday use cases of data modeling include:

• Predictive Modeling: Creating a statistical or mathematical model to predict the future based on data for sales forecasting, resource allocation, quality controlling and demand planning. Identifying new patterns and relationships will lead to new insights and possibly better opportunities.
• Customer Segmentation: Through the division of customers into different groups on the basis of behaviors, preferences, demographics or other characteristics, you can do customer segmentation which is a popular data modeling use case.
• Fraud Detection: Identifying fraudulent activities by analyzing patterns and data inconsistency is now possible due to data models that can detect fraud patterns like an individual filing multiple claims immediately after they get the policy.
• Recommendation Engines: Recommendation engines for eCommerce, search engines, movies, and TV shows, and many more industries use data models that rely on quick data access, storage and manipulation which keeps them up-to-date at all times without affecting the performance and user experience.
• Natural Language Processing: Utilizing topic modeling that auto-learns to analyze word clusters through text and Named Entity Recognition (NER) that detects and classifies significant information from text, we can perform Natural Language Processing (NLP) on social media, messaging apps and other data sources.
• Data governance: A process of ensuring that a company’s data is extracted, stored, processed and discarded as per data governance policies. It has a data quality management process to ensure monitoring and improvement of data gathering. Tracking data from the original state to a final state, maintaining metadata that ensures a track record of data for accuracy and completion, ensuring data security and compliance. Data stewards are responsible for the integrity and accuracy of specific data sets.
• Data integration: If any data has ambiguity or inconsistency, then the data integration use case is ideal for identifying those gaps and modeling the data entities, attributes, and relationships into a database.
• Application development: Data modeling plays a key role in data management and intelligence reports, data filtration, and other uses while developing web applications, mobile apps, and dynamic user experience interfaces like business intelligence applications and data dashboards. Data modeling is a versatile tool supporting various business objectives, from database design to data governance and application development.

Also, see: How to Download Images from Amazon? Tools and Tips Explained

Tips for Effective Data Modeling

Practical data modeling tips are as follows:

1. Identify the purpose and scope of the data model

To build a data model that not only addresses users’ needs but also high-performance and scalable, you need to know what problem it is solving, the data sources for the model, the type of data the model would store, the kind of people who would be using the model, level of details required for them, key entities, attributes and their relationships. You would also need to address the data quality requirements by all stakeholders.

2. Involve stakeholders and subject matter experts

Involving stakeholders and subject matter experts is crucial when designing a data model as they provide valuable insight into the business needs and can help identify potential issues early on.

3. Follow best practices and standards

There are a few things that you need to make sure are right and up to their standards when creating a data model. Firstly, choose an industry-wide accepted standardized modeling notations like Entity-Relationship (ER) diagrams, and Unified Modeling Language (UML), Business Process Model and Notation (BPMN), etc consistently to make sure things are clear and understandable.

4. Use a collaborative approach

Make sure you encourage stakeholders to let you know of their input in the form of thoughts and opinions so that all outlooks are considered. All stakeholders including IT staff, subject matters, end-users, etc are represented to maintain group diversity. Use diagrams and flowcharts to help stakeholders understand data model and give feedback in an efficient manner. Regularly schedule meetings to discuss progress, review blockers or concerns and give an update to all stakeholders.

5. Document and communicate the data model

Documenting business requirements play a vital role when a project is initiated. In the first step, when requirements are gathered and analyzed, it is important to map them in official documents. Similarly, documenting a data model is important when implementing a collaborative approach because it provides coherent guidelines to the teammates working on a project.

Avoid using technical jargon and acronyms that all stakeholders are not familiar with. Instead, use clear and concise language to define data model and its components. Use diagrams and flowcharts with a standardized notation to explain data model of how it relates to business processes to the stakeholders.

Official documents of data models bridge the communication gap between application developers and stakeholders and bring everyone on a coherent approach of what has been implemented along with all data entities, attributes, relationships, and the rules defined on a logical layer of the data model. Overall, documenting and communicating the data model is an essential aspect of data modeling and helps to ensure its effectiveness and long-term viability.

Data Modeling Tools

A wide range of data modeling tools is being used for data modeling, out of which six are mentioned below:

A popular tool utilized by developers to create custom applications through its API which lets them create custom data modeling tools that can be integrated with ERwin to provide additional functionality for users. This allows the users to customize the tool as per their needs.

2. SAP PowerDesigner:

SAP PowerDesigner tool meant to be customized and used per the user’s specific needs. It has the option to use script in VBScript, JScript and PerlScript to automate tasks, apply validation rules and perform complex calculations. Adding macros to automate repetitive tasks can be done in a snap. Add-ins can be custom-developed using .NET or Java and interacted via API. Templates of data models define entities, attributes, relationships and other key elements. With the model extensions, a user can create custom extensions to store specific domain concepts and customize the tool as per their needs.

3. Oracle SQL Developer Data Modeler:

Oracle SQL Data Modeler is a powerful data models design and management tool that allows the user to create and alter data structures like ER diagrams, data types and constraints so the users may utilize it as needed. Custom plug-ins can be developed using Java to support custom reports, implement specific data modeling conventions, etc, and can be shared across teams for easier collaboration and to maintain a consistent data model.

4. Toad Data Modeler:

This tool supports relational and NoSQL data modeling, including entity relationship diagramming, reverse engineering, and database schema generation. It also supports integration with other data management tools like Toad for Oracle. According to db-engine , Oracle is the most used database management system.

Microsoft Visio is a general-purpose diagramming tool that can use for data modeling. It includes templates for entity relationship diagrams, data flow diagrams, and other types commonly used in data modeling.

6. MySQL Workbench:

MySQL Workbench is an open-source tool explicitly designed to allow users to create and interact with MySQL databases by adding new features and functionalities like Entity-Relationship diagrams, forward and reverse engineering, and database schema generation.

Many other data modeling tools are available, and the choice of tool depends on the project’s specific requirements and the user’s preferences.

Benefits of Data Modeling

Data modeling has several benefits, including that data modeling can help ensure that the database is designed to quickly accommodate future growth and changes in business requirements. Data modeling assists in identifying data redundancies, errors, and irregularities for better insights.

It equips data scientists with an in-depth understanding of data structure, attributes of data, relationships, and constraints of the data. Data modeling also helps in data storage optimization, which plays a significant role in minimizing data storage costs.

Related: Best Web Scraping Tools For Data Gathering In 2023

Final Remarks

Finally, we shed light on the fact that data modeling is the stepping stone of the data management process to achieve business objectives and other essential utilization. We may comprehend the data structure, relationships, and limitations by building a data model.

By making it easier to ensure everyone working on the project is familiar with the data. It is the fundamental phase of the data management process to achieve crucial business objectives and other vital usages that assist in decision-making driven by data analysis.

You can avoid uncertainties and inaccuracies. Data continuity, reliability, and validity are improved by addressing issues. Provides a common language and a framework or schema for better data management practices.

The examples and discussion of this writing provided insight into how data modeling processes raw data to discover patterns, trends, and relationships in data. Also, it provides improved data storage efficiency to cancel out useless data.

Streamlined data retrieval with organized storage. By adopting best practices and leveraging the right tools and techniques, data professionals can help organizations unlock their data’s full potential, driving business growth and innovation.

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What is Data Modeling? Types, Process, and Tools

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• Published: 29 Dec, 2023
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Whatever domain you operate in, data is the blood that keeps the heart of your business pumping. If you don’t have enough information or if it’s there but you don’t know how to make sense of it, you will be far behind your rivals.

Mastering data modeling ensures you have reliable data and the expertise to apply it strategically, keeping you ahead in the competitive landscape.

This article explains data modeling, exploring its types, components, techniques, tools, and practical steps for effective design.

What is data modeling?

Data modeling is the process of discovering, analyzing, and representing data requirements for business operations and software applications. It starts with identifying and scoping data needs, followed by visualizing and precisely communicating these needs through a data model . This model serves as a blueprint, detailing the connections and structures of the organization's data.

A simple data model in the form of an Entity-Relationship Diagram (ERD) for the online bookstore

For example, the diagram above represents a simplified data model for an online bookstore, featuring three entities "Customer," "Order," and  "Book,"  each with their listed attributes and relationships.

Benefits of data modeling

Looking at the beneficial part of data modeling, it

• provides a shared vocabulary for discussing data across the organization;
• captures and documents essential information about the organization's data and systems;
• serves as a primary communication tool, especially useful during projects  involving business process design, software development, and database structuring; and
• offers a foundational starting point for system customization, integration, or replacement.

This process streamlines data management  by making it more accessible and understandable, ensuring its integrity and efficient utilization within databases and systems. Additionally, it guides the development and optimization of data architecture and database design, supporting the effective use and flow of data in various applications.

Speaking of data architecture, many people think that data modeling is similar to data architecture. And that is not so.

Data modeling vs data architecture

While data modeling and data architecture are both crucial in managing an organization's data, they serve different purposes and operate at different levels.

Data modeling vs. data architecture in a nutshell

Data modeling f ocu s. Data modeling is about creating detailed diagrams or models of data. It deals with identifying what data is needed, where it comes from, how it moves, and how it should be structured.

Data modeling goal. The primary aim is to align the business's core rules with data definitions, optimizing how data is stored and used for business activities.

People involved in data modeling.  In most cases, these are data modelers (we’ll explain what they are further). However, other technical professionals like software engineers, data architects , and sometimes data scientists can participate in the data modeling. They may use AI tools to assist in this process.

Data architecture focus. Data architecture takes a broader view: It encompasses not just the data itself but also how it aligns with the business's overall strategy. It involves planning and overseeing the entire data landscape of an organization.

Data architecture goal.  The aim is to ensure data quality, manage data governance , and align data management with business objectives.

People involved in data architecture.  Data architecture involves many participants, including IT personnel, data architects, data engineers , nontechnical industry experts, executives, data consumers, and producers.

Data modeling generally  focuses on the detailed design and structure of specific data sets. In contrast, data architecture looks at the bigger picture, organizing and governing the entire data ecosystem of an organization.

D ata model ing co ncepts

Different data models may use various conventions to represent data, but they fundamentally consist of the same basic building blocks: entities, relationships, attributes, and domains. Let’s take a look at each of them.

Data model components

Entities.  An entity, typically depicted as a rectangle in data models, represents a category or object an organization collects information about. Entities are the “nouns” of a data model, answering fundamental questions like who, what,  when, where, why, or how.  For example, a “who” may be a person or organization of interest, a “what” — a product or service, and a “when” — some time interval like a date of purchase, etc.

In a data model for a hotel property management system , “Customer” is an entity represented as a rectangle labeled “Customer” containing information about hotel guests.

Relationships. Graphically represented as lines, relationships illustrate the associations between entities. They can show high-level interactions between conceptual entities, detailed interactions between logical entities, or constraints between physical entities. We’ll explain conceptual, logical, and physical  data modeling types in the next section.

For example, a line connecting the “Customer” and “Room” entities indicates a relationship, such as which customer booked which room.

Attributes. These are properties or characteristics of an entity, usually depicted as a list inside the entity's rectangle. They describe, identify, or measure aspects of the entity.

Returning to our hotel example, attributes like CustomerID, Name, and Contact Information will be listed inside the “Customer” rectangle, providing specific details about each customer.

Identifiers/Primary keys.   Identifiers or primary keys,  often underlined in the entity, are unique attributes that distinctly identify each instance of an entity. The CustomerID within the “Customer” entity is an identifier, uniquely distinguishing each customer.

Foreign key s.  The goal of a primary key is to create connections between entities. For instance, a foreign key in the “Booking” entity might reference the CustomerID from the “Customer” entity, linking a booking to a specific customer.

Domains.  These components define the set of possible values for an attribute, providing a means of standardizing characteristics.

The domain for the “Room Type” attribute in the “Room” entity might include specific room categories like “Single,” “Double,” and “Deluxe Suite.”

These components collectively form the structure of a data model, making it a vital tool for organizing and understanding a company's data, such as in a hotel's customer management system.

Data modeling types based on their abstraction levels

In the data modeling process, a data model passes through three phases, evolving in complexity and detail : conceptual, logical, and physical. At each stage, it needs input from both business users and data management professionals.

Different features of conceptual, logical, and physical data models

Conceptual data model

This high-level, simplified representation defines key entities and their relationships according to the business requirements. It's abstract and not tied to technical specifics, aiming at understanding the “what” of the business data, e.g., “What are the key things the business deals with, and how are they related?”

At the conceptual data modeling phase, entity-relationship (ER) diagrams are often utilized. These diagrams help you identify the main entities (like customers, products, etc.) and illustrate how they interact. ( We’ll explain ER and other schemas along with their use cases later.)

For example, in a hotel's conceptual data model, you might have entities like "Guest," "Room," and "Reservation," showing fundamental relationships such as guests making room reservations.

Logical data model

This model is more detailed than the conceptual one.  It further refines  specific data structures, including entities,  their attributes, and relationships, while still not addressing physical storage details.

As far as our hotel example goes, the logical model would detail attributes for "Guest" (like name, contact information), "Room" (like room type, rate), and "Reservation" (like reservation dates, room assigned), providing a more comprehensive understanding of how data elements interrelate within the hotel's operations.

Physical data model

As the most detailed model, the physical data model outlines how to store and access data in a particular database. It includes data types, sizes, constraints, and table relationships. In the hotel context, this model would lay out the database schema, describing how guest information, room details, and reservation data are stored, with specific table structures. Additionally, it includes optimization strategies for database performance.

It’s worth noting that each model serves a specific purpose within the phases of the data modeling process: The conceptual model establishes the overall framework, the logical model details specific data structures and relationships, and the physical model translates these into an actual database schema. You can’t just go with one and ignore the others.

Data modeling  techniques: examples and applications

There are a few core data modeling techniques or schemas: relational, entity-relationship, hierarchical, network, dimensional, object-oriented database, and object-relational models. They provide frameworks for organizing, storing, and managing data, each suited to different business needs and data peculiarities.

You can determine the most appropriate technique at the logical data modeling stage once the entities and relationships are clearly defined. Different factors influence the decision, such as the characteristics of the business operations, the complexity of queries and reports needed,  the nature of the data ( structured, unstructured, or semi-structured ), performance considerations,  the intended database management system, etc.

Relational model

In this model, you structure your data in tables, each representing a different entity. Every row in a table is a record with a unique identifier (key), and each column keeps an entity's attribute. Foreign keys establish relationships between tables by referencing  primary keys in other tables. This simple and flexible model allows easy data retrieval and manipulation through SQL queries.

Application: Relational models are fundamental for any application that relies on a relational database. T hey are essential for accurately representing and understanding data relationships in a wide range of solutions, from enterprise resource planning systems to customer relationship management software.

Entity-relationship model

As we said earlier, t his schema is foundational in conceptual data modeling. But it can be used at later phases as well, especially if you work with relational database systems. It employs entities (representing data objects) and relationships (connections between entities) to map out data structures.

Entities are defined by their attributes and are depicted as rectangles,  a visualization method also prevalent in other data modeling approaches.  Relationships are shown as lines connecting these rectangles.  This model is handy for visualizing data structures and their interconnections, making it a valuable tool during the database design phase.

Application: Entity-relationship models are o ften used in content management systems, organizational charts, and file systems, where data needs to be retrieved  in a top-down approach.   This involves starting from a general overview of the system and progressively refining it into more detailed data structures.

Hierarchical model

Resembling a tree structure, this model organizes data in a hierarchy where each record has a single parent and possibly many children. It's characterized by transparent parent-child relationships, with data retrieved through a top-down tree traversal.  While its rigidity can be a limitation for more complex data interactions, it still finds use in specific applications.

Application:  Hierarchical models are used in Extensible Markup Language (XML) systems and geographic information systems (GISs), where their structured approach is beneficial.

Network model

This model expands on the hierarchical model by allowing multiple parent-child relationships. Data is structured as a graph, with entities (or  records) being the nodes a nd relationships between them — edges.  This allows for more complex data relationships but can lead to a more complicated database structure.

Application: Network models are essential in business applications, social networks, customer management, educational and other systems, where it's possible to easily segment data into different attributes.

Dimensional model

This model organizes data into fact tables and dimension tables. Fact tables contain quantitative data (like sales figures), while dimension tables hold descriptive attributes related to the facts (like time, location, and product characteristics). The dimensional model supports data analysis and reporting due to its intuitive organization, simplifying complex queries and improving query performance.

Application: The dimensional model is particularly important for data warehousing and business intelligence  applications, where fast retrieval of aggregated data for reporting and analysis is crucial. This model also creates a foundation for OLAP systems that support complex queries and allow users to view different aspects of data without disrupting its integrity .

Object-oriented model

Inspired by object-oriented programming, this model treats data as objects. Each object represents a real-life entity (or problem) encapsulated in a single structure that combines attributes (characteristics or properties of the object) and behavior (real-life actions that can be performed on or by the object.) Objects that share similar behavior and attributes form classes. Such an organization allows more intuitive representations of real-world scenarios in the data model.

Application:  The object-oriented model is widely used in complex software systems such as computer-aided design (CAD) and 3D modeling software.

Object-relational model

Combining elements of both relational and object-oriented models, this hybrid approach allows for storing complex data types (like objects) within a relational table structure. It offers the versatility of object-oriented models with the simplicity and robustness of relational databases.

Application: The model fits large-scale applications that require advanced data management capabilities, like enterprise resource planning (ERP) systems and solutions involving complex data processing.

How data modeling works: Key steps to a data modeling process

There are different approaches — and consequently the amount of steps — to a data modeling process. According to the Data Management Body of Knowledge (DMBoK) , the key stages include planning for data modeling, building a data model, validating and testing the model, and maintaining the model. Let’s take a closer look at each step.

Data modeling process steps

Planning and requirement analysis

This phase is about understanding the business context and preparing for the data modeling process. It starts with analyzing the business' data needs: what information you capture, how your company uses it, an d the particular data requirements  for data quality, format, and security. This analysis is crucial in pinpointing the essential types of data your business handles and determining how these data types should be structured and interrelated .

You can learn more about this in our article on how to write a business requirements document .

Another vital part of this phase is e stablishing data standards: You decide on conventions for data formatting, quality, and consistency to ensure that data models across the organization follow a unified approach.

At this point, you also plan for data storage. Here, the focus is on selecting the right kind of storage solutions (like databases and data warehouses ) that align with the data's volume, security, and access needs.

Building a data model

You can move to the building part by relying on prior analysis and existing models. You may start by examining current data structures, databases, and published standards, integrating any specific data requirements identified.

Building a data model requires you to go through the three st ages  we’ve discussed above.

• Conceptual data model stage:  It establishes the overarching framework of data elements and their interconnections, tailored to business perspectives.
• Logical data model stage:  It enhances the conceptual model with detailed data specifications, refining entity relationships and data type definitions. It’s also the point in data modeling, where you choose the modeling technique.
• Physical data model stage:  It finalizes the model for implementation, focusing on database-specific optimizations and physical data storage solutions.

Remember that the data modeling process is iterative: draft your model, consult with business professionals and analysts to clarify terms and rules, and refine the model based on their feedback, asking further questions as needed.

Validation and testing

Once each phase of the building process is completed, the resulting model — be it conceptual, logical, or physical — needs to be validated and tested. The model undergoes checks to see if it aligns with the business requirements identified in the first phase. It also ensures the model maintains data integrity and performs efficiently under expected operational conditions.

Maintaining data models

Data models are not static; they evolve. This final phase is about keeping the data model up to date with changes in business processes, regulations, and technological advancements. It ensures that the model accurately represents the business and its data needs.

Data modeling to ols

Many data modeling tools range from basic versions, offering simple drawing capabilities for creating entities and relationships, to more  advanced options. More modern tools can seamlessly transition from conceptual to logical and physical models, even generating the necessary database structures. Below, you will find some popular data modeling software options.  Please note that we do not promote any of the tools in the list. They are just for showcase purposes.

Data modeling tools compared

ER/Studio  is a data modeling tool designed for complex environments. It supports various database systems, offers detailed modeling capabilities, and is ideal for large-scale data architecture projects. The tool provides extensive features for documenting and sharing data models, with pricing typically geared towards enterprise clients.

DbSchema Pro

DbSchema Pro  is known for its interactive diagrams and effective schema synchronization capabilities. It allows users to visually design, manage, and document database schemas. This tool is suitable for relational and NoSQL databases, offering a free trial and a one-time purchase option, making it accessible to a broad range of users.

Archi  is a user-friendly data modeling tool for beginners and those new to architectural modeling. It's an open-source tool, making it free to use, and it offers basic functionalities for creating and analyzing enterprise architectures. Its simplicity and no cost make it a popular choice for educational purposes and small projects.

SQL Database Modeler

SQL Database Modeler  is a cloud-based solution for designing, documenting, and sharing database schemas. The tool is convenient for remote and collaborative work, with subscription-based pricing that makes it adaptable for both small and large-scale projects.

Oracle SQL Developer Data Modeler

Oracle SQL Developer Data Modeler provides extensive features for designing, analyzing, and optimizing Oracle databases. The tool is known for its depth of functionality and integration with Oracle products, making it a preferred choice for Oracle database users. More good news? It's available free of charge, adding value for Oracle clients.

What is a data modeler? Role a nd responsibilities

Data modelers  are professionals who specialize in creating data models that define the organization, integration, and management of data. In larger companies, this is a dedicated role. Still, in smaller companies, database administrators, data or business analysts, IT managers, system architects, or software developers can handle these tasks.

Core data modeler responsibilities include the following tasks.

• Creating and updating conceptual, logical, and physical data models to meet organizational needs.
• Working with various teams to understand and define data requirements for business processes.
• Ensuring data models comply with data governance  policies and standards.
• Designing efficient data models for enhanced database functionality.
• Maintaining detailed documentation of data architecture and metadata.
• Modifying data models in response to evolving business needs and technological advanc ements.

With these responsibilities, data modelers play a pivotal role in shaping the data landscape of an organization, ensuring that data structures are efficient, compliant, and adaptable to the changing needs of the business and technology.

How to design a data model: Tips and best practices

Designing an effective data model requires a careful approach. Here are some tips and best practices to help you create data models that work.

Understand business requirements. Always b egin by thoroughly understanding the business needs and how data will support them.

Identify important entities and relationships. Determine the main entities (e.g., customers, products) and how they relate.

Prioritize data integrity. Ensure accuracy and consistency in data through rules and constraints.

Use normalization wisely.  Apply normalization to reduce redundancy but balance it with performance needs.

Plan for scalability. Design with future growth in mind to accommodate increasing data volumes.

Focus on user needs. Consider the end-users and how they will interact with the data.

Incorporate flexibility.  Allow room for changes as business needs evolve.

Validate with stakeholders.  Regularly review the model with business stakeholders for alignment.

Ensure clear documentation. Document the data model thoroughly for clarity and future reference.

Test and iterate.  Regularly test the model and make iterative improvements.

These tips provide a framework for designing a data model that is robust, scalable, and aligned with business objectives.

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Data modeling is the process of creating a visual representation of databases and information systems to help users understand the data they contain, the relationships between them, and how they can be organized. Effective data models help navigate data’s shared connections and make it easier to design optimized databases.

A key facet of data management, data models contribute to a business’s data governance programs and data quality processes by maintaining consistency in naming conventions, semantics, and security, and by helping users to identify and fix errors. Here’s what you need to know about data models.

Table of Contents

How Data Modeling Works

Data modeling is the process of visualizing the relationships among—and the locations of—various data points. A data modeler is usually a database administrator or data architect that works with the data. Essentially, the process involves ** steps:

• Gathering requirements and details on business processes to develop a framework that aligns with business goals
• Identifying entities in the dataset and their key properties, and creating a draft model that illustrates their relationships
• Identifying related attributes and mapping them to the entities, which allows the model to reflect the business use of the data
• Finalizing the data model and validating its accuracy with test queries

Depending on the type of data model—conceptual, logical, or physical—the diagram you create can include varying degrees of simplicity, detail, and abstraction. Data models are not static documents—they’re meant to be updated and revised as data assets and business needs change.

What Are The Features Of Data Modeling?

All data modeling approaches share some key functionalities and capabilities—here’s what you should expect.

Data Entities And Their Attributes

Entities are abstractions of real pieces of data. For example, in a customer relationship management (CRM) system, “customer” is an entity that represents the individuals in the database. Attributes are the properties that characterize those entities—for example, date of birth or acquisition source. Attributes can be used to find similarities and make connections across entities. These connections are known as relationships.

Unified Modeling Language (UML)

UML is the building blocks and best practices for data modeling. It’s a standard modeling language that helps data professionals visualize and construct appropriate model structures for their data needs. UML diagrams make it easier for technical and non-technical users to understand the structure of a model.

Normalization Through Unique Keys

When building out relationships within a large dataset, several units of data need to be repeated to illustrate all necessary relationships. Normalization is the technique that eliminates repetition by assigning unique keys or numerical values to different groups of data entities. These unique keys are also known as primary keys. An example of this in a CRM is a customer ID number, which can be used to link an individual record across multiple tables or databases without having to create duplicate records in each instance.

Read Data Modeling vs. Data Architecture to learn the key differences between these two powerful components of enterprise data use.

5 Benefits Of Data Modeling

As part of a larger data management effort, data modeling offers several distinct benefits to enterprises.

Data Quality

Data modeling can help you clean, organize, and structure data before it is analyzed. This makes it possible to identify duplicates in data, discover missing data, and set up monitoring to ensure its long-term quality. The end result is a database less prone to errors.

Despite being an added step that may need to be repeated multiple times throughout a project’s development process, modeling a database before work begins sets up the scope and expectations for the project. This in turn reduces development and maintenance costs by ensuring you don’t end up spending more time and resources on a step than is necessary and justified by the data itself.

Collaboration

The early stages of a project’s development can be too abstract for individuals with little to no technical experience to fully understand. Data modeling creates a visual representation of how data will flow through a system, which helps non-technical stakeholders better grasp what is happening with the data and provide feedback. The visual nature of data modeling—especially conceptual data modeling— allows for more collaboration and discussions among shareholders and nontechnical departments such as marketing and customer experience.

As the number of privacy and security regulations that impact data continue to grow, it is essential to include privacy and security requirements from the earliest stages of a system’s development. Data modeling facilitates a deep understanding of the data structure, which enables developers to identify and include the necessary components for compliance into the database’s infrastructure. This ensures that data privacy and security compliance will be continually monitored as part of your data governance activities.

Documentation

Documentation is needed to encapsulate the development process of a system and helps with solving any future problems or inconsistencies that may arise as well as with training future employees. By building an in-depth data model early in the development process, you’ll be able to include that into the system’s documentation to allow for a deeper understanding of how it works.

Challenges of Data Modeling

Data modeling is a complex process that can present challenges. Here are some of the most common.

Limited Flexibility

Most types of data models are fairly rigid, meaning that if you want to make any changes to the data structure you usually need to restructure the entire database. Therefore, they are difficult to adapt when requirements change.

Data models can be complex, especially as they become more detailed. This complexity can make it challenging for non-technical stakeholders to understand the processes and collaborate on their development.

Time-Consuming

Until recently, data modeling was a manual process. The time and effort required to develop the models was significant, especially for datasets that were large and complex. Today the process is benefiting from automation, which is reducing the burden on data professionals, but the volume of data and the variety of relationships within the data continue to grow.

Unclear Business Requirements

Undefined or unclear business requirements is a process challenge for data modeling. In order to develop effective data models that reflect and align with a business’s strategic goals, you should be working with other divisions to gather concrete business requirements and use those to identify and map the entities and attributes in the model.

Data models that are unmoored from the larger business context and goals negatively impact buy-in from others at your company, leading to models that do not get the attention and feedback needed to remain up-to-date, useful, and relevant.

3 Types Of Data Models 

Data models can be divided into three main types based on the level of abstraction needed between various data points, the format of the data and how the data are stored.

Conceptual Data Model

Conceptual data models, also referred to as conceptual schemas, are the most simple of the three types and represent data at a high level of abstraction. This approach doesn’t go in-depth into the relationship between the various data points, simply offering a generalized layout of the most prominent data structures.

Thanks to their simple nature, conceptual data models are often used in the first stages of a project. They also don’t require a high level of expertise and knowledge in databases to understand, making them the perfect option to use when working with non-technical stakeholders.

Logical Data Model

Logical data models, also referred to as logical schemas, are an expansion on the basic framework laid out in conceptual models but include more relational factors. This model features some basic annotations regarding the overall properties or data attributes, but still lacks an in-depth focus on actual units of data.

Physical Data Model

Physical data models, also referred to as physical schemas, are a visual representation of data design as it’s meant to be implemented. They’re also the most detailed of all data modeling types and are usually reserved for the final steps before database creation.

In addition to the three primary types of data modeling, you can choose between several different design and infrastructure approaches for the visualization process. Choosing the infrastructure determines how the data is visualized and portrayed in the final mapping. These approaches include:

• Hierarchical data modeling , where the data is organized in parent-child relationships.
• Relational data modeling , which maps the relationships between data that exist in different tables.
• Entity-relationship data modeling, which visually maps the relationship between data points.
• Object-oriented data modeling, which groups entities into class hierarchies.

Read our comprehensive guide to the differences between logical and physical data models to better understand the strengths, weaknesses, and applications of each.

Top 4 Data Modeling Tools

Data modeling tools bring together the ability to discover and document datasets with visual design functionality to create the models. Many of the tools on the market today extend that core functionality to support a wide variety of data architecture and governance activities as well.

While there are numerous tools available to help you with data modeling, here are four standouts in the market:

Apache Spark

This open-source system focuses on processing large sets of data. As a unified analytics engine, Spark can be used for nearly anything in the data science workflow. The tool also integrates with a large variety of data science, business intelligence and data storage platforms.

This open-source modeling toolkit allows the creation of models in the ArchiMate modeling language aligned with the TOGAF Standard for Enterprise Architecture used by the world’s leading organizations to improve business efficiency.

erwin Data Modeler by Quest

This longtime fixture of the data modeling world helps users find, visualize, design, deploy, and standardize enterprise data assets. The tool supports the creation of logical, physical and conceptual data models.

This cloud-based collaborative web tool for making database diagrams lets users import the database structure from their database management system to create a variety of database designs.

Bottom Line: How Data Modeling Adds Value

Data modeling is an essential tool to help businesses better understand and work with the data in their databases or information management systems. It’s also a contributor to their data quality and data governance efforts. An awareness of the different types and approaches to data modeling can contribute to a business’s data governance programs and data quality processes while helping ensure consistency, accuracy, and quality of data.

Read Data Management: Types and Challenges to gain a better understanding of the many components that go into an overarching enterprise data strategy.

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Chapter 5 Data Modelling

Adrienne Watt

Data modelling is the first step in the process of database design. This step is sometimes considered to be a high-level and abstract design phase, also referred to as conceptual design . The aim of this phase is to describe:

• The data contained in the database (e.g., entities: students, lecturers, courses, subjects)
• The relationships between data items (e.g., students are supervised by lecturers; lecturers teach courses)
• The constraints on data (e.g., student number has exactly eight digits; a subject has four or six units of credit only)

In the second step , the data items, the relationships and the constraints are all expressed using the concepts provided by the high-level data model. Because these concmepts do not include the implementation details, the result of the data modelling process is a (semi) formal representation of the database structure. This result is quite easy to understand so it is used as reference to make sure that all the user’s requirements are met.

The third step is database design . During this step, we might have two sub-steps: one called database logical design , which defines a database in a data model of a specific DBMS, and another called database physical design , which defines the internal database storage structure, file organization or indexing techniques. These two sub-steps are database implementation and operations/user interfaces building steps.

In the database design phases, data are represented using a certain data model. The data model is a collection of concepts or notations for describing data, data relationships, data semantics and data constraints. Most data models also include a set of basic operations for manipulating data in the database.

Degrees of Data Abstraction

In this section we will look at the database design process in terms of specificity. Just as any design starts at a high level and proceeds to an ever-increasing level of detail, so does database design. For example, when building a home, you start with how many bedrooms and bathrooms the home will have, whether it will be on one level or multiple levels, etc. The next step is to get an architect to design the home from a more structured perspective. This level gets more detailed with respect to actual room sizes, how the home will be wired, where the plumbing fixtures will be placed, etc. The last step is to hire a contractor to build the home. That’s looking at the design from a high level of abstraction to an increasing level of detail.

The database design is very much like that. It starts with users identifying the business rules; then the database designers and analysts create the database design; and then the database administrator implements the design using a DBMS.

The following subsections summarize the models in order of decreasing level of abstraction.

External models

• Represent the user’s view of the database
• Contain multiple different external views
• Are closely related to the real world as perceived by each user

Conceptual models

• Provide flexible data-structuring capabilities
• Present a “community view”: the logical structure of the entire database
• Contain data stored in the database
• Constraints
• Semantic information (e.g., business rules)
• Security and integrity information
• Consider a database as a collection of entities (objects) of various kinds
• Are the basis for identification and high-level description of main data objects; they avoid details
• Are database independent regardless of the database you will be using

Internal models

The three best-known models of this kind are the relational data model, the network data model and the hierarchical data model. These internal models:

• Consider a database as a collection of fixed-size records
• Are closer to the physical level or file structure
• Are a representation of the database as seen by the DBMS.
• Require the designer to match the conceptual model’s characteristics and constraints to those of the selected implementation model
• Involve mapping the entities in the conceptual model to the tables in the relational model

Physical models

• Are the physical representation of the database
• Have the lowest level of abstractions
• Run-time performance
• Storage utilization and compression
• File organization and access methods
• Data encryption
• Are the physical level – managed by the operating system  (OS)
• Provide concepts that describe the details of how data are stored in the computer’s memory

Data Abstraction Layer

In a pictorial view, you can see how the different models work together. Let’s look at this from the highest level, the external model.

The external model is the end user’s view of the data. Typically a database is an enterprise system that serves the needs of multiple departments. However, one department is not interested in seeing other departments’ data (e.g., the human resources (HR) department does not care to view the sales department’s data). Therefore, one user view will differ from another.

The external model requires that the designer subdivide a set of requirements and constraints into functional modules that can be examined within the framework of their external models (e.g., human resources versus sales).

As a data designer, you need to understand all the data so that you can build an enterprise-wide database. Based on the needs of various departments, the conceptual model is the first model created.

At this stage, the conceptual model is independent of both software and hardware. It does not depend on the DBMS software used to implement the model. It does not depend on the hardware used in the implementation of the model. Changes in either hardware or DBMS software have no effect on the database design at the conceptual level.

Once a DBMS is selected, you can then implement it. This is the internal model. Here you create all the tables, constraints, keys, rules, etc.  This is often referred to as the logical design .

The physical model is simply the way the data is stored on disk. Each database vendor has its own way of storing the data.

A schema is an overall description of a database, and it is usually represented by the  entity relationship diagram (ERD) . There are many subschemas that represent external models and thus display external views of the data. Below is a list of items to consider during the design process of a database.

• External schemas: there are multiple
• Multiple subschemas: these display multiple external views of the data
• Conceptual schema: there is only one. This schema includes data items, relationships and constraints, all represented in an ERD.
• Physical schema: there is only one

Logical and Physical Data Independence

Data independence refers to the immunity of user applications to changes made in the definition and organization of data. Data abstractions expose only those items that are important or pertinent to the user. Complexity is hidden from the database user.

Data independence and operation independence together form the feature of data abstraction. There are two types of data independence: logical and physical.

Logical data independence

A logical schema is a conceptual design of the database done on paper or a whiteboard, much like architectural drawings for a house. The ability to change the logical schema, without changing the external schema  or user view,   is called logical data independence . For example, the addition or removal of new entities, attributes or relationships to this conceptual schema should be possible without having to change existing external schemas or rewrite existing application programs.   

In other words, changes to the logical schema (e.g., alterations to the structure of the database like adding a column or other tables) should not affect the function of the application (external views).

Physical data independence

Physical data independence refers to the immunity of the internal model to changes in the physical model. The logical schema stays unchanged even though changes are made to file organization or storage structures, storage devices or indexing strategy.

Physical data independence deals with hiding the details of the storage structure from user applications. The applications should not be involved with these issues, since there is no difference in the operation carried out against the data.

conceptual schema : another term for logical schema

data independence : the immunity of user applications to changes made in the definition and organization of data

data model :a collection of concepts or notations for describing data, data relationships, data semantics and data constraints

data modelling : the first step in the process of database design

database logical design :  defines a database in a data model of a specific database management system

database physical design :  defines the internal database storage structure, file organization or indexing techniques

entity relationship diagram (ERD) : a data model describing the database showing tables, attributes and relationships

external model :  represents the user’s view of the database

external schema : user view

internal model:   a representation of the database as seen by the DBMS

logical data independence : the ability to change the logical schema without changing the external schema

logical design : where you create all the tables, constraints, keys, rules, etc.

logical schema : a conceptual design of the database done on paper or a whiteboard, much like architectural drawings for a house

operating system (OS) : manages the physical level of the physical model

physical data independence : the immunity of the internal model to changes in the physical model

physical model :  the physical representation of the database

schema : an overall description of a database

• Describe the purpose of a conceptual design.
• How is a conceptual design different from a logical design?
• What is an external model?
• What is a conceptual model?
• What is an internal model?
• What is a physical model?
• Which model does the database administrator work with?
• Which model does the end user work with?
• What is logical data independence?
• What is physical data independence?

Also see Appendix A: University Registration Data Model Example

Attribution

This chapter of  Database Design  is a derivative copy of  Database System Concepts  by Nguyen Kim Anh licensed under  Creative Commons Attribution License 3.0 license

The following material was written by Adrienne Watt:

• Some or all of the introduction, degrees of data abstraction, data abstraction layer

Database Design - 2nd Edition Copyright © 2014 by Adrienne Watt is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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Data modeling is the process of creating a visual representation of either a whole information system or parts of it to communicate connections between data points and structures.

The goal of data modeling to illustrate the types of data used and stored within the system, the relationships among these data types, the ways the data can be grouped and organized and its formats and attributes.

Data models are built around business needs. Rules and requirements are defined upfront through feedback from business stakeholders so they can be incorporated into the design of a new system or adapted in the iteration of an existing one.

Data can be modeled at various levels of abstraction. The process begins by collecting information about business requirements from stakeholders and end users. These business rules are then translated into data structures to formulate a concrete database design. A data model can be compared to a roadmap, an architect’s blueprint or any formal diagram that facilitates a deeper understanding of what is being designed.

Data modeling employs standardized schemas and formal techniques. This provides a common, consistent, and predictable way of defining and managing data resources across an organization, or even beyond.

Ideally, data models are living documents that evolve along with changing business needs. They play an important role in supporting business processes and planning IT architecture and strategy. Data models can be shared with vendors, partners, and/or industry peers.

Learn the building blocks and best practices to help your teams accelerate responsible AI.

Read the guide for data leaders

Like any design process, database and information system design begins at a high level of abstraction and becomes increasingly more concrete and specific. Data models can generally be divided into three categories, which vary according to their degree of abstraction. The process will start with a conceptual model, progress to a logical model and conclude with a physical model. Each type of data model is discussed in more detail in subsequent sections:

They are also referred to as domain models and offer a big-picture view of what the system will contain, how it will be organized, and which business rules are involved. Conceptual models are usually created as part of the process of gathering initial project requirements. Typically, they include entity classes (defining the types of things that are important for the business to represent in the data model), their characteristics and constraints, the relationships between them and relevant security and data integrity requirements. Any notation is typically simple.

They are less abstract and provide greater detail about the concepts and relationships in the domain under consideration. One of several formal data modeling notation systems is followed. These indicate data attributes, such as data types and their corresponding lengths, and show the relationships among entities. Logical data models don’t specify any technical system requirements. This stage is frequently omitted in agile or  DevOps  practices. Logical data models can be useful in highly procedural implementation environments, or for projects that are data-oriented by nature, such as  data warehouse  design or reporting system development.

They provide a schema for how the data will be physically stored within a database. As such, they’re the least abstract of all. They offer a finalized design that can be implemented as a  relational database , including associative tables that illustrate the relationships among entities as well as the primary keys and foreign keys that will be used to maintain those relationships. Physical data models can include database management system (DBMS)-specific properties, including performance tuning.

As a discipline, data modeling invites stakeholders to evaluate data processing and storage in painstaking detail. Data modeling techniques have different conventions that dictate which symbols are used to represent the data, how models are laid out, and how business requirements are conveyed. All approaches provide formalized workflows that include a sequence of tasks to be performed in an iterative manner. Those workflows generally look like this:

• Identify the entities.  The process of data modeling begins with the identification of the things, events or concepts that are represented in the data set that is to be modeled. Each entity should be cohesive and logically discrete from all others.
• Identify key properties of each entity.  Each entity type can be differentiated from all others because it has one or more unique properties, called attributes. For instance, an entity called “customer” might possess such attributes as a first name, last name, telephone number and salutation, while an entity called “address” might include a street name and number, a city, state, country and zip code.
• Identify relationships among entities.  The earliest draft of a data model will specify the nature of the relationships each entity has with the others. In the above example, each customer “lives at” an address. If that model were expanded to include an entity called “orders,” each order would be shipped to and billed to an address as well. These relationships are usually documented via unified modeling language (UML).
• Map attributes to entities completely.  This will ensure the model reflects how the business will use the data. Several formal data modeling patterns are in widespread use. Object-oriented developers often apply analysis patterns or design patterns, while stakeholders from other business domains may turn to other patterns.
• Assign keys as needed, and decide on a degree of normalization that balances the need to reduce redundancy with performance requirements.  Normalization is a technique for organizing data models (and the databases they represent) in which numerical identifiers, called keys, are assigned to groups of data to represent relationships between them without repeating the data. For instance, if customers are each assigned a key, that key can be linked to both their address and their order history without having to repeat this information in the table of customer names. Normalization tends to reduce the amount of storage space a database will require, but it can at cost to query performance.
• Finalize and validate the data model.  Data modeling is an iterative process that should be repeated and refined as business needs change.

Data modeling has evolved alongside database management systems, with model types increasing in complexity as businesses' data storage needs have grown. Here are several model types:

• Hierarchical data models  represent one-to-many relationships in a treelike format. In this type of model, each record has a single root or parent which maps to one or more child tables. This model was implemented in the IBM Information Management System (IMS), which was introduced in 1966 and rapidly found widespread use, especially in banking. Though this approach is less efficient than more recently developed database models, it’s still used in Extensible Markup Language (XML) systems and geographic information systems (GISs).
• Relational data models  were initially proposed by IBM researcher E.F. Codd in 1970. They are still implemented today in the many different relational databases commonly used in enterprise computing. Relational data modeling doesn’t require a detailed understanding of the physical properties of the data storage being used. In it, data segments are explicitly joined through the use of tables, reducing database complexity.

Relational databases frequently employ structured query language (SQL) for data management. These databases work well for maintaining data integrity and minimizing redundancy. They’re often used in point-of-sale systems, as well as for other types of transaction processing.

• Entity-relationship (ER) data models  use formal diagrams to represent the relationships between entities in a database. Several ER modeling tools are used by data architects to create visual maps that convey database design objectives.
• Object-oriented data models  gained traction as object-oriented programming and it became popular in the mid-1990s. The “objects” involved are abstractions of real-world entities. Objects are grouped in class hierarchies, and have associated features. Object-oriented databases can incorporate tables, but can also support more complex data relationships. This approach is employed in multimedia and hypertext databases as well as other use cases.
• Dimensional data models  were developed by Ralph Kimball, and they were designed to optimize data retrieval speeds for analytic purposes in a  data warehouse . While relational and ER models emphasize efficient storage, dimensional models increase redundancy in order to make it easier to locate information for reporting and retrieval. This modeling is typically used across  OLAP  systems.

Two popular dimensional data models are the star schema, in which data is organized into facts (measurable items) and dimensions (reference information), where each fact is surrounded by its associated dimensions in a star-like pattern. The other is the snowflake schema, which resembles the star schema but includes additional layers of associated dimensions, making the branching pattern more complex.

Data modeling makes it easier for developers, data architects, business analysts, and other stakeholders to view and understand relationships among the data in a database or data warehouse. In addition, it can:

• Reduce errors in software and database development.
• Increase consistency in documentation and system design across the enterprise.
• Improve application and database performance.
• Ease data mapping throughout the organization.
• Improve communication between developers and business intelligence teams.
• Ease and speed the process of database design at the conceptual, logical and physical levels.

Data modeling tools

Numerous commercial and open source computer-aided software engineering (CASE) solutions are widely used today, including multiple data modeling, diagramming and visualization tools. Here are several examples:

• erwin Data Modeler  is a data modeling tool based on the Integration DEFinition for information modeling (IDEF1X) data modeling language that now supports other notation methodologies, including a dimensional approach.
• Enterprise Architect  is a visual modeling and design tool that supports the modeling of enterprise information systems and architectures as well as software applications and databases. It’s based on object-oriented languages and standards.
• ER/Studio  is database design software that’s compatible with several of today’s most popular database management systems. It supports both relational and dimensional data modeling.
• Free data modeling tools  include open source solutions such as Open ModelSphere.

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What Is Data Modeling?

Data can be messy.

This is often a headache for data analysts . But understanding a dataset’s structure and the relationship between different data points can also help you manipulate it to meet your business intelligence needs.

To figure all this out, data analysts typically use a process known as data modeling. Data modeling allows you to dive deep into data, helping design, implement, and manage complex database systems. Data models also keep data analysts, software designers, engineers, and other stakeholders on the same page, ensuring everyone’s needs are being met.

These benefits sound great, but what exactly does data modeling involve? Why is it important? And what different types of data models exist? 

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In this introductory guide, we’ll answer all your questions, including:

• What is data modeling?
• Why is data modeling important?
• Types of data models
• The data modeling process
• Examples of data modeling
• Data modeling tools (and how to choose one)
• Benefits and challenges of data modeling

Ready to demystify data modeling? Then let’s jump in.

1. What is data modeling?

Data modeling is the process of mapping how data moves from one form or component to another, either within a single database or a data management system.

Data modeling is a fundamental data wrangling and design task that should happen before any database, computer system, app, algorithm, or other data structure is created. By defining the relationship between different data elements and visually representing these, data modeling helps analysts build systems that are fit for purpose.

If this feels a bit abstract, it might help to think of data modeling as a little like designing a new building. Before an engineer constructs a block of apartments, they must first understand the different elements required and how these interact. Where will the windows and doors go? Where should the pipes come in and out of the building? Crucially, how do all these elements relate to one another? Only with these details mapped out can the engineer hope to create a solid structure that does what it is supposed to do.

Similarly, data modeling helps data analysts define everything they need to know about their data, from data formats and data flows to data handling functions. Only once they have all this information can they hope to create a solid structure that meets their needs.

Data modeling doesn’t just serve an upfront purpose, either. Once a database is up and running, the model acts as an all-important reference guide. This allows future data engineers and analysts to understand the principles underlying the database’s original design and construction, how it works, and how data is shared between different systems. This is important because no system or database stays the same. Imagine trying to upgrade a building without a blueprint explaining how it was constructed. It would be a bit of a mess!

While it’s possible to create a database without first carrying out data modeling, it won’t be nearly as effective. Backward engineering a poorly planned system takes much more time and effort than simply investing the necessary resources upfront.

2. Why is data modeling important?

As we explained in section one, the main reason why data modeling is so important is that it informs data structures that are fit for purpose. 

However, data modeling offers numerous additional benefits. Some of these include:

• It provides insights : While data modeling is the foundation of effective data structures, it also provides useful insights before you reach this point. For example, you’ll quickly learn to spot where data are missing or incorrect. By generally improving the understanding of data, data modeling can help high-level decision-making, even before a database or structure is up and running.
• It tackles core data wrangling tasks: Data modeling forces analysts to standardize data, establish hierarchies, and generally make the data more consistent and usable. All these tasks are for key data cleaning . So by modeling your data, you are effectively killing two birds with one stone—creating a structural blueprint and tidying your data.
• It improves communication: Data modeling involves having a clear understanding of how different stakeholders will use data, what kinds of reports they’ll need, and so on. Data modeling inherently encourages clearer communication between different groups, ensuring everyone understands their role and how the data will impact their and others’ work.
• It saves resources: Designing a database upfront (before you invest time and money in building it) reduces unnecessary duplication of tasks. It also ensures the database won’t miss important functionality and it minimizes data storage requirements by identifying and eradicating duplicate data.
• It supports compliance: Every organization has statutory data protection responsibilities. By comparing your model against these, you can ensure compliance with industry data regulations and standards.
• It makes management tasks more efficient: By properly modeling data flows early on, you can quickly identify procedural gaps or inefficiencies, improving all aspects of your data management practices.

As you can see, data modeling is a tool with a great many uses. It’s definitely a string worth adding to your bow.

3. Types of data models

When exploring different data models, you’ll find there are many individual data models designed for specific data modeling tasks. These range from network models to relational models, and more. However, if you’re new to the concept of data modeling, a more helpful distinction at this stage is the different categories of data models. 

Broadly speaking, these categories focus on what’s known as a model’s level of abstraction, or how close to the real world the model is. So, for instance, at a high level of abstraction, a data model will describe the overall structure of a database or information, without focusing on the detail. Meanwhile, at a low level of abstraction, a data model will provide a granular schematic of how a system should be implemented, on a task-by-task basis.

When categorizing data models in this way, there are three main options to choose from, each building on the one that comes before it. 

Let’s take a look.

Conceptual data modeling

At the highest level is the conceptual data model. A simplified, loosely defined representation of a data system, a conceptual model’s purpose is to define a structure’s main entities (or table types) and the relationships between them. 

The conceptual model is the first step in any data modeling project. It helps designers grasp the organization’s high-level business needs and encourages discussion between data analysts, software engineers, and other teams, departments, and stakeholders, about how the database should be designed. 

Although every model is different, it’s safe to say that the conceptual model isn’t usually tied to the final implementation of a database. Think of it as the first iteration where you’re ironing out the kinks before diving into the details. That said, it’s still important to get it right, as it is the foundation upon which you will build your more detailed logical and physical models.

Logical data modeling

Building on the conceptual data model, a logical data model is a more thorough representation of a system. It is the first model that describes the data’s attributes (or the ​​characteristics that define all items) and keys (sets of attributes that help uniquely identify rows and their relationship to the tables within the model).

A logical data model is useful when you’re trying to understand the detailed requirements of a system or network of systems before committing to a full system implementation.

Physical data modeling

Lastly, the physical data model builds on the logical data model. The physical model is a detailed system representation, defining the specific data elements such as table and column names, accounts, indexes, and different data types.

Most commonly created by database administrators and software developers, the physical model outlines the parameters required for your database or database management system (DBMS), including software and hardware. As your end goal, the physical data model is tied to specific database implementations and database management systems.

4. The data modeling process

Okay, now we understand the different types of data models, what does the process involve?

Data modeling almost always follows a sequential process, starting with the conceptual model and moving down through the levels of abstraction to the logical and physical models (which we described in the previous section). While data modeling tasks—like any in data analytics—can be fairly complex, they also rely on well-established processes, making life that little bit easier.

You may come across different variations of the data modeling process. But they all tick off the same general steps:

1. Determine the purpose of the model

First, determine the purpose of your model. What problem is the model trying to solve? And what specific requirements need to be met?

For example, if you’re planning to use the data for predictive analytics , the model should be designed to reflect this by focusing on the elements most relevant to this task. Determining a focus and clear set of goals will help you to identify the pertinent entities in the model and the relationships between them.

2. Identify the main entities in the model (and their attributes)

The next step is to identify the main entities in your model. Entities are the ‘things’ in your data that you are interested in.

For example, if you’re tracking customer orders, the main entities will be customers and orders. Meanwhile, you’ll also need to identify each entity’s attributes or values. In this case, customer attributes could be first and last names, and telephone numbers. Attributes for orders, meanwhile, might be the price of the order, what the item is, or the item’s SKU.

3. Define the relationships between entities

Once you’ve identified the main entities in the model, you’ll need to define the relationships between them.

F or example, if you’re tracking customer orders, the relationship between customers and orders might be that each customer’s address is also the shipping address. Defining these relationships is often achieved by creating a preliminary model that represents the rough structure of the data. This provides the first understanding of the data’s layout and potential problems it may have.

4. Identify integrity rules and constraints

Integrity rules and constraints keep data accurate and consistent while ensuring that they satisfy the functions of your database.

For instance, the data must be organized logically, and easy to retrieve, update, delete and search. If you’ve ever played around with Microsoft Excel , you’ll be more familiar with the idea of rules and constraints than you realize.

For example, if a column has the ‘NOT NULL’ constraint, it means the column cannot store NULL values. In practice, this might mean that an order has to have a customer name and a product number to be valid.

5. Identify data that needs to be included in the model

Next up, you’ll need to identify any data that needs to be included in the model.

This can be easily achieved by creating a diagram or sample to help you spot where there are gaps in your existing data. The additional data required might be data you already have access to, or it could be external data that needs funneling into your model.

6. Create, validate and update the model

The final step is to create and test the model, using appropriate sample data to ensure it meets the requirements outlined in step one.

Testing the model against real-world data will ensure it accurately presents the correct information and confirm that the model is performing as intended. You may need to update the model at this stage. Don’t worry if you do, it’s good practice. You’ll need to regularly update your data model anyway as new sources become available or as business needs change over time.

5. Examples of data modeling

In section 3, we outlined the different types of data models. In this section, we’ll focus on three examples of data models that are currently used:

Relational models

A relational model is one in which data is organized into tables, using unique columns and rows. Relational models also use what are known as keys; unique identifiers for each record in the database. 

While relatively straightforward to create, and useful for easily manipulating and retrieving data, relational models are also quite rigid. This rigidity means they are not always useful for highly complex data analytics tasks, but they remain popular for their ability to bring order to chaotic datasets.

In the real world, relational modeling is often used to organize information like financial transactions, products, calendar events, or any other ordered list of items.

Related reading: What is a relational database?

Dimensional models

A dimensional data model is one in which data is organized into ‘dimensions’ and ‘measures’.

Dimensions contain qualitative values (things like dates, addresses, or geographical data) which can be used to categorize, segment, or reveal details in your dataset. Meanwhile, measures contain quantitative values, i.e. numbers, that you can measure. 

This kind of model is often used in business since it lacks the rigidity of relational models and can be used to map more complex relationships. While this offers greater flexibility it does make the model more challenging to develop. While dimensional models can present more valuable insights, the trade-off is that it is usually harder to extract these.

In the real world, dimensional models are used for many analytics tasks, such as analyzing customer behavior, sales data, or financial trends over time.

Entity-relationship models

An entity-relationship model (or ER model) is a variation of the relational model. It is often used to describe the structure of the relationship between entities within a specific domain area. 

The items of interest we want to track might include people, products, or events. Meanwhile, relationships describe the connections between these entities, such as that between a person and a product, or a product and an event.

In the case of the example shown here, the relationship mapped is between students, enrolments, lectures, and subjects.

In the real world, entity-relationship models are often used to represent the structure of data in software applications, data warehouses, or other information systems.

6. Data modeling tools (and how to choose one)

While data modeling is an inescapably hands-on task, it has become much easier as more accessible data modeling tools have been introduced. These tools are often provided by DBMS providers and are typically designed to support their specific systems. 

However, most data modeling tools follow the same general principles. Namely, a good tool simplifies database design, considers your business rules, and minimizes the risk of unnecessary mistakes.

Some common data modeling tools include:

• erwin Data Modeler
• SQL Database Modeler
• IBM Infosphere Data Architect

How to choose a data modeling tool

The best data modeling tool for a given purpose depends on many factors, including your specific needs, the size and complexity of the data set, your organization’s strategic objectives, and the available budget. 

The best place to start is researching and finding out which tools are available. Once you’ve created a shortlist, here are some questions to ask yourself:

• Does the tool balance intuitive design (for the average user) with more advanced functionality (for more technical team members)?
• Is high performance important? Is the tool fast enough? Will it work under pressure in the real world?
• Data models require regular amends as your data and the situation changes. Does the tool allow you to update the model easily or it is a cumbersome task?
• How secure is the tool? Pretty much all tools claim to put security first, but you have statutory obligations to protect your data – does it meet the high standards in your specific jurisdiction?
• Do you have existing database systems in place that you intend to keep using? If so, will these integrate with your chosen tool?

When conducting your research, you’ll find it especially helpful to speak with other data professionals to see which tools they prefer. Finally, draw up a shortlist of tools and evaluate each one based on its features, price, and user reviews.

7. Benefits and challenges of data modeling

By mapping the relationships between data elements and the rules that govern them, data modeling can help you design an efficient and effective model for your database or DBMS.

There are many benefits to data modeling, including the ability to:

• Organize data in a way that is easy to understand and use
• Reduce data redundancy and improve consistency
• Improve data retrieval and storage
• Share data between different systems
• Improve the quality of data by providing a clear and consistent view 

Meanwhile, there are some challenges associated with data modeling, too. These include the need for:

• Careful planning and design to ensure the data model meets the needs of both the system and the business
• Skilled personnel who can understand and manipulate the data model
• Adequate resources (both time and money) to support the data modeling process

Overall, though, solving these challenges is a small price to pay. Trying to cut corners is a false economy and will result in much higher costs in the long run!

In this introductory guide, we’ve explored everything you need to know to get started with data modeling. We’ve learned about the relationships between data and how to create models that reflect these relationships. 

Data modeling isn’t just about making a database work but ensuring that it works in a way that makes sense for your business. Once you have a working model, you can start thinking about the database design and how you will implement it. A good model can lead to a well-performing, scalable database that will serve your organization for many years.

As we’ve shown, data modeling is a valuable skill with broad applications. To learn more about a potential career in data analytics, why not try this free, 5-day Data Analytics Short Course ? Sign up for five daily lessons direct to your inbox. Alternatively, check out the following introductory data analytics articles:

•  A complete guide to time series analysis and forecasting
• Data transformation: A beginner’s guide
• What is data mining?

The Agile Data (AD) Method

• Enterprise Architect
• Vertical Slicing
• Lean Data Governance

IE The   is simple and easy to read, and is well suited for high-level logical and enterprise data modeling. The only drawback of this notation, arguably an advantage, is that it does not support the identification of attributes of an entity. The assumption is that the attributes will be modeled with another diagram or simply described in the supporting documentation. Barker The   is one of the more popular ones, it is supported by Oracle’s toolset, and is well suited for all types of data models. It’s approach to subtyping can become clunky with hierarchies that go several levels deep. IDEF1X This notation is overly complex. It was originally intended for physical modeling but has been misapplied for logical modeling as well. Although popular within some U.S. government agencies, particularly the Department of Defense (DoD), this notation has been all but abandoned by everyone else. Avoid it if you can. UML This is not an official data modeling notation (yet). Although several suggestions for a   exist, none are complete and more importantly are not “official” UML yet. However, the Object Management Group (OMG) in December 2005 announced an RFP for data-oriented models.

3.  How to Model Data

It is critical for an application developer to have a grasp of the fundamentals of data modeling so they can not only read data models but also work effectively with Agile data engineers who are responsible for the data-oriented aspects of your initiative. Your goal reading this section is not to learn how to become a data modeler, instead it is simply to gain an appreciation of what is involved.

The following tasks are performed in an iterative manner:

• Identify entity types

Very good practical books about data modeling include  Joe Celko’s Data & Databases  and  Data Modeling for Information Professionals  as they both focus on practical issues with data modeling.  The Data Modeling Handbook  and  Data Model Patterns  are both excellent resources once you’ve mastered the fundamentals.  An Introduction to Database Systems  is a good academic treatise for anyone wishing to become a data specialist.

3.1  Identify Entity Types

An entity type, also simply called entity (not exactly accurate terminology, but very common in practice), is similar conceptually to object-orientation’s concept of a class – an entity type represents a collection of similar objects. An entity type could represent a collection of people, places, things, events, or concepts. Examples of entities in an order entry system would include  Customer ,  Address ,  Order ,  Item , and  Tax . If you were class modeling you would expect to discover classes with the exact same names. However, the difference between a class and an entity type is that classes have both data and behavior whereas entity types just have data.

Ideally an entity should be  normal , the data modeling world’s version of cohesive. A normal entity depicts one concept, just like a cohesive class models one concept. For example, customer and order are clearly two different concepts; therefore it makes sense to model them as separate entities.

3.2  Identify Attributes

Each entity type will have one or more data attributes. For example, in  Figure 1  you saw that the  Customer  entity has attributes such as  First Name  and  Surname  and in Figure 2 that the  TCUSTOMER table had corresponding data columns  CUST_FIRST_NAME  and  CUST_SURNAME  (a column is the implementation of a data attribute within a relational database).

Attributes should also be cohesive from the point of view of your domain, something that is often a judgment call. – in Figure 1 we decided that we wanted to model the fact that people had both first and last names instead of just a name (e.g. “Scott” and “Ambler” vs. “Scott Ambler”) whereas we did not distinguish between the sections of an American zip code (e.g. 90210-1234-5678). Getting the level of detail right can have a significant impact on your development and maintenance efforts. Refactoring a single data column into several columns can be difficult, database refactoring is described in detail in  Database Refactoring , although over-specifying an attribute (e.g. having three attributes for zip code when you only needed one) can result in overbuilding your system and hence you incur greater development and maintenance costs than you actually needed.

3.3  Apply Data Naming Conventions

Your organization should have standards and guidelines applicable to data modeling, something you should be able to obtain from your operational DBAs (if conventions don’t exist you should lobby to have some put in place). These guidelines should include naming conventions for both logical and physical modeling, the logical naming conventions should be focused on human readability whereas the physical naming conventions will reflect technical considerations. You can clearly see that different naming conventions were applied in Figures  1  and  2 .

As you saw in  Introduction to Agile Modeling , AM includes the  Apply Modeling Standards  practice. The basic idea is that developers should agree to and follow a common set of modeling standards on a software  initiative . Just like there is value in following common coding conventions, clean code that follows your chosen coding guidelines is easier to understand and evolve than code that doesn’t, there is similar value in following common modeling conventions.

3.4  Identify Relationships

In the real world entities have relationships with other entities. For example, customers PLACE orders, customers LIVE AT addresses, and line items ARE PART OF orders. Place, live at, and are part of are all terms that define relationships between entities. The relationships between entities are conceptually identical to the relationships (associations) between objects.

Figure 5  depicts a partial LDM for an online ordering system. The first thing to notice is the various styles applied to relationship names and roles – different relationships require different approaches. For example the relationship between  Customer  and  Order  has two names,  places  and  is placed by , whereas the relationship between  Customer  and  Address  has one. In this example having a second name on the relationship, the idea being that you want to specify how to read the relationship in each direction, is redundant – you’re better off to find a clear wording for a single relationship name, decreasing the clutter on your diagram. Similarly you will often find that by specifying the roles that an entity plays in a relationship will often negate the need to give the relationship a name (although some  CASE tools  may inadvertently force you to do this). For example the role of  billing address  and the label  billed to  are clearly redundant, you really only need one. For example the role  part of  that  Line Item  has in its relationship with  Order  is sufficiently obvious without a relationship name.

Figure 5. A logical data model (Information Engineering notation).

You also need to identify the cardinality and optionality of a relationship (the  UML  combines the concepts of optionality and cardinality into the single concept of multiplicity). Cardinality represents the concept of “how many” whereas optionality represents the concept of “whether you must have something.” For example, it is not enough to know that customers place orders. How many orders can a customer place? None, one, or several? Furthermore, relationships are two-way streets: not only do customers place orders, but orders are placed by customers. This leads to questions like: how many customers can be enrolled in any given order and is it possible to have an order with no customer involved?  Figure 5  shows that customers place zero or more orders and that any given order is placed by one customer and one customer only. It also shows that a customer lives at one or more addresses and that any given address has zero or more customers living at it.

Although the UML distinguishes between different types of relationships – associations, inheritance, aggregation, composition, and dependency – data modelers often aren’t as concerned with this issue as much as object modelers are. Subtyping, one application of inheritance, is often found in data models, an example of which is the  is a  relationship between  Item  and it’s two “sub entities”  Service  and  Product . Aggregation and composition are much less common and typically must be implied from the data model, as you see with the  part of  role that  Line Item  takes with  Order . UML dependencies are typically a software construct and therefore wouldn’t appear on a data model, unless of course it was a very highly detailed physical model that showed how views, triggers, or stored procedures depended on other aspects of the database schema.

3.5  Apply Data Model Patterns

Some data modelers will apply common data model patterns, David Hay’s book  Data Model Patterns is the best reference on the subject, just as object-oriented developers will apply analysis patterns and  design patterns . Data model patterns are conceptually closest to analysis patterns because they describe solutions to common domain issues. Hay’s book is a very good reference for anyone involved in analysis-level modeling, even when you’re taking an object approach instead of a data approach because his patterns model business structures from a wide variety of business domains.

3.6  Assign Keys

There are two fundamental strategies for assigning keys to tables. First, you could assign a natural key which is one or more existing data attributes that are unique to the business concept. The  Customer  table of  Figure 6  there was two candidate keys, in this case  CustomerNumber  and  SocialSecurityNumber . Second, you could introduce a new column, called a surrogate key, which is a key that has no business meaning. An example of which is the  AddressID  column of the  Address  table in  Figure 6 . Addresses don’t have an “easy” natural key because you would need to use all of the columns of the  Address  table to form a key for itself (you might be able to get away with just the combination of  Street  and  ZipCode  depending on your problem domain), therefore introducing a surrogate key is a much better option in this case.

Figure 6. Customer and Address revisited ( UML notation ).

Let’s consider  Figure 6  in more detail. Figure 6 presents an alternative design to that presented in  Figure 2 , a different naming convention was adopted and the model itself is more extensive. In Figure 6 the  Customer  table has the  CustomerNumber  column as its primary key and  SocialSecurityNumber  as an alternate key. This indicates that the preferred way to access customer information is through the value of a person’s customer number although your software can get at the same information if it has the person’s social security number. The  CustomerHasAddress  table has a composite primary key, the combination of  CustomerNumber  and  AddressID . A foreign key is one or more attributes in an entity type that represents a key, either primary or secondary, in another entity type. Foreign keys are used to maintain relationships between rows. For example, the relationships between rows in the  CustomerHasAddress  table and the  Customer  table is maintained by the  CustomerNumber  column within the  CustomerHasAddress  table. The interesting thing about the  CustomerNumber  column is the fact that it is part of the primary key for  CustomerHasAddress  as well as the foreign key to the  Customer  table. Similarly, the  AddressID  column is part of the primary key of  CustomerHasAddress  as well as a foreign key to the  Address  table to maintain the relationship with rows of  Address .

Although the “natural vs. surrogate” debate is one of the great religious issues within the data community, the fact is that neither strategy is perfect and you’ll discover that in practice (as we see in  Figure 6  ) sometimes it makes sense to use natural keys and sometimes it makes sense to use surrogate keys. In  Choosing a Primary Key: Natural or Surrogate?  I describe the relevant issues in detail.

3.7 Normalize to Reduce Data Redundancy

Data normalization  is a process in which data attributes within a data model are organized to increase the cohesion of entity types. In other words, the goal of data normalization is to reduce and even eliminate data redundancy, an important consideration for application developers because it is incredibly difficult to stores objects in a relational database that maintains the same information in several places.

Table 2  summarizes the three most common normalization rules describing how to put entity types into a series of increasing levels of normalization. Higher levels of data normalization (Date 2000) are beyond the scope of this book. With respect to terminology, a data schema is considered to be at the level of normalization of its least normalized entity type. For example, if all of your entity types are at second normal form (2NF) or higher then we say that your data schema is at 2NF.

Table 2. Data Normalization Rules.

	An entity type is in 1NF when it contains no repeating groups of data.
	An entity type is in 2NF when it is in 1NF and when all of its non-key attributes are fully dependent on its primary key.
	An entity type is in 3NF when it is in 2NF and when all of its attributes are directly dependent on the primary key.

Figure 7  depicts a database schema in ONF whereas  Figure 8  depicts a normalized schema in 3NF. Read the  Introduction to Data Normalization  essay for details.

Why data normalization? The advantage of having a highly normalized data schema is that information is stored in one place and one place only, reducing the possibility of inconsistent data. Furthermore, highly-normalized data schemas in general are closer conceptually to object-oriented schemas because the object-oriented goals of promoting high cohesion and loose coupling between classes results in similar solutions (at least from a data point of view). This generally makes it easier to  map your objects to your data schema . Unfortunately, normalization usually comes at a performance cost. With the data schema of  Figure 7  all the data for a single order is stored in one row (assuming orders of up to nine order items), making it very easy to access. With the data schema of  Figure 7  you could quickly determine the total amount of an order by reading the single row from the  Order0NF  table. To do so with the data schema of  Figure 8  you would need to read data from a row in the  Order  table, data from all the rows from the  OrderItem  table for that order and data from the corresponding rows in the  Item  table for each order item. For this query, the data schema of  Figure 7  very likely provides better performance.

Figure 7. An Initial Data Schema for Order ( UML Notation ).

Figure 8. A normalized schema in 3NF ( UML Notation ).

In class modeling, there is a similar concept called  Class Normalization  although that is beyond the scope of this article.

3.8  Denormalize to Improve Performance

Normalized data schemas, when put into production, often suffer from performance problems. This makes sense – the rules of data normalization focus on reducing data redundancy, not on improving performance of data access. An important part of data modeling is to denormalize portions of your data schema to improve database access times. For example, the data model of  Figure 9  looks nothing like the normalized schema of  Figure 8 . To understand why the differences between the schemas exist you must consider the performance needs of the application. The primary goal of this system is to process new orders from online customers as quickly as possible. To do this customers need to be able to search for items and add them to their order quickly, remove items from their order if need be, then have their final order totaled and recorded quickly. The secondary goal of the system is to the process, ship, and bill the orders afterwards.

Figure 9. A Denormalized Order Data Schema ( UML notation ).

To denormalize the data schema the following decisions were made:

• The Item table was left alone to support quick searching of item information.
• To support the addition and removal of order items to an order the concept of an  OrderItem  table was kept, albeit split in two to support outstanding orders and fulfilled orders. New order items can easily be inserted into the  OutstandingOrderItem  table, or removed from it, as needed.
• The Order  and  OrderItem  tables were reworked into pairs to handle outstanding and fulfilled orders respectively. Basic order information is first stored in the  OutstandingOrder  and  OutstandingOrderItem  tables and then when the order has been shipped and paid for the data is then removed from those tables and copied into the  FulfilledOrder  and  FulfilledOrderItem  tables respectively. Data access time to the two tables for outstanding orders is reduced because only the active orders are being stored there. On average an order may be outstanding for a couple of days, whereas for financial reporting reasons may be stored in the fulfilled order tables for several years until archived. There is a performance penalty under this scheme because of the need to delete outstanding orders and then resave them as fulfilled orders, clearly something that would need to be processed as a transaction.
• The contact information for the person(s) the order is being shipped and billed to was also denormalized back into the  Order  table, reducing the time it takes to write an order to the database because there is now one write instead of two or three. The retrieval and deletion times for that data would also be similarly improved.

Note that if your initial, normalized data design meets the performance needs of your application then it is fine as is. Denormalization should be resorted to only when performance testing shows that you have a problem with your objects and subsequent profiling reveals that you need to improve database access time. As my grandfather said, if it ain’t broke don’t fix it.

5. Evolutionary/ Agile Data Modeling

Evolutionary data modeling is data modeling performed in an iterative and incremental manner. The article  Evolutionary Development  explores evolutionary software development in greater detail. Agile data modeling is evolutionary data modeling done in a collaborative manner. The article  Agile Data Modeling: From Domain Modeling to Physical Modeling  works through a case study which shows how to take an agile approach to data modeling.

Although you wouldn’t think it, data modeling can be one of the most challenging tasks that an Agile data engineer can be involved with on an agile software development team. Your approach to data modeling will often be at the center of any controversy between the agile software developers and the traditional data professionals within your organization. Agile software developers will lean towards an evolutionary approach where data modeling is just one of many activities whereas traditional data professionals will often lean towards a  big design up front (BDUF)  approach where data models are the primary artifacts, if not THE artifacts. This problem results from a combination of the  cultural impedance mismatch , a misguided need to enforce the  “one truth” , and “normal” political maneuvering within your organization. As a result agile data engineers often find that navigating the political waters is an important part of their data modeling efforts.

6. How to  Become Better At Modeling Data

How do you improve your data modeling skills? Practice, practice, practice. Whenever you get a chance you should work closely with Agile data engineers, volunteer to  model data with them , and ask them questions as the work progresses. Agile data engineers will be following the AM practice  Model With Others  so should welcome the assistance as well as the questions – one of the best ways to really learn your craft is to have someone as “why are you doing it that way”. You should be able to learn physical data modeling skills from Agile data engineers, and often logical data modeling skills as well.

Similarly you should take the opportunity to work with the enterprise architects within your organization. As you saw in  Agile Enterprise Architecture  they should be taking an active role on your team, mentoring your team in the enterprise architecture (if any), mentoring you in modeling and architectural skills, and aiding in your team’s modeling and development efforts. Once again, volunteer to work with them and ask questions when you are doing so. Enterprise architects will be able to teach you conceptual and logical data modeling skills as well as instill an appreciation for enterprise issues.

You also need to do some reading. Although this article is a good start it is only a brief introduction. The best approach is to simply ask the Agile data engineers that you work with what they think you should read.

My final word of advice is that it is critical for application developers to understand and appreciate the fundamentals of data modeling. This is a valuable skill to have and has been since the 1970s. It also provides a common framework within which you can work with Agile data engineers, and may even prove to be the initial skill that enables you to make a career transition into becoming a full-fledged Agile data engineer.

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Data Models in DBMS

A Data Model in Database Management System (DBMS)  is the concept of tools that are developed to summarize the description of the database. Data Models provide us with a transparent picture of data which helps us in creating an actual database. It shows us from the design of the data to its proper implementation of data.

Types of Relational Models

• Conceptual Data Model
• Representational Data Model
• Physical Data Model

It is basically classified into 3 types:-

1. Conceptual Data Model

The conceptual data model describes the database at a very high level and is useful to understand the needs or requirements of the database. It is this model, that is used in the requirement-gathering process i.e. before the Database Designers start making a particular database. One such popular model is the entity/relationship model (ER model) . The E/R model specializes in entities, relationships, and even attributes that are used by database designers. In terms of this concept, a discussion can be made even with non-computer science(non-technical) users and stakeholders, and their requirements can be understood.

Entity-Relationship Model( ER Model): It is a high-level data model which is used to define the data and the relationships between them. It is basically a conceptual design of any database which is easy to design the view of data.

Components of ER Model:

• Entity: An entity is referred to as a real-world object. It can be a name, place, object, class, etc. These are represented by a rectangle in an ER Diagram.
• Attributes: An attribute can be defined as the description of the entity. These are represented by Ellipse in an ER Diagram. It can be Age, Roll Number, or Marks for a Student.
• Relationship: Relationships are used to define relations among different entities. Diamonds and Rhombus are used to show Relationships.

Characteristics of a conceptual data model

• Offers Organization-wide coverage of the business concepts.
• This type of Data Models are designed and developed for a business audience.
• The conceptual model is developed independently of hardware specifications like data storage capacity, location or software specifications like DBMS vendor and technology. The focus is to represent data as a user will see it in the “real world.”

Conceptual data models known as Domain models create a common vocabulary for all stakeholders by establishing basic concepts and scope

2. Representational Data Model

This type of data model is used to represent only the logical part of the database and does not represent the physical structure of the database. The representational data model allows us to focus primarily, on the design part of the database. A popular representational model is a Relational model . The relational Model consists of Relational Algebra and Relational Calculus . In the Relational Model, we basically use tables to represent our data and the relationships between them. It is a theoretical concept whose practical implementation is done in Physical Data Model. 

The advantage of using a Representational data model is to provide a foundation to form the base for the Physical model

3. Physical Data Model

 The physical Data Model is used to practically implement Relational Data Model. Ultimately, all data in a database is stored physically on a secondary storage device such as discs and tapes. This is stored in the form of files, records, and certain other data structures. It has all the information on the format in which the files are present and the structure of the databases, the presence of external data structures, and their relation to each other. Here, we basically save tables in memory so they can be accessed efficiently. In order to come up with a good physical model, we have to work on the relational model in a better way. Structured Query Language (SQL) is used to practically implement Relational Algebra.

This Data Model describes HOW the system will be implemented using a specific DBMS system. This model is typically created by DBA and developers. The purpose is actual implementation of the database.

Characteristics of a physical data model:

• The physical data model describes data need for a single project or application though it maybe integrated with other physical data models based on project scope.
• Data Model contains relationships between tables that which addresses cardinality and nullability of the relationships.
• Developed for a specific version of a DBMS, location, data storage or technology to be used in the project.
• Columns should have exact datatypes, lengths assigned and default values.
• Primary and Foreign keys, views, indexes, access profiles, and authorizations, etc. are defined

Some Other Data Models

1. hierarchical model.

The hierarchical Model is one of the oldest models in the data model which was developed by IBM, in the 1950s. In a hierarchical model, data are viewed as a collection of tables, or we can say segments that form a hierarchical relation. In this, the data is organized into a tree-like structure where each record consists of one parent record and many children. Even if the segments are connected as a chain-like structure by logical associations, then the instant structure can be a fan structure with multiple branches. We call the illogical associations as directional associations.

2. Network Model

The Network Model was formalized by the Database Task group in the 1960s. This model is the generalization of the hierarchical model. This model can consist of multiple parent segments and these segments are grouped as levels but there exists a logical association between the segments belonging to any level. Mostly, there exists a many-to-many logical association between any of the two segments.

3. Object-Oriented Data Model

In the Object-Oriented Data Model , data and their relationships are contained in a single structure which is referred to as an object in this data model. In this, real-world problems are represented as objects with different attributes. All objects have multiple relationships between them. Basically, it is a combination of Object Oriented programming and a Relational Database Model.

4. Float Data Model

The float data model basically consists of a two-dimensional array of data models that do not contain any duplicate elements in the array. This data model has one drawback it cannot store a large amount of data that is the tables can not be of large size.

5. Context Data Model

The Context data model is simply a data model which consists of more than one data model. For example, the Context data model consists of ER Model, Object-Oriented Data Model, etc. This model allows users to do more than one thing which each individual data model can do.

6. Semi-Structured Data Model

Semi-Structured data models deal with the data in a flexible way. Some entities may have extra attributes and some entities may have some missing attributes. Basically, you can represent data here in a flexible way.

Advantages of Data Models

• Data Models help us in representing data accurately.
•  It helps us in finding the missing data and also in minimizing Data Redundancy.
• Data Model provides data security in a better way.
• The data model should be detailed enough to be used for building the physical database.
• The information in the data model can be used for defining the relationship between tables, primary and foreign keys, and stored procedures.

Disadvantages of Data Models

• In the case of a vast database, sometimes it becomes difficult to understand the data model.
• You must have the proper knowledge of SQL to use physical models.
• Even smaller change made in structure require modification in the entire application.
• There is no set data manipulation language in DBMS.
• To develop Data model one should know physical data stored characteristics.
• Data modeling is the process of developing data model for the data to be stored in a Database.
• Data Models ensure consistency in naming conventions, default values, semantics, security while ensuring quality of the data.
• Data Model structure helps to define the relational tables, primary and foreign keys and stored procedures.
• There are three types of conceptual, logical, and physical.
• The main aim of conceptual model is to establish the entities, their attributes, and their relationships.
• Logical data model defines the structure of the data elements and set the relationships between them.
• A Physical Data Model describes the database specific implementation of the data model.
• The main goal of a designing data model is to make certain that data objects offered by the functional team are represented accurately.
• The biggest drawback is that even smaller change made in structure require modification in the entire application.
• Reading this Data Modeling tutorial, you will learn from the basic concepts such as What is Data Model? Introduction to different types of Data Model, advantages, disadvantages, and data model example.

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Statistics By Jim

Making statistics intuitive

Statistical Analysis Overview

By Jim Frost Leave a Comment

What is Statistical Analysis?

Statistical analysis involves assessing quantitative data to identify data characteristics, trends, and relationships. Scrolling through the raw values in a dataset provides virtually no useful information. Statistical analysis takes the raw data and provides insights into what the data mean. This process can improve understanding of the subject area by testing hypotheses, producing actionable results leading to improved outcomes, and making predictions, amongst many others.

The field of statistics is the science of learning from data, and it studies that process from beginning to end to understand how to produce trustworthy results. While statistical analysis provides tremendous benefits, obtaining valid results requires proper methods for collecting your sample , taking measurements, designing experiments, and using appropriate analytical techniques. Consequently, data analysts must carefully plan and understand the entire process, from data collection to statistical analysis. Alternatively, if someone else collected the data, the analyst must understand that context to interpret their results correctly.

If you’re good with numbers and enjoy working with data, statistical analysis could be the perfect career path for you. As big data, machine learning, and technology grow, the demand for skilled statistical analysts is rising. It’s a great time to build these skills and find a job that fits your interests.

Some of the fastest-growing career paths use statistical analysis, such as statisticians , data analysts, and data engineers. In this post, you’ll learn the different types of statistical analysis, their benefits, and the key steps.

Types Statistical Analysis

Given the broad range of uses for statistical analysis, there are several broad categories. These include descriptive, inferential, experimental, and predictive statistics. While the goals of these approaches differ, they all aim to take the raw data and turn them into meaningful information that helps you understand the subject area and make decisions.

Choosing the correct approach to bring the data to life is an essential part of the craft of statistical analysis. For all the following types of statistical analyses, you’ll use statistical reports, graphs, and tables to explain the results to others.

Descriptive

Descriptive statistical analysis describes a sample of data using various summary statistics such as measures of central tendency , variability , relative standing , and correlation . These results apply only to the items or people that the researchers measure and not to a broader population . Additionally, correlations do not necessarily imply causation.

For example, you can report the mean test score and the correlation between hours of studying and test scores for a specific class. These results apply only to this class and no one else. Do not assume the correlation implies causation.

Inferential

Inferential statistical analysis goes a step further and uses a representative sample to estimate the properties of an entire population. A sample is a subset of the population. Usually, populations are so large that it’s impossible to measure everyone in a population.

For example, if you draw a simple random sample of students and administer a test, statistical analysis of the data allows you to estimate the properties of the population. Statistical analysis in the form of hypothesis testing can determine whether the effects and correlations you observe in the sample also exist in the population.

Suppose the hypothesis test results for the correlation between hours studying and test score is statistically significant . In that case, you can conclude that the correlation you see in the sample also exists in the larger population. Despite being statistically significant, the correlation still does not imply causation because the researchers did not use an experimental design.

The sample correlation estimates the population correlation. However, because you didn’t measure everyone in the population, you must account for sampling error by applying a margin of error around the sample estimate using a confidence interval .

Learn more about the Differences between Descriptive and Inferential Statistical Analysis .

Designed Experiments

Statistical analysis of experimental designs strives to identify causal relationships between variables rather than mere correlation. Observing a correlation in inferential statistics does not suggest a causal relationship exists. You must design an experiment to evaluate causality. Typically, this process involves randomly assigning subjects to treatment and control groups.

Does increasing study hours cause test scores to improve or not? Without a designed experiment, you can’t rule out the possibility that a confounding variable and not studying caused the test scores to improve.

Suppose you randomly assign students to high and low study-time experimental groups. The statistical analysis indicates the longer-duration study group has a higher mean score than the shorter-duration group. The difference is statistically significant. These results provide evidence that study time causes changes in the test scores.

Learn more about Correlation vs. Causation and Experimental Design: Definition and Types .

Predictive statistical analysis doesn’t necessarily strive to understand why and how one variable affects another. Instead, it predicts outcomes as precisely as possible. These analyses can use causal or non-causal correlations to predict outcomes.

For example, assume that the number of ice cream cones consumed predicts the number of shark attacks in a beach town. The correlation is not causal because ice cream cone consumption doesn’t cause shark attacks. However, the number of cones sold reflects favorable weather conditions and the number of beachgoers. Those variables do cause changes in the number of shark attacks. If the number of cones is easier to measure and predicts shark attacks better than other measures, it’s a good predictive model.

Three Key Steps in Statistical Analysis

Producing trustworthy statistical analysis requires following several key steps. Each phase plays a vital role in ensuring that the data collected is accurate, the methods are sound, and the results are reliable. From careful planning to adequate sampling and insightful data analysis, these steps help researchers and businesses make informed, data-driven decisions. Below, I outline the major steps involved in conducting solid statistical analysis.

The planning step is essential for creating well-structured experiments and studies that effectively set up the statistical analysis from the start. Whether working in a lab, conducting fieldwork, or designing surveys, this stage ensures that the research design gathers data that statistical analysis can use to answer the research question effectively. Researchers can make informed decisions about variables, sample sizes, and sampling methods by analyzing data patterns and previous statistical analyses. Using the proper sampling techniques allows researchers to work with a manageable portion of the population while maintaining accuracy.

This careful preparation reduces errors and saves resources, leading to more reliable results. Optimizing research strategies during the planning phase allows scientists and businesses to focus on the most relevant aspects of their investigations, which results in more precise findings. Researchers design the best studies when they keep the statistical analysis in mind.

Learn more about Planning Sample Sizes and Sampling Methods: Different Types in Research .

Data Collection

After all the planning, the next step is to go out and execute the plan. This process involves collecting the sample and taking the measurements. It might also require implementing the treatment under controlled conditions if it’s an experiment.

For studies that use data collected by others, the researchers must acquire, prepare, and clean the data before performing the statistical analysis. In this context, a crucial part of sound statistical analysis is understanding how the data were gathered. Analysts must review the methods used in data collection to identify potential sampling biases or errors that could affect the results. Sampling techniques, data sources, and collection conditions can all introduce variability or skew the data. Without this awareness, an analyst risks drawing flawed conclusions.

Analysts can adjust their approach and account for any limitations by carefully examining how the data was collected, ensuring their statistical analysis remains accurate and trustworthy.

Statistical Analysis

After data collection, the statistical analysis step transforms raw data into meaningful insights that can inform real-world decisions. Ideally, the preceding steps have all set the stage for this analysis.

Successful research plans and their effective execution allow statistical analysis to produce clear, understandable results, making it easy to identify trends, draw conclusions, and forecast outcomes. These insights not only clarify current conditions but also help anticipate future developments. Statistical analysis drives business strategies and scientific advancements by converting data into actionable information.

Learn more about Hypothesis Testing and Regression Analysis .

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