Data Modeling and Analytics: A Comprehensive Guide

Arsalan Mohammed • Last Modified: December 29th, 2022

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Nowadays there is a steep rise in the data-related applications in all data-driven companies. The concepts of Data Modeling and Data Analytics have become very important. Both these concepts revolve around data and usually are interdependent on each other.

Data Analysis is a process of gaining valuable insights by cleaning, transforming, and modeling the data. Data Analysis is an important process in generating important decisions based on the data. Data Analysis also helps in visualizing the enterprise data for better understanding.

Data Modeling is the process of mapping and visualizing the complete methodology behind the collection, updating, and storage of enterprise data for gaining maximum insights along with stable data usage.

In this article, you will get to know in detail about the concepts of Data modeling and analytics along with a few use cases.

Table of Contents

What is Data Modeling?

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Data modeling is the process of generating and analyzing relations between different components of the enterprise data (creating a data model) starting from generation, storage, and many more. It is performed by a set of tools that analyze the complete history of the data and the impact of data on the software systems.

The Data Model defines how the final data is displayed to the user. It also determines the steps to fit together and defines the flow of data from one step to another. This process is also important for designing business-critical IT systems of an organization in determining if the new system will work flawlessly, the type of data required to meet needs, and the data usage.

Data Model consists of a complex software system design diagram made using the combination of text and symbols, representing the flow of data between the steps as well as the final state of data. It acts as a blueprint for creating newer systems.

Data Modeling also utilizes some basic concepts of Data Analysis. A Data Modeller has expertise in both data modeling and analytics.

Types of Data Models

  • Conceptual Data Model: This model determines the main aspects of business data and finds the most important parameters for the business insights. 
  • Logical Data Model: This model focuses more stringently on each parameter of the business data. This helps in looking at all the technical details of the data that would help the business.
  • Physical Data Model: This model creates the actual blueprints for the data flow. This model forensically determines the details like databases, applications, features, and many more.

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What is Data Analytics?

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Data Analytics is the process of transforming, processing, and organizing raw data to gain valuable insights that are relevant to business decisions and provide actionable insights to provide maximum value to the enterprise data. This process also helps in measuring the decisions made reducing the risks associated with making decisions based on data. This process also converts the raw data into insights in the form of visual applications like graphs, pie charts, tables, and many more.

Data Analytics has gained importance in recent times and data-driven companies have adopted this process to better understand their target customers and reduce losses by making informed decisions. This process also helps in altering the advertising campaigns as well. The process of data analytics has advanced a lot and is now becoming automated using various algorithms and even adopted in mechanical sectors to convert raw data into sensible conclusions.

Types of Data Analysis

  • Quantitative Data Analysis: This data analysis technique focuses mostly on the statistical aspects of the enterprise data. This determines the pattern in the data and finds the rise and fall of data in a timeline. There are two types of Qualitative Data Analysis:
    • Descriptive Analysis: This type of Data Analysis technique is used to find the patterns that exist in a particular data. This technique deals with applying mathematical aspects like percentages, frequencies, and central tendencies ( finding mean, median, mode) to give a description of the contents of data.
    • Inferential Analysis: This technique is used when there is a correlation between the data sets and is required to find the differences. This technique deals with aspects like t-tests, chi-square, and many more.
  • Qualitative Data Analysis: Qualitative analysis is the opposite of quantitative analysis, this analysis technique usually deals with non-numeric data like audio, video, images, and many more, It also determines the changes that take place in data.

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Uses of Data Modelling and Analytics

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Better Quality of Applications

The first and foremost benefit of data modeling and analytics is the ability to generate higher-quality applications that are stable and less error-prone, reducing application crashes and in turn reducing maintenance efforts. Users usually create applications without the use of a data modeling process which results in the following consequences:

  • The raw data and user information is directly stuffed into the variables.
  • These variables are manipulated throughout the course of codes and create newer values based on the initial variables. 
  • This process continues and finally, it becomes impossible to revert back.

The size of an organization doesn’t matter when the code is written without a proper structure. Without structure, the code becomes a mess that cannot be solved. This also reduces the options for updates and modification to the existing codes since it is highly tangled and difficult to understand.

Better Requirement Analysis for Application development

Data Modeling helps is creating and gathering the tangible information that enterprises could rely on. The data model deals with the collection of data and the requirements for creating the applications. Having a proper requirement documented and formatted reduces the misinterpretations and reduces the efforts to analyze the requirements. Data Modeling and analytics allow for proper focus on the compartmentalized efforts of each team toward the application.

It also employs the use of jargon in the model, which is forwarded into the development phase of the application. Data Modeling and analytics help in creating a more competitive and sophisticated product that meets the customer requirements a lot better. This also means that the results of analytics performed on the requirements data are a lot better interpreted.

Better Risk Management for Application

Performing Data Modeling and analytics on an existing ideology about a product helps in understanding and mitigating any foreseen risk associated with it in the development phase. This helps in structuring and planning newer methodologies that would reduce the risk of applications being released in the market among the target audiences.

This Data modeling and analytics also help in calculating the complexity of the procedural methodology to be applied in the development phase of the product. This enables the developers to take a simpler but effective path reducing the cost, efforts, and risk (of failure and incompatibility) while developing the product.

Reduced Time for Application development

Data modeling plays an important part in the development process of a new application. This directly impacts the cost and time associated with the application. If a proper data model is made before the process of development starts, it reduces the time required for the requirement gathering, planning on the go, and errors caused due to it.

Creating a data model helps in changes further down the lifecycle. For instance, when there is a requirement to add new tables to the program you can directly add them to the data model and update the existing program without major confusion and structural imbalances.

If there is no data model, the team would need to update the database as well as code which is time-consuming since there is no structure and the consequences of each change would need to be managed. And in case there are multiple changes spread across the code base, it is a very difficult task to maintain consistency and robustness in code.

Early detection of errors and data incompatibility issues

Usually, in a program where there is no proper data modeling and analytics, the errors in data are not found until the program is executing. When the user uses the application and an error message pops up regarding the bad data, this means that the data was bad from the start, and since no data modeling and analytics were performed on it, it was impossible to detect these errors in the testing phase of the applications. The earlier detection helps in solving it before it brings a negative impact on the application and its users.

Data Modeling and Analytics give an accurate view of the user interactions with the application and business data, even the minute details like which specific parts are accessed and how it is used by them. This allows employing corrections to the critical parts that are found by the information provided by data modeling and analytics. The data model audits also enable you to find the optimizations that will benefit the users the most.

Better performance of the application

Data modeling and analytics not only save money but also makes the applications run faster and more efficiently. Data modeling impacts the performance of applications by charting a plan that determines the usage of data by the application. This enables the developers to know the kind of data and the storage locations of the data as well. This enables them to write efficient codes to retrieve the data quickly.

Using unstructured and unorganized data from tables causes the developers to write more SQL queries to just figure out the location of data. Through data modeling and analytics, the data is structured into tables which enables the finding of the desired information. This makes applications run faster without slowing down for large amounts of data processing.

Documentation for future maintenance and support

Data Model allows you to find the relationships between different entities and processes. Data modeling and analytics are used to define all these entity relationships at a single location for easier access resulting in easier maintenance of the processes.

Data modeling and analytics also help in documenting the application’s design and business requirements. Being a single source it becomes easier to understand by all the teams removing any changes that occur due to the transmission of information. Also, all the changes and implementations can be monitored efficiently, Data modeling and analytics require expertise but the benefits are higher.


Data modeling and analytics are important techniques that are required for data-driven organizations to thrive. Data modeling deals with the representation and planning of the structure and flow of data, whereas Data Analytics deals with gaining valuable insights to shape the decisions of the organization. Data modeling requires parts of data analysis in order to derive a blueprint. This article provided a comprehensive guide on Data modeling and analytics along with the five best use case benefits it provides. 

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