Data Overwhelm? Get Unstuck

A practical way to get your bearings with data from multiple systems

You start a new role and inherit a set of dashboards you didn’t build. Or you’re running a small business that’s finally growing, and you realize gut instinct isn’t enough to make the next decision. Or perhaps you’re looking at new systems, sitting through demos and trying to imagine what the data would look like once it’s actually yours. Different situations, same feeling: suddenly there’s a lot of data on the table, and an unspoken expectation that you should be able to make sense of it.

In those moments, the data itself isn’t the hardest part. It’s the space between knowing the business and knowing the systems: recognizing familiar metrics, but not trusting the numbers or being clear on what they stand for; feeling like you should have some answers while still trying to understand what you’re even looking at. And particularly in small businesses, it’s a space you’re in without the staff or time to reconcile everything. It’s where many people get stuck.

And when you’re there, there can be a temptation to start analyzing just to have something to say. Any answer can feel better than no answer. But that’s like moving spontaneously through unfamiliar terrain just because you have a map in your hand. Before you head off in any direction, you stop and orient yourself. You figure out where you actually are, what landmarks you can trust, and which tools will help you make sense of the surroundings. Working with data is no different. You need to get your bearings.

When you have data but not a clear answer

Consider an established HVAC service business serving a core geographic area. Demand is steady, crews are close to capacity, and leadership is starting to ask whether it makes sense to expand service into neighboring ZIP codes or towns. On the surface, it sounds like a simple data question: Do the numbers support expanding our service crews?

The business has data, but it’s scattered across systems built for different purposes. Scheduling and dispatch data documents where crews are already going and how long jobs take. Billing data shows revenue by job and customer. Customer records reflect repeat service and maintenance plans. Lead intake data captures calls and requests, including inquiries from areas they don’t currently serve. Website data may hint at interest beyond the core territory.

Some of these systems will overlap. The same customer, job, or dollar amount may show up in more than one place. That’s not a flaw. It’s a clue. Overlap usually means the data is being used to answer different questions, from different angles.

Taken together, these datasets don’t add up to a single answer. Before modeling or projecting anything, the business needs to get oriented. Which data speaks to demand? Which reflects capacity? Which helps frame financial risk?

At this point, the goal isn’t to find the answer yet. It’s to understand what each dataset is actually good for. After that, you can move forward without guessing.

Build your data map

When you’re surrounded by data from multiple systems, the most useful thing you can do is stop treating it as one big pile. Instead, create a simple map that connects different types of data, usually contained in different systems, to the kinds of questions they’re designed to answer. This isn’t documentation or cleanup. You’re just trying to make the landscape visible.

Start by listing the systems and the data from those systems that you have. For each one, answer three basic questions:

• What kinds of questions was this system built to answer?

• What does it do particularly well?

• What should it not be used for?

For our growing HVAC service business, that might look something like this:

• Operational systems (scheduling, dispatch, job tracking)
  Show capacity and how crews spend their time, not unmet demand.

• Financial systems (billing, accounting, invoicing)
  Show revenue and margins, not why demand exists or where it’s coming from.

• Customer systems (service history, maintenance plans)
  Show repeat behavior over time, not early interest or acquisition.

• Lead intake systems (calls, forms, service requests)
  Show demand signals, not completed work or guaranteed revenue.

• Digital behavior data (website activity, service pages)
  Show awareness and interest, not operational or financial reality.

One metric, multiple locations (and definitions)

Sometimes the same activity shows up in more than one place. A service call appears in the scheduling system, in the billing records, and in the customer history, each time with slightly different context. A lead might show up as a missed call, a website request, or a note in a customer record, depending on where it entered the system. The overlap isn’t accidental. Each system is capturing the same moment from a different angle.

The goal here isn’t to decide which system is “right.” It’s to understand what role each one plays. Once you do that, overlapping data stops feeling like a contradiction and becomes context: different views of the same business, answering different questions.

Create a small shared vocabulary

Once you’ve outlined your data sources and mapped those to the kinds of questions they answer, it helps to pause and agree on how you’ll describe what you’re seeing. With data coming from multiple systems, alignment with your team matters.

Pick a short list of terms that keep coming up and agree on what you mean by them. For example:

• What counts as a customer?

• What do you mean when you say demand?

• What does revenue include? What’s not included?

• When you talk about a job, lead, or request, where does it show up first?

These don’t need to be perfect or permanent. They just need to be clear enough to keep conversations moving without constant clarification.

Once you’re oriented, take the next step

For our HVAC business, being oriented means they know what data they have in their landscape and can make intentional choices about which systems to rely on in different situations.

When considering expansion of their service crew, they know which systems they look to for capacity and scheduling lead times, which ones they use to understand demand, and which ones they trust when evaluating financial risk.

They understand what the data means, and what it doesn’t. So when lead counts don’t match completed jobs, that difference isn’t treated as an error to fix. It’s expected. Lead data is used to gauge interest and potential demand; completed jobs are used to understand what the operation can actually deliver. Each number has a role.

One last note: once you have your bearings, the next step isn’t to answer everything at once. It’s to choose where to focus; to choose your next destination.

Understanding the landscape gives you a way to be deliberate. You can decide which question matters most right now, and which view of the data is most appropriate for that question. And instead of reacting to every number on the page, you prioritize the signals that fit the question and let the rest stay in the background.

Then, when you are ready, you choose the next question or destination, and map how you will get there. Orienting with your data soon turns into forward motion step by step.