Your data already looks good, so what’s all this talk of it getting dolled up just to show off? Well, data modeling isn’t a vapid or conceited process at all. In fact, it’s the opposite. Data modeling is about improving the usefulness and functionality of data used in BI applications by documenting and defining variable relationships. Let’s dive into a few things that can help bolster your understanding of data modeling.
What Is Data Modeling?
To really understand the purpose and benefits of data modeling, it makes sense to start at the very beginning: What is it? The purpose behind data modeling is to set parameters for how individuals and applications understand and interact with data.
Metadata is typically formatted in a standard way when you load it onto a platform. But just because something is automatically set up for you doesn’t mean it’s done in the best possible way. Data modeling is sort of like taking a step before the first step in your data analysis. While this might seem pedantic or unnecessary to some, taking the time to do this can actually vastly increase your team’s ability to leverage data.
How Can Data Modeling Improve Your BI Outcomes?
When thinking about how different tools, concepts, or processes can be implemented in your own organization, it’s essential to look at tangible improvements in outcomes. You’ll want to do this for any aspect of your business—as if something isn’t cost- and time-effective, chances are it’s not going to be a long-term best practice. So, how can data modeling improve your BI outcomes—but also operating performance in general? Here are a few of the top reasons for doing active data modeling:
- Make outcomes more accurate – When using data to make decisions, accuracy is just about the most important consideration. If you’re not getting precise results, you might actually be doing more harm than good, as you can make big moves based on poor information. With data modeling, you can set standardized terms so that users know how to frame queries to receive accurate feedback.
- Less time required to integrate and maintain systems – There’s some front-end work required in the data modeling process. Doing this, however, can actually save your teams tons of time and effort over the long run. Once you have done a thorough job of data modeling, you should only have to make minor adjustments from time to time. On the other hand, if you don’t do this, you’ll be starting from scratch every time you adopt new tools or need to update integrated systems.
- Easier to see when things aren’t quite right – As already mentioned the accuracy of your data analyses is critical to actually benefiting from it. Regardless of how good your platforms are, you’re occasionally going to run into situations where results aren’t exactly what you needed. When data modeling has been done effectively, it can be much easier to spot what went wrong and rectify the issue.
- Makes self-service BI much more achievable – You probably know self-service BI is one of the biggest trends in analytics today. This is because self-service tools, such as relational search, make it so more people can use data to derive actionable insights. Data modeling is an important part of this process, as it makes using tools much more intuitive for those without deep expertise.
- Everyone uses the same vocabulary – Many organizations find themselves in a situation where people on the data team use one set of terms to describe things, which are in turn entirely different from those used by other departments. Data modeling can help everyone get on the same page by creating a standardized vocabulary. This helps eliminate unnecessary confusion during communication.
No matter the focus of your organization, data modeling is an important consideration. While its utility isn’t always obvious at first, taking the time to do this well can vastly improve the end results of analytics.