Why You Need Data Modeling

Date: 2022-10-27 | Time of reading: 6 minutes (1123 words)

Why You Need Data Modeling

Business cannot exist without data. Companies plan, analyze and solve their problems using data. Information is collected from everywhere at each stage of the organization's activities. Modeling is needed to make the most of the collected data.

What is data modeling

Data modeling is a method of compiling and structuring information. You don't need special technical skills to do data modeling. All you need is logic and understanding of business processes.

What are the benefits of modeling

Here are some of the challenges data modeling can help you solve in business:

  • Organizes business processes;
  • Provides storage, systematization of information and make it easier to find the information you need;
  • Sorts extra data and fills in gaps.

Proper organization of data ultimately leads to effective management decisions.

How to model data

Start by building a conceptual model, then create a logical model based on it, and finally a physical one.

Conceptual data modeling

This is a visual representation of business processes and the relationship between them. This stage does not require software; it is enough to draw a model by hand.

Logical data modeling

You create a conceptual model in the first step, and then you can convert it into a logical data model. To do this, add key and non-key attributes to the entities. And also you can show the relationships between primary and foreign keys. The result is an independent model that is compatible with all databases.

Physical data modeling

The entities are transformed into tables; the attributes are transformed into columns. The names of the table and columns within it are adapted for compatibility with databases.

For ordinary users, the physical model is difficult to understand. Use the logical concept for dialogue with users, and use the physical one for in-depth analysis.

Data Modeling Approaches

Hierarchical data model

The simplest model was presented by IBM in 1996. In the approach, much attention is paid to the hierarchy based on the principle of a tree structure. The «parent» entity is located at the top, «children» goes down from it.

Information Management System is great for taking inventory. In a clothing store, at the top of the model is «clothing», its «children» can be «male» and «female». The decomposition separates women's wardrobe items (tops, dresses, skirts) from men's wardrobe items (trousers, shirts, jackets).

Hierarchical data model

Relational data model

This model was invented by Edgar Codd in 1970. Edgar, an IBM employee and computer scientist, based the model on an intuitive way to create tables. The data is organized in columns and rows, each cell is in its place. This method is still used effectively in the field of enterprise computing. Specialists use this method in the field of enterprise computing. Using relational models, it is convenient to track the number of goods in stock, store information about customers and conduct transactions in e-commerce.

ER data models

This model stands for «entity-relationship». Within the model, entities are connected using relationships.

For example, when a user places an order in an online store, the order is assigned a unique code. This is an entity. Further, many events can happen with this order: the client can add or remove items, change the quantity, or even make a return. All these actions are the relationships to the entity.


The Object Oriented Data Model (OODM) maps any complex scenarios. A closer look highlights the obvious and not so obvious business problems. OODM has proven to be great for funnel analysis. A potential client acts as an object with its own attributes (name, email, phone, etc.). As long as the lead has not turned into a client, there are two objects: a «potential client» and a «client» itself. The «client» has all the same attributes as the «potential client», but with additions: ID, addresses and payment details.

OODM is designed to build relationships around a real-world object. When specialists build such a model, they rely not only on logic and mathematics, but also on the actual situation.

Dimensional data model

When there is too much information, it is divided into data of the first, second and subsequent orders. In such a structure, it is very difficult to quickly find the required solution. Dimensional models are used to optimize databases and make it easier to find the information you need.

Dimensional data model

It is recommended to use OLAP (online analytical processing) for trend analysis, sales forecasting, financial reporting, etc.

How to build an efficient data model

  1. Set a clear, measurable goal. The goal should work for the result. Imagine what effect you want to achieve through model building.
  2. Strive for simplicity. This advice applies to both the initial stage of working with the model and subsequent ones. At the very beginning, do not try to make the model complex and confusing. Get maximum clarity. In the future, you will fill it with the necessary information without making it difficult to understand.
  3. Organize your data based on facts, dimensions, filters, and order. Let's say a business owner has over a hundred stores. The task is to find the point that is the most effective in terms of sales of product N over the past year. "Fact" is the sum of sales of each specific store. "Dimensions" are the product N that the owner is interested in, and the location of the store. "Filter" sets the time interval (year), and "order" determines the Top 10 stores according to the specified parameters.
  4. Avoid mistakes. This is especially important when it comes to a large-scale project. If you skip a mistake to the next stage, there is a chance that at the very end of the project you will have to return to the roots. To avoid such situations, double-check.

Data modeling tools

There is a wide range of data modeling tools on the market. In this post, we list a few of them. When choosing a tool, rely on the needs of your own business and pay attention to the following points:

  • How clear is the organization of work with large amounts of data?
  • Is there a visualization functionality?
  • Can this tool streamline data mapping through the enterprise?

Here are some options:

  • Erwin Data Modeler is a tool from Quest. Suitable for designing and visualizing data and compiling detailed reports.
  • Enterprise Architect does a great job of visualizing business processes.
  • ER/Studio is a service for working with complex data; you can build dimensional and relational models in it.
  • Altcraft Platform is a marketing automation platform that combines data from various sources into a single profile. Analytics works in real time, visualization is achieved through charts and reports. The advantage of the tool is the speed of processing large amounts of information.

Data modeling in Altcraft PlatformData modeling in Altcraft Platform







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Author: Polina Zakaryan






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