I want quick insights. I want a report. How hard can that be?
You’re probably familiar with this scenario. The data from a SaaS solution needs to be presented more effectively, or you’re simply looking for an easier way to gain insights from your data. You make the request, and weeks later, a report finally arrives.
In conversations with clients, I’ve noticed that our advice often goes beyond that. Instead of just the report, suddenly more things get added: a data warehouse, a data pipeline, various tools. And then I regularly hear the same response: “I don’t want a sports car—I want a scooter. Keep it simple.”
The offer that sounds too good to be true
That desire for speed makes sense. With an MCP integration or a data dump into Excel, you can now have immediate access to your data in an AI tool within fifteen minutes, and a report is generated. So why would you still invest in that sports car? Why not just use a connector or a manual data dump into Excel, supplemented by an AI model that does the rest?
The answer doesn’t lie in the speed of the first step. That often works just fine. It lies in what happens once you want to take it further.
Where It Gets Stuck
What an AI model builds based on a data dump is often a black box. You and your team have no insight into exactly what’s happening under the hood. And at some point, you hit a wall.
The model doesn’t understand what you mean, which business definition you’re using, or how certain data actually relates to each other. Or worse yet: the structure of the data dump changes, and your report breaks. The person who’s supposed to provide the dump forgets to do so, or is on vacation. Incomplete figures don’t suddenly become complete just because of AI.
Ultimately, it always comes down to the same thing: can you explain to your model what something means? What the correct definition is? How the data relates to one another, and what is and isn’t allowed? Without that foundation, you’ll be stuck manually tweaking and cleaning up a connector or a data dump.
Starting with a STEP isn’t the wrong choice
This doesn’t mean that starting with a “step” is wrong. A quick way to gain initial insights is often exactly what’s needed to prove that a data-driven approach delivers value.
But it’s good to know in advance that growth almost always requires taking the next step. Sooner or later, you’ll want to formalize things so that the entire organization sees and understands the same thing. That way, you can see what’s happening under the hood—or at least steer how the data is used.
You don’t have to start with a sports car—or even end up with one. But do expect that you may eventually need a car to really move forward.
Curious to know where your organization stands?
Do you recognize this tension between quick results and sustainable insights? I’d love to talk with you to figure out what the right next step is for your situation—whether that’s a quick scan or a deeper conversation about your data foundation.
