What if the data isn’t perfect? This is how we work with incomplete or unstable information.

Working with imperfect data is more common than it seems. Sometimes, the data we receive isn’t as reliable as we’d like. Entries are missing, frequency is inconsistent, or the capture system is poorly defined. It happens more often than people admit—especially in industrial environments where digital systems coexist with Excel sheets, poorly calibrated sensors, or processes that were never designed to record information.

And while data quality is essential, the reality is that many times you have to work with what you’ve got.

Why does the data arrive like this?

It’s not always due to lack of effort. Some common causes we encounter:

• The process wasn’t designed to record data from the start.

• Multiple sources that aren’t synchronized with each other.

• Capture is manual or semi-automated and depends on individual habits.

• Previous migrations were done without standardization.

• The data exists, but it isn’t contextualized (Which machine? Which shift? With what configuration?).

In these cases, it’s not just about cleaning the data, but about understanding it, interpreting it, and making conscious decisions about its use.

And what do we do when we get a dataset like this?

We don’t discard it, but we don’t take it at face value either. Here are some of the strategies we apply:

1. Assess reliability before processing
   We analyze consistency, gaps, and anomalous patterns. We don’t assume everything is useful, nor that everything is useless.

2. Design proportional solutions
   Visualization with human interpretation, noise-resistant models, alerts with flexible thresholds… we aim for a balance between automation and judgment.

3. Look for patterns, not certainties
   When data is irregular, we focus on trends, repetitions, and relationships rather than on individual values.

4. Add external context
   A single data point may mean nothing, but if we know which process it belongs to, everything changes. We contextualize to understand.

5. Recommend improvements from day one
   Whenever we detect a structural data problem, we propose realistic actions to improve its capture, traceability, or standardization. Even if they’re not implemented right away, they remain on the table.

And when we can’t build with full confidence…

We address it with honesty, but also with perspective. If the available data doesn’t allow for reliable decision-making, we propose long-term solutions: new capture points, digitizing key parts of the process, or integrating systems that are still disconnected.

In the meantime, we help prioritize which decisions can move forward with some backing, and which should be reviewed more carefully. Because even when everything isn’t ready, you can still move forward with sound judgment.

Not everyone starts from the same point

Over the years, at WonderBits we’ve helped many companies that didn’t start from scratch, but from an existing system—built over time, with the effort and decisions that made their growth possible.

We know that digitalization is not always linear or orderly—especially in organizations with a long track record that have evolved according to their needs and priorities at each stage.

That’s why we don’t judge the data we receive: we see it as a reflection of a real business story, and from there we build solutions that respect that context while still pursuing continuous improvement.

In summary

At WonderBits, we always aim to work with reliable, well-structured data. But when that’s not possible, we apply technical criteria and common sense to extract value without compromising the analysis.

We don’t promise miracles—but we do deliver useful solutions, even in imperfect contexts.