Data Visualization vs. Data Modeling: A Detailed Comparison

In an era where data is often called the "new oil," it's imperative to understand the tools and techniques harnessing its potential. Data visualization and modeling stand out as twin titans. However, their distinct roles often lead to confusion. This blog will demystify the realms of data visualization and data modeling.

Data visualization is the graphical representation of information and data. On the other hand, data modeling involves the creation of abstract representations of the data structure. 

At the crossroads of these two concepts, a million-dollar question arises–how do they differ, and how can they work together to bring out the best in data? Continue reading to find out. 

What Is Data Modeling?

Data modeling is like creating a blueprint of an information system, or parts of it, to illustrate the connections between different data points and structures. It's all about identifying the kinds of data in the system, their relations, formats, and features, and how they can be neatly grouped and organized.

This process is based on the business’ needs. The initial steps involve gathering business needs directly from stakeholders and end-users. These needs, along with specific rules and requirements, are then transformed into data structures, which pave the way for designing a solid database. In today's world, data modeling is used everywhere, from banks to hospitals. 

Types of Data Models

The three most common types of data models are relational, dimensional, and entity-relationship (E-R). There are two advanced models—Object-Oriented Data Model and Dimensional Data Model. Besides these, there are a few others, like hierarchical, network, object-oriented, and multi-value, but they aren't as widely used. 

The type of data model you choose sets the stage for how data is stored and organized and how you can retrieve it. 

1. Relational

Think of the relational model as an old-school but popular approach. It organizes data into tables with rows and columns. Imagine you have data on mobile phones. Your basic elements are the 'type of phones' and 'number of phones.' 'Type' is a dimension—like the description or location, and 'number' is a measure used for math stuff like adding or averaging. 

These tables are called 'relations,’ the columns are 'attributes,' the rows are 'tuples,' and the 'domain' is the group of values a column can have. You can link tables using common data elements (called keys). Like, you can relate phone types to their prices or suppliers.

2. Dimensional

This model adapts to how a business uses data. It's designed for faster online queries and is excellent for data warehouses. Imagine you're keeping track of apple sales. The number of apples sold is a 'fact,' and the info around it (like apple type, price, and sale date) are 'dimensions.' 

A fact table is the main table in a dimensional model. It's fast to get data for a specific activity, but analyzing the data might need some extra work since it doesn't link everything together like the relational model.

3. Entity-Relationship (E-R)

E-R models are like visual diagrams showing how different business data are connected. They've got boxes for activities or 'entities' and lines, which show how these boxes are linked, or the 'relationships.' This diagram helps in building a relational database. For example, every row might represent a phone, and the fields in that row have attributes like type and color. Key data elements tie tables together.

4. Object-Oriented Data Model

This more advanced model uses objects as the fundamental structure for defining and describing data, as opposed to the entity-relationship model, which represents data in terms of entities and their relationships. 

Objects mirror real-world entities and comprise various attributes. For instance, as an object, a customer would have attributes like name, address, phone number, and email address. In an entity-relationship model, these attributes are stored in different tables to establish relationships between these tables.

5. Dimensional Data Model

Dimensional data models are the backbone of business intelligence (BI) and online analytical processing (OLAP) systems. Primarily used in data warehouses for maintaining historical transactional data, you can also utilize these models for smaller datasets. 

The dimensional data models include several structures, like fact tables, dimension tables, and lookup tables, which together form the foundation for creating enterprise data warehouses and online transaction processing systems. 

The principal goal of a dimensional model is to facilitate rapid responses to business-related queries about forecasts, trends, and more. By providing an organized approach for business intelligence reporting, dimensional modeling enhances cross-departmental information sharing, fostering efficient collaboration and decision-making within an organization.

Role of Data Modeling in Data Analytics

Data modeling is key for effective data analytics. It ensures that data is structured, organized, and understood, making extracting meaningful insights from the data easier. For example, data modeling helps in:

1. Designing Databases: Data modeling helps create the structure of databases, including the development of schemas, which outline the organization of data in a database, the types of data you can store, and how these different data elements are related. This forms the foundation for efficient data storage and retrieval, essential for data analytics.

2. Understanding Data Relationships: Data modeling involves defining relationships between various data entities. Understanding these relationships is key in data analytics, as it can reveal patterns and dependencies that might otherwise be overlooked.

3. Integrating Data: Data modeling aids in integrating data from various sources by ensuring you can merge and cohesively analyze data from different databases. This is especially important when dealing with large data sets or conducting complex analyses involving multiple databases.

4. Predictive Modeling: Data models are often used in predictive modeling, a key aspect of data analytics. Use these models to predict future trends, identify risks, and find opportunities based on historical data and trends.

5. Ensuring Data Consistency: By defining the rules for data, data modeling can help ensure the quality and consistency of data. It helps prevent errors in data analysis and can make the results of data analysis more reliable and accurate.

Benefits of Data Modeling

Data modeling plays an integral role in organizing and structuring data. It allows for a visual representation of data and facilitates better understanding and utilization of this data. Here are five key benefits of data modeling:

1. Ensures Consistency

Data modeling enforces standards ensuring data consistency across the organization so everyone uses and interprets the data in the same way, reducing confusion and misinterpretation when conducting analysis.

2. Facilitates Data Integration

Data modeling is crucial when integrating data from various sources. It helps map relationships between different data types and sources, ensuring data can be accurately combined and analyzed collectively.

3. Improves Data Quality

Through data modeling, inconsistencies, duplications, and errors can be identified and addressed early in the data preparation process. It leads to higher-quality data, resulting in more accurate and reliable analytics.

What is Data Visualization? 

In today's data-driven world, there is an unprecedented rise in data volume daily. Making sense of these massive amounts of data, often quantified in quintillions of bytes, would be arduous without tools and strategies for data proliferation, such as data visualization.

Data visualization enables businesses to spot data trends that would otherwise be challenging to decipher. This graphical representation of data empowers analysts to visualize concepts and detect emerging patterns more intuitively. These tools provide a clear and accessible way to interpret complex data patterns and insights, facilitating informed decision-making.

Common Data Visualization Techniques

Here are some standard data visualization techniques and their benefits:

1. Dashboards: These provide a quick overview of the key performance indicators (KPIs) relevant to a particular objective or business process.

2. Graphs: Graphs are versatile and can represent complex data relationships effectively.

3. Infographics: These are visually appealing presentations of data or information, typically used for simplifying complex information.

4. Maps: Represent geographic data using maps to understand location-based trends and patterns.

5. Charts: Charts are highly effective in comparing data sets and understanding their relationships.

6. Videos: These can dynamically represent data changes over time, providing a more interactive experience.

7. Slides: Slides offer a sequential representation of data, useful in presentations or reports.

Benefits of Data Visualization

1. Enhanced Decision-Making: By visualizing data, decision-makers can better understand intricate data relationships, improving insights and decision-making processes.

2. Improved Data Interpretation: Data visualization simplifies complex data sets, making them more accessible and easier to interpret.

3. Discovering Patterns: Visualization helps identify emerging patterns and trends that might go unnoticed in text-based data.

Choosing Between Data Modeling and Visualization: A Detailed Comparison

Here’s a quick overview of the differences between Data Modeling and Visualization:

Feature	Data Modeling	Data Visualization
Definition	Data modeling involves creating an Entity-Relationship model for database tables to elucidate the links between them. It includes designing schemas for Data Warehouses and representing the interconnections between tables.	Data Visualization, on the other hand, is the practice of presenting data in a graphical or pictorial format to reveal hidden data trends and patterns. It translates complex data sets into an easy-to-understand format.
Techniques	Techniques utilized in data modeling include Entity-Relationship Diagrams (ERDs), which demonstrate the relationships among different database tables. It also employs data dictionaries and Unified Modeling Language (UML).	Data visualization uses various graphical elements such as graphs, charts, and tables to visually present data, showing the interrelations between diverse data attributes.
Used For	Data modeling aims to ensure accurate data representation and storage within a database. It unveils the inherent data structure by identifying data entities, their attributes, and their interrelationships.	Data Visualization helps efficiently communicate information to users by employing graphical elements for data representation.
Benefits	Data Modeling allows quicker data access throughout an organization, facilitates correct data structuring, and helps enforce data compliance standards.	Data Visualization aids businesses in gaining a deeper understanding of their customers, products, and processes, enhancing decision-making capabilities and predictive abilities.
Tools	Various tools for data modeling include Erwin Data Modeler, ER/Studio, DbSchema, ERBuilder, HeidiSQL, Navicat Data Modeler, Toad Data Modeler, and Archi, among others.	Tools used for data visualization include Knowi, Tableau, Dygraphs, QlikView, DataHero, ZingCHhart, and Domo, along with programming languages like Python and R.
Performed By	Data Architects and Data Modelers typically carry out data modeling tasks.	Data Engineers usually perform Data Visualization tasks.

When to use Data Modeling

1. Designing Databases: Use data modeling when designing databases. It creates a visual representation of data and establishes the relationships between different data elements.

2. Improving Data Consistency: Enforce business rules, regulatory compliances, and government policies on data to improve the consistency, quality, and reliability of the data.

3. Increasing Efficiency of Data Processing: A well-designed data model can increase efficiency in data processing, as it helps structure data to be easily accessed, managed, and updated.

4. Data Integration: Data modeling is critical when integrating disparate data sources, providing a unified view of data elements and their relationships.

When to use Data Visualization

1. Interpreting Complex Data Sets: Use data visualization to analyze complex datasets. It helps to understand patterns, trends, and correlations in the data.

2. Presenting Data to Non-Technical Users: If you are presenting data to stakeholders or users who may not have technical expertise, data visualization can be an effective tool. It simplifies data interpretation by depicting it in a graphical format.

3. Making Informed Decisions: Use it to make data-driven decisions. Visualization provides a clear, quick, and efficient way to understand and analyze data, enabling informed decision-making.

4. Data Reporting: Data visualization is crucial for data reporting as it provides an appealing and straightforward way to represent the results.

How Data Modeling Supports the Creation of Meaningful Visualizations

Data modeling plays a pivotal role in supporting the creation of meaningful visualizations. Here's how:

1. Structured and Organized Data: A well-structured data model organizes data coherently and logically. This structure facilitates the extraction and presentation of data in various visualization formats, making it easier to interpret and understand.

2. Ensuring Data Integrity: By establishing relationships between different data elements, data modeling helps to maintain data integrity. This integrity ensures the accuracy and consistency of data visualizations, as the visualizations are based on trustworthy and reliable data.

3. Improves Efficiency: A good data model improves data processing efficiency, making it faster and easier to retrieve the data needed for visualizations. It can be particularly beneficial when dealing with large volumes of data or complex data sets.

4. Facilitates Data Analysis: Data modeling can facilitate more in-depth data analysis by providing a clear, detailed view of how data is related and organized. 

5. Supports Interactivity: In many cases, modern data visualization tools require data to be structured in a certain way to support interactive features. A well-designed data model ensures the data is organized so you can use effectively with these tools.

6. Better Understanding of Data Relationships: Data modeling helps understand relationships between different entities and attributes and creates visualizations highlighting these relationships, providing deeper insights.

Conclusion

To sum it up, data modeling and visualization are like two sides of the same coin, each playing a pivotal role in the data analysis journey. They are different tools used at various stages of data analysis, each critical in its own right.

Data modeling is about crafting a robust structure for your data, forming the foundation during the database design phase. On the other hand, data visualization steps in after data is processed and analyzed, presenting data in an easy-to-understand and interpretable way.
But navigating this data journey can be challenging, and that's where Growth Natives comes in. We are experts at handling your data needs, from helping you build customizable data models for your business to crafting compelling data visualizations. Email us at info@growthnatives.com or call +1 855-693-4769 to connect with us and let us help you unlock the true potential of your data.