It is now easy for businesses to understand their customers than ever. This has been made possible by the huge amounts of data available today. A business that uses data to understand its customers has better chances of growth than others. But you will need some techniques to un-complicate this data into useful information that organizations can utilize for smart decision-making and strategy. That is why it is always beneficial to have Data Modeling and Visualization tools and techniques in your arsenal.

No matter where you work or what you do, data will always be a part of your process. And, for data to become valuable, Data Modeling and Data Visualization are important. By the use of visuals such as graphs and charts, businesses are able to extract hidden patterns and trends from data. This makes it easy to understand and interpret data. There is also a need to ensure that the data stored in a Database is accurately represented. It should be easy to extract relationships, rules, and associations from the data. Data Modeling makes this possible. This article discusses Data Modeling & Visualization in detail.

What is Data Modeling?

Data Modeling and Visualization: Data Modeling
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Data Modeling refers to the process of creating a visual representation of an entire information system or some of its parts to communicate the relationships between data points and structures. The purpose is to show the types of data stored in the system, the relationships among the data types, the formats and attributes of the data, and how the data can be grouped and organized.

Data Models are normally created around business needs. Requirements and rules are defined upfront via feedback obtained from business stakeholders so that they can be used for designing a new system. The Data Modeling process begins with the collection of information about business requirements from both stakeholders and end-users. The business requirements are then translated into data structures for the formulation of a concrete Database design.

Today, Data Modeling finds its application across every sector you could possibly think of, from Financial Institutions to the Healthcare Industry. A study by LinkedIn rates Data Modeling as the fastest-growing profession in the present job market.

What is Data Visualization?

Data Modeling and Visualization: Data Visualization
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Data Visualization refers to the process of representing data and information graphically. By the use of visual elements like graphs, charts, and maps, Data Visualization tools offer an accessible way to view and understand trends and patterns in data. 

Data Visualization helps organizations to analyze huge volumes of data and make data-driven decisions. It also makes it easy for individuals and companies to understand data. Data Visualization is very useful today as companies are generating and collecting huge data volumes. It can help them to unmask hidden gems from data, which are good for growth.

Data Modeling and Visualization: Key Similarities

Data Modeling and Visualization: Similarities
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The following are the key similarities between Data Modeling and Visualization:

  • They both deal with Data: Data is at the center of both Data Modeling and Data Visualization. They help users make sense of vague sets of data and get the relevant metrics to help in better decision-making. 
  • No need for ML Algorithms: Both Data Modeling and Visualization don’t require the use of Machine Learning algorithms to get the correct results. 
  • They both use Visual Elements: In both Data Modeling and Data Visualization, the answers are in the form of visual elements rather than text or numbers. However, they differ in the types of visual elements that are used.
  • No need for Data Analysis: Both Data Modeling and Visualization don’t require data to be analyzed. Instead, Data Engineers and Data Modelers go straight into working with the data the way it is to discover inconsistencies in the data.

Data Modeling and Visualization: Key Differences

The following table summarizes the differences between Data Modeling and Visualization:

FeatureData ModelingData Visualization
DefinitionData Modeling refers to designing the Entity-Relationship modeling for Database tables to establish the connections between tables. It also involves designing the schema for Data Warehouses. Thus, it shows how tables are connected in schema terms. Data Visualization involves presenting data in a visual context to show hidden trends and patterns in data. Such trends and patterns may not be explicit in text data. Visualization makes data easy for anyone to understand. 
TechniquesData Modeling techniques include Entity-Relationship Diagrams (ERDs) to depict the way data has been stored in the Database. The ERDs show the types of relationships between the different tables in the Database, whether one-to-many, many-to-many, etc. It also uses data dictionaries and Unified Modeling Language (UML).Data Visualization involves the use of graphs, charts, and tables to present data visually. These visual tools show how the different data attributes are related to each other. 
Used ForData Modeling is used to ensure that data is stored in a database and represented accurately. It shows the inherent structure of data by identifying data identities, attributes, and the relationship between the entities. Data Visualization is used to communicate information clearly and efficiently to the users by presenting it using visual elements. 
BenefitsFacilitate faster access to data across the entire organization. Data Modeling also makes it easy to establish the correct structure of data and enforce compliance standards. Helps businesses understand their customers, products, and processes better. This is good for sound decision-making and making predictions. 
ToolsCommon Data Modeling tools include Erwin Data Modeler, ER/Studio, DbSchema, ERBuilder, HeidiSQL, Navicat Data Modeler, Toad Data Modeler, Archi, and others.  Data Visualization is done using tools such as Knowi, Tableau, Dygraphs, QlikView, DataHero, ZingCHhart, Domo, and others. It can also be done in programming languages such as Python and R. 
Performed ByData Architects and Modelers.Data Engineers.
Data Modeling vs Data Visualization


This is what you’ve learned in this article:

  • Businesses rely on data to make evidence-based decisions for growth. 
  • Data Modeling and Visualization make data more valuable to an organization. Data Modeling visualizes the entire or only some parts of an information system to establish the relationship between data points and structures. It shows the relationship between various entities in a database. 
  • Data Visualization on the other hand involves presenting data visually using graphics. It helps businesses to extract hidden trends and patterns from data for decision-making. 
  • Both Data Modeling and Visualization deal with data and use visual elements to present data, but there are significant differences between the two. 
  • Data Modeling techniques include the use of ERD, UML, and Data Dictionaries to present the entities of an information system. Data Visualization techniques involve the use of charts, graphs, and tables to present data visually. 

However, it’s easy to become lost in a blend of data from multiple sources. Imagine trying to make heads or tails of such data. This is where Hevo comes in.

Hevo Data with its strong integration with 150+ Sources allows you to not only export data from multiple sources & load data to the destinations, but also transform & enrich your data, & make it analysis-ready so that you can focus only on your key business needs and perform insightful analysis.

Give Hevo Data a try and sign up for a 14-day free trial today. Hevo offers plans & pricing for different use cases and business needs, check them out!

Share your experience of understanding Data Modeling and Visualization in the comments section below.

Nicholas Samuel
Technical Content Writer, Hevo Data

Nicholas Samuel is a technical writing specialist with a passion for data, having more than 14+ years of experience in the field. With his skills in data analysis, data visualization, and business intelligence, he has delivered over 200 blogs. In his early years as a systems software developer at Airtel Kenya, he developed applications, using Java, Android platform, and web applications with PHP. He also performed Oracle database backups, recovery operations, and performance tuning. Nicholas was also involved in projects that demanded in-depth knowledge of Unix system administration, specifically with HP-UX servers. Through his writing, he intends to share the hands-on experience he gained to make the lives of data practitioners better.

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