05 January 2021


4 Data Modeling Techniques to Drive Business Impact

Data modeling plays a vital role in the growth and overall success of many businesses. Since businesses generate enormous quantities of data—especially with the advent of technology like the Internet of Things, customer reviews, and chatbots—it’s critical that enterprises organize their data in ways that make structural sense, and are scalable and understandable so that they can be leveraged to drive real business impact.

Given the inexorable incoming tides of using data for machine learning and artificial intelligence, success with analytics projects heavily depend on well-built data models.

As with any large organizational project, it’s important for IT and the rest of the business to be aligned prior to actually designing and building a data model. The time-honored methods of ensuring that all teams strive to achieve a solid foundation of collaboration and coordination to steer key performance indicators (KPIs) are paramount.

Each group should have a deep understanding of the other’s goals, desires and drivers, in order to generate real business impact. This way, your data modeling is set up for success – no matter the area of mastery or skillset of those involved.

What Is a Data Model?

Simply put, a data model is a way of organizing disparate data elements while keeping track of how those elements relate to each other. A data model consists of entities—high-level buckets like products or customers—and attributes—details about those entities, like customer addresses or product descriptions.

Three flavors of data models

In general, at a high level, we can think of three categories of data models:

Another way to think about types of data models

In addition to the above (a more abstract way of thinking about the details that data includes in different kinds of models), we can also explore the models that businesses use based on what they want to accomplish.

4 Key Data Modeling Techniques

So how do you actually use these different kinds of models in your organization? Here are the top four techniques business analysts and data modelers use to help drive impact.

  1. Entity relationship diagrams show the relationships between the entities, and, in some cases, help to illustrate how business-related concepts interact. By looking at the relationships as a diagram, database modelers can develop a deeper understanding of the system.
  2. Development of a data dictionary, which provides information about the attributes of the data model, including names, definitions, and other elements that are captured in a database, can be used to help better understand where the data is coming from, what it looks like, and how it might be used.
  3. Data mapping, which can be thought of as a specialized version of a data dictionary, is used during a data migration project or when merging two disparate existing systems. In a data mapping scenario, data attributes are mapped across different data sets in a way that different labels can easily be merged.
  4. A glossary, which acts as the source of business truth to ensure that people are on the same page when discussing various data attributes and entities of the model. In general, a data glossary is owned by the business, not the IT-focused team. And it’s a good practice to have only one glossary, even if there is more than one data dictionary.

How to Prepare for Better Data Modeling

If you’re about to embark on a data modeling mission, here are some ways to get prepared.

Wrapping up

The underlying goal of building a data model is to make your organization more successful. With that in mind, it requires a deep understanding of key business objectives, as well as technical and database knowledge. Armed with that, your data modeling mission should achieve great success.

If you’d like help getting started with a new data project, sign up for a free AI Assessment—we can help you figure out what would have the most impact on your business.

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