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6 Data Modeling Techniques For Better Business Intelligence

Data modelling techniques - Code written in yellow and turquoise

Summary - Data modeling plays a crucial role in the growth of any business who understands that data-driven decisions are key to their success. Having your data in the right format makes sure that you can get to the answers to your business questions more easily and quickly.


Every day 2.5 quintillion bytes of data are created, and this pace is likewise accelerating at a daily rate. With so much information at our disposal, it is becoming increasingly important for organizations and enterprises to access and analyze the relevant data to predict outcomes and improve services.

However, arbitrarily organizing the data into random structures and relationships is not enough. In order to access the data properly and extract the most out of it, it is essential to model your data correctly.

So, What is Data Modeling Exactly?

In simple terms, data modelling is nothing but a process through which data is stored structurally in a format in a database. Data modelling is important because it enables organizations to make data-driven decisions and meet varied business goals.

The entire process of data modelling is not as easy as it seems, though. You are required to have a deeper understanding of the structure of an organization and then propose a solution that aligns with its end-goals and suffices it in achieving the desired objectives.

Types of Data Models

Data modeling can be achieved in various ways. However, the basic concept of each of them remains the same. Let’s have a look at the commonly used data modeling methods:

Hierarchical model

As the name indicates, this data model makes use of hierarchy to structure the data in a tree-like format. However, retrieving and accessing data is difficult in a hierarchical database. This is why it is rarely used now.

Hierarchical data model example

Relational model

Proposed as an alternative to hierarchical model by an IBM researcher, here data is represented in the form of tables. It reduces the complexity and provides a clear overview of the data.

Relational data model example

Network model

The network model is inspired by the hierarchical model. However, unlike the hierarchical model, this model makes it easier to convey complex relationships as each record can be linked with multiple parent records.

Network data model example

Object-oriented model

This database model consists of a collection of objects, each with its own features and methods. This type of database model is also called the post-relational database model.

Object-oriented data model example

Image from Lucidchart

Entity-relationship model

Entity-relationship model, also known as ER model, represents entities and their relationships in a graphical format. An entity could be anything – a concept, a piece of data, or an object.

Entity-relationship data model example

Image from Lucidchart

Now that we have a basic understanding of data modeling, let’s see why it is important.

Importance of Data Modeling

  • A clear representation of data makes it easier to analyze the data properly. It provides a quick overview of the data which can then be used by the developers in varied applications.
  • Data modeling represents the data properly in a model. It rules out any chances of data redundancy and omission. This helps in clear analysis and processing.
  • Data modeling improves data quality and enables the concerned stakeholders to make data-driven decisions.

Since a lot of business processes depend on successful data modeling, it is necessary to adopt the right data modeling techniques for the best results.

Best Data Modeling Practices to Drive Your Key Business Decisions

Have a clear understanding of your end-goals and results

You will agree with us that the main goal behind data modeling is to equip your business and contribute to its functioning. As a data modeler, you can achieve this objective only when you know the needs of your enterprise correctly.

It is essential to make yourself familiar with the varied needs of your business so that you can prioritize and discard the data depending on the situation.

Key takeaway: Have a clear understanding of your organization’s requirements and organize your data properly.

Keep it sweet and simple and scale as you grow

Things will be sweet initially, but they can become complex in no time. This is why it is highly recommended to keep your data models small and simple, to begin with.

Once you are sure of your initial models in terms of accuracy, you can gradually introduce more datasets. This helps you in two ways. First, you are able to spot any inconsistencies in the initial stages. Second, you can eliminate them on the go.

Key takeaway: Keep your data models simple. The best data modeling practice here is to use a tool which can start small and scale up as needed.

Organize your data based on facts, dimensions, filters, and order

You can find answers to most business questions by organizing your data in terms of four elements – facts, dimensions, filters, and order.

Let’s understand this better with the help of an example. Let’s assume that you run four e-commerce stores in four different locations of the world. It is the year-end, and you want to analyze which e-commerce store made the most sales.

In such a scenario, you can organize your data over the last year. Facts will be the overall sales data of last 1 year, the dimensions will be store location, the filter will be last 12 months, and the order will be the top stores in decreasing order.

This way, you can organize all your data properly and position yourself to answer an array of business intelligence questions without breaking a sweat.

Key takeaway: It is highly recommended to organize your data properly using individual tables for facts and dimensions to enable quick analysis.

Keep as much as is needed

While you might be tempted to keep all the data with you, do not ever fall for this trap! Although storage is not a problem in this digital age, you might end up taking a toll over your machines’ performance.

More often than not, just a small yet useful amount of data is enough to answer all the business-related questions. Spending huge on hosting enormous data of data only leads to performance issues, sooner or later.

Key takeaway: Have a clear opinion on how much datasets you want to keep. Maintaining more than what is actually required wastes your data modeling, and leads to performance issues.

Keep crosschecking before continuing

Data modeling is a big project, especially when you are dealing with huge amounts of data. Thus, you need to be cautious enough. Keep checking your data model before continuing to the next step.

For example, if you need to choose a primary key to identify each record in the dataset properly, make sure that you are picking the right attribute. Product ID could be one such attribute. Thus, even if two counts match, their product ID can help you in distinguishing each record. Keep checking if you are on the right track. Are product IDs same too? In that aces, you will need to look for another dataset to establish the relationship.

Key takeaway: It is the best practice to maintain one-to-one or one-to-many relationships. The many-to-many relationship only introduces complexity in the system.

Let them evolve

Data models are never written in stone. As your business evolves, it is essential to customize your data modeling accordingly. Thus, it is essential that you keep them updating over time. The best practice here is to store your data models in as easy-to-manage repository such that you can make easy adjustments on the go.

Key takeaway: Data models become outdated quicker than you expect. It is necessary that you keep them updated from time to time.

The Wrap Up

Data modeling plays a crucial role in the growth of businesses, especially when you organizations to base your decisions on facts and figures. To achieve the varied business intelligence insights and goals, it is recommended to model your data correctly and use appropriate tools to ensure the simplicity of the system.

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Originally published September 9, 2019, updated Sep, 25 2019

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