In Part 2 of my upcoming chapter on organizations and data modeling, I go into how politics and power influence things. We’ve all seen this, and while I’m not sure this chapter will entirely cover everyone’s circumstances, hopefully it will at least get people thinking about the impact politics and power in their work.
I’m on the road right now, but here’s the first section to kick things off. The chapter is mostly done, and I hope to post it - and free sections - very soon.
Thanks,
Joe
Data is Political.
You might have cringed reading that, but it’s true. The data modeling process is often marked by negotiation, influence, and outright conflict. Behind every metric is a potential turf war. Every entity might be a battle for naming rights. The decision of what constitutes the "source of truth" can become a zero-sum game where one department's win is another's loss.
Early in my career, I saw how data can be politicized. At one company, the CEO received glowing reports from every department: the sales close rate was phenomenal, retail was crushing it, and marketing couldn’t miss. Everything seemed amazing! There was just one problem. The numbers didn’t match the reality on the ground. The warehouse was overflowing with unsold inventory, a fact that directly contradicted the rosy pictures being painted. The CEO found it impossible to trust the data. The CEO, tired of the conflicting narratives, sat me outside his office as his “numbers guy” and tasked me with providing him with the correct data. It took a while, mainly because I had to gain the trust of the CEO and the various department heads who felt threatened by my position, but we got there. That experience showed me that data wasn’t just about numbers.
The lesson that stuck with me is that data is political. I saw firsthand how departments could shape numbers to make their performance look better than it was. It also taught me that data models are political artifacts. They reflect a shared understanding, but that "shared" understanding is often shaped by influence, competing interests, and unwritten rules. A poor model in a dysfunctional organization creates a downward spiral where trust erodes and bad decisions multiply.
In contrast, a supportive and constructive organization with psychological safety will likely provide you with numerous opportunities to properly model your data, which in turn gives people better data upon which to base decisions and act. This positive feedback loop is far more desirable. In high-trust organizations, data models become accelerators of alignment instead of weapons in turf wars. Trust creates clarity, and clarity reinforces trust.
I realized that to succeed, I couldn't just be the “numbers guy.” I had to become a Practitioner who could get the technicals right, a Salesperson who could convince stakeholders of the new reality, and a Servant who could mediate departmental disputes and gain trust. To navigate this landscape, you must also learn to operate in these three distinct, often overlapping, personas. You won't always be equally good at fitting these personas, so it might be best to work with people who can fill in where you’re a bit weaker, and you fill in where you’re stronger. But data modeling is a mix of practice, sales, and serving. Briefly, here are the requirements of these personas.
- The Practitioner is the data detective and the master builder, the technical expert comfortable working in the trenches. You understand the schemas in various systems, can reverse-engineer an arcane system, decipher poor-quality data, and build the data model under pressure.
- As a Salesperson, you’re the translator and diplomat. You translate complex technical work into compelling and tangible business value, convincing people why it matters. As you’ll see, this is not easy. You will need to be both an advocate and a negotiator.
- When you’re a Servant, you’re the empathetic facilitator. You understand that your primary job is to listen, build consensus, and mediate disputes over definitions to deliver timely, real-world value.
Think of these personas as roles you’ll switch in and out of, depending on the situation. Mastering them isn't just about building better data models. It's about creating the trust and alignment that turn data into a true organizational asset.