Chapter 2 - What Is This All For?

Arguments for and against data modeling and why there's no free lunch.

This is an early draft of Chapter 2 of my upcoming book, Practical Data Modeling. I’m opening up the curtain, so to speak, and showing you the writing process of my book. Hopefully, you’ll learn something and maybe want to write a book too.

Chapter 2 covers some arguments against data modeling and why data modeling matters. This sets the context for future chapters in Part 1, where we cover the history of data modeling and Mixed Model Arts. If you’re wondering if this book will get technical, don’t worry. Part 2 will get super technical and nerdy, diving into various data modeling approaches across various use cases.

A note on early drafts:

Early drafts are meant to be a “good enough” stab at a topic. It’s not final, and things will change. As the old saying goes, the real act of writing starts in the editing and revision process. I don’t expect my early drafts to be perfect. Feel free to beat the crap out of them. This helps the book improve.

Thanks,

Joe Reis¹

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Have you ever wondered why we model data at all? This is a critical question that I feel is often glossed over. Regarding data modeling, I see a spectrum of motivations and practices.

On one end, data modeling is done thoughtfully and intentionally. Data modeling is treated as a serious craft, avoiding shortcuts. These people appreciate the power and benefits of proper data modeling.

In the middle, I often meet people who wish to model their data formally but don’t due to a lack of resources, time, or knowledge. As a result, they take shortcuts with data, piecing it together to get the job done, albeit not in an ideal way. This is a common approach to data handling today.

On the other end, some argue that data modeling wastes time. They see it as a costly, slow, and cumbersome process that hinders speed and rapid iteration. They believe that the focus should be on getting things done quickly, and the formalities of data modeling only get in the way.

Who’s right? Is there a right way to model data? Can neglecting formal data modeling be justified?

In light of these varied views, we will explore why the best approach to data modeling often depends on specific situational factors that we will examine in this chapter. A central theme of this book is “It Depends.” Every organization and situation is different. Blanket statements lack context and understanding of your unique challenges. We need to view the practice of data modeling as a spectrum of tradeoffs and be aware of what we’re gaining and losing across this spectrum.

So, what is this all for? In this chapter, we’ll look at the various arguments for and against data modeling. We will challenge these criticisms by exploring how intentional data modeling can be essential, even in environments prioritizing speed and flexibility. Let’s first examine whether data modeling is a waste of time.

Is Data Modeling a Waste of Time?

“Data modeling is a waste of time.” - Some people I meet.

You might ask yourself, why bother with data modeling? This is a fair question. In this case, I’m talking about intentional data modeling. As you’ll learn, data is intentionally or unintentionally modeled with varying degrees of outcomes.

Here are some arguments I hear about why data modeling is a waste of time.

“Data modeling is too much work.”
“Data modeling takes too much time.”
“We don’t have the resources or personnel for data modeling.”
“We’re a small company, and our business is too small to model.”
“We’re a startup moving too fast for a model to matter.”
“Our company is too big and complicated to model.”
“Data modeling is old and antiquated.”