There are many applications for business intelligence and data analytics tools for enterprises today. Organizations that can leverage their data are going to be at a distinct advantage. But this isn’t going to happen if you without the right analytics foundation in place. Here’s how better data modeling leads to better business.
What Is Data Modeling?
Data modeling is an important, but often overlooked, part of the data analytics and business intelligence process. It’s a way of outlining and organizing the procedural and structural elements behind BI tools.
You might be familiar with business intelligence architecture, which — like any kind of architecture — is the underlying construction of a BI system. It helps to think of data modeling as a sort of blueprint for various architectures. These models can be applied to all kinds of applications, and help in several ways.
Why Is Data Modeling Important?
Let’s keep with the architecture-blueprint metaphor. It doesn’t make sense to try to build a skyscraper without a well-defined and -developed plan. You would start trying to build the thing and immediately run into problems. No one would know where to put all the pieces. And even if the structure were put together, it would be nearly impossible to diagnose issues down the line without that blueprint.
Data modeling works in the same way. This element must be present in order to glean value from data later. There are a few key areas that typically require data modeling:
- Physical models provide context on structural elements.
- Logical models deal with how data can be manipulated within an application.
- Conceptual models help portray how business assets are connected.
These three areas need to be accounted for in order to ensure any BI tools are functioning in the proper way.
How Can Data Modeling Improve Business Outcomes?
Ultimately, it’s important to know how data modeling is going to lead to better outcomes for your business. No one wants to invest time and resources into initiatives that fail to ultimately improve performance. Here, it makes sense to once again consider the massive difference between approaching things with and without data modeling.
Your business likely has a lot of data. And if it doesn’t right now, it will in the future once you’re been able to collect more of it. It’s essential to use data modeling in order to keep all that data from gumming up your analytics as things grow and evolve within your organization. Today, platforms like ThoughtSpot simplify the data modeling process so companies can start connecting users with data insights in a more streamlined fashion. For instance, ThoughtSpot offers employees a relational search tool that operates much like Google.
What happens when data modeling is done right?
First, it’s possible to get a greater amount of insights, faster, more efficiently, and from more sources. In the past, only data professionals were able to actually do analysis. Modern data modeling is leading the way to greater data democratization. This means all employees hold the potential for discovering relevant insights.
Furthermore, when data modeling is done the right way, it facilitates a far more efficient process. Analysts shouldn’t have to spend their time building reports for low-level queries that can be run by any employee. They should be working on more in-depth projects that require a deeper understanding of data. This is now becoming a reality, as analyst backlogs can be reduced with this kind of technology.
Every business is going to be slightly different on how they want to implement their BI and analytics tools. However, one thing is going to be constant across the board: It’s essential to keep things running smoothly with well-designed data modeling.