The key components of Data Modeling
Of all the terms related to data and business intelligence, data modeling might be one of the more misunderstood concepts. This is because “modeling” can mean a lot of things. To understand data modeling, you have to look at the backend architecture of BI and data analytics platforms.
While some people might assume data modeling refers to visualizations and other representations of data, this isn’t really the case. Instead, data modeling is mostly about the relationship between various terms used to define how data is actually analyzed by programs and stored in databases, while also creating a single, continuous set of terms for users at all levels. This is accomplished at the very core of your enterprise BI, and should be designed and implemented by the top data experts within your organization.
It’s also important to know several different, but related, aspects go into building comprehensive data models. There are three main types of data modeling:
- Conceptual Data Modeling – This determines what kinds of data goes into the model, which is determined by what an organization is hoping to analyze.
- Logical Data Modeling – This is sort of a blueprint for how the data model should function.
- Physical Data Modeling – This pertains to the nuts and bolts of what will actually make the data model function properly and provide useful results.
All of these elements play a role in the overall implementation of data modeling, and all must be addressed in order to create trustworthy results. But the explanation of what something is doesn’t tell the full story of what it does. Let’s look closer at some of the key components of data modeling.
Why Is Data Modeling Important?
Now that you have a better grasp on what’s actually included in the term “data modeling,” it’s time to dig a bit deeper into why this is an essential consideration for enterprises looking to leverage their data. Here are some of the top reasons why data modeling is important for any enterprise looking to use BI analytics for decisions:
- You won’t be able to ensure accuracy otherwise – Probably the most critical aspect to data modeling is the fact that it fundamentally determines the accuracy of enterprise analytics. Without taking the time to construct reliable and thorough data models, the results of your BI queries might not be trustworthy. This isn’t just an annoyance, BI that isn’t accurate is more than useless; it’s a liability.
- You can potentially preserve otherwise squandered capital – Data modeling isn’t a costly thing to perform. It’s usually one of the smaller budget lines when building applications. However, despite being a smaller cost, it has the power to save tons of money. This applies during the design phase, where programming can be streamlined and improved through strong data modeling. But it also matters longer term, as applications built with better data models tend to be more useful. Furthermore, you don’t want to be troubleshooting issues after you’ve released an application, when you could have fixed it beforehand.
- Get everyone using the same vocabulary – When data modeling is done well, it can help everyone using BI applications understand how terms relate to each other. This will in turn yield more intuitive usability, which can help users perform tasks more efficiently and with greater precision.
- Better performance – Not all applications are the same when it comes to their analytics capabilities. But the same principles also apply when it comes to application performance. Sloppy data modeling can lead to procedural inefficiencies that will slow down database functionality. Data models need to be done right from the beginning in order to avoid these pratfalls.
As you can see, data modeling plays an important role in the overall data analytics framework. Once you understand the key components of data modeling, it’s clear why enterprises need to prioritize it when building applications or curating their BI suite.