The Fundamentals Of Data Modeling

The Fundamentals Of Data Modeling

Data modeling can be confusing to many people. Those who aren’t familiar with the intricacies of data analysis and its interrelated parts might not know how data modeling differs from other terms.

To that end, here are some of the fundamentals of data modeling.

What Is Data Modeling And Why Does It Matter?

While the ultimate goal of data modeling is better visualization, many steps go into that process. Every organization has different needs for its data. Similarly, no two enterprises will have the exact same data sets, or even kinds of data.

Due to the inherently abstract nature of data, its modeling needs to provide a framework for how organizations collect, sort, interpret, and present this information. Enterprises will want to continuously evaluate and evolve their data modeling framework along with their operations.

Here are a few of the top reasons this is important:

Better applications and results:

The end doesn’t always justify the means. But it certainly does when it comes to data modeling. Infusing modeling into the greater scope of analytics, your organization will likely end up with better-functioning applications and outcomes. This includes being faster, having better documentation capabilities, and making fewer mistakes. All of these elements inherent to data modeling will improve business operations.

Lower overall expenditure:

It can be tough to convince people of the value of spending money on something when it’s a complex subject — or the benefits aren’t immediately clear. You might struggle a bit explaining data modeling to executives, but they certainly won’t have trouble understanding the idea of saving money. While data modeling is a line in the budget, it’s relatively small, and can go a long way to limiting other expenses, such as programing and maintenance.

Greater focus:

A big point of data modeling is refining the functioning of the way data is applied to business applications. Everyone working within an organization has his or her own area of expertise. Data experts know how to work with information, but don’t have extensive knowledge in other areas of the organization. Data modeling combines the expertise from a variety of players in order to create a process that does a better job solving problems.

Now that you’ve seen reasons for incorporating data modeling into your business’s standard operating practices, it’s time to determine how to do this.

Determine Organizational Data Modeling Needs

Data modeling is clearly an essential part of working with business intelligence tools and data sets. But how should your organization begin thinking about data modeling if it’s something you’ve never considered in the past? While there are certainly options for doing the work in-house, this can take time to implement if you need to hire new people, change protocols, and reach a consensus about the scope of data modeling requirements.

Working with an organization that can help you better understand the data modeling process, as well as equip your business with the right tools could be the optimal solution. Companies such as ThoughtSpot provide tools enterprises need to build a more robust analytics program. They offer a variety of artificial intelligence-enhanced applications that can bring your organization to the next level. Moreover, their custom-built data architectures make it faster and easier for anyone on the team to get actionable insights.

With that said, while there are several different ways data modeling can be accomplished and incorporated into business applications, some options will be wrong for your particular organization. Through understanding the underlying needs of the enterprise, you can get a better idea of how your data modeling structure should be conceptualized and built.