A Midstream energy company we worked with had just finished rolling out Oracle's NetSuite. It covered all their basics. Financial reports, standard operations, the usual. But their business ran on knowing which options were most profitable for getting products to market, and they had a steady stream of external data coming in from customers and partners that needed to reconcile with what NetSuite was showing.

The natural fix? Spreadsheets. One analyst would pull the data. Then another. Then a third. By the time those three people sat back down together, each had their own model and their own logic for how the numbers fit together. Three smart people. Three completely different answers.

We're going to use their story throughout this blog, because what happened next has lessons for any business running on data, and at this point, that's all of them.

The Clarity Problem

When three people measure the same thing three different ways, you don't end up with three useful data points. You end up with nothing. If no one believes the number, you can't make decisions with it.

Technology won't solve this part for you. Before you build anything, someone has to decide what the right KPI actually is. What gets counted. What gets left out. And once you set that, you hold it steady. Every time you change the definition, you restart its history. The trend line disappears, and comparing this quarter to last year becomes meaningless.

You also have to decide who gets to build the KPI; it can't be 2 or 3 people, you must designate 1 person and let them set the path.

Case Study: At the company we worked with, that decision fell to the Controller. His solution to the three competing spreadsheets? Build his own model and ignore the others. Not wrong exactly, but it tells you how broken things had gotten. The real win wasn't that his model was better. It was that everyone finally knew whose answer was the authoritative one.

The Harmony Problem

When three people independently measure the same thing, they spend more time arguing about who's right than running the business. That's not a people problem. It's a systems problem.

Bias sneaks in without anyone meaning it to. The warehouse manager pulls the on-time shipment report and quietly excludes that one delivery because it wasn't like the others. The production analyst sees a strange spike in the data and writes it off as a broken sensor. Both calls feel reasonable in the moment. But every quiet exclusion is a judgment, and judgments compound when nobody's coordinating.

Case Study: When we stepped in to help, we brought everything back to zero. We defined the numbers from scratch, then brought the results to the whole group at once. Early meetings were messy, and people had real questions about accuracy. We would pull the details behind each number and walk through them one by one.

But then something shifted. The conversation stopped being about us entirely. The business subject matter experts started debating each other. Fringe cases got explored, and not everyone left the room happy. But the Controller made the final call. It got documented. The team committed, agreed or not. That's the change that actually matters. The conversation stopped being "is this number right?" and became "what do we do about it?"

The Speed Problem

Assembling a manual analysis three times over is expensive. Not just in time, but in how often you're willing to do it. When pulling the numbers is that painful, you only do it when there's no other choice. Decisions end up running on information that's weeks old.

In the Midstream world, six weeks is a long time for conditions to move.

Case Study: We built data pipelines directly into NetSuite, then delivered the output through a Power BI report. Same calculation, every time, for everyone. No manual pulls, no copy-paste, no waiting on someone to get back to you with their version.

After a short training session, the Accounting team could run it themselves. And instead of finding out what happened six weeks ago, they started finding out the next business day. They knew how the month was tracking before it was even over. That kind of visibility changes how a team makes decisions. Instead of reacting to the past, they could actually see around the corner.

What This Actually Built

Getting to one version of the truth is hard work, but not for the reasons people expect. The technology is the easy part. The hard part is getting a room of smart, opinionated people to agree on what the number is and who decides when it changes.

Once this client had that, something much bigger happened: they were acquired by a larger firm. Businesses with clean, reliable data are easier to buy and integrate. Due diligence moves faster, and the numbers hold up when someone is scrutinizing them from the outside.

If your team spends more time arguing about the data than acting on it, that's the problem worth addressing first. At FirstLight, we've helped companies work through exactly this, starting with how the numbers get defined and ending with a solution the whole team actually trusts.

Book a free assessment, and let's take a look at where your versions of the truth are getting in the way.

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