If you're looking at the data your organization generates and you feel like you're staring down a tsunami, you're not alone.
Decades of software platforms generating disparate data sets leaves organizations with no lack of information. Add to this the falling cost of storage, and your problem is no longer deciding what to keep—it’s figuring out what the heck to do with it.
You might even feel like your data belongs on an episode of Hoarders.
And here’s the kicker: the cost of ambiguity is real. The longer your data sits scattered, unclear, or underutilized, the harder it is to define ROI on the systems and people managing it. That means your messy data could also be costing you.
The good news is: You don’t have to tackle the whole mess at once.
Whenever big changes are on the horizon the fastest way to get buy-in across the organization is to start with a fast finish in mind: Go after the low-hanging fruit.
The trick with picking the fruit is identifying, not only the pain point that matters, but something you can quickly solve. Here are a few questions that help identify that sweet, sweet fruit:
What data are we already using—just not efficiently? (If analysts are still pulling the same monthly report manually, that’s a clue.)
Where are mistakes costing us the most? (If forecasting errors are consistently leading to missed opportunities, that’s ripe for automation or cleanup.)
What decisions are leaders making again and again that could be data-driven? (If executives are stuck in “gut feel” mode, a dashboard could change the game.)
Where are people already complaining? (Pain points are often the easiest wins when you clean up the underlying data.)
What manual processes could be automated—and then transformed into actionable data? (For example, service tickets or expense approvals that not only save time but also create a clean dataset to analyze trends.)
Starting here builds momentum. People see immediate wins. Leaders get clarity. And suddenly, the “data tsunami” starts to feel like something you can ride rather than drown in.