For years, many companies have invested time and effort in capturing data: sales, costs, schedules, incidents, projects, deliveries, and hours worked.
But in most cases, that data is used only to describe what already happened.
Reports, dashboards, and summaries are useful, but they all look backward.
They answer questions such as:
- How many projects were delivered late?
- How much did each one cost?
- Which client was the most profitable?
That descriptive approach is necessary — but it has a clear limit: it only explains the past.
The past is valuable, but business happens in the future.
The Value Leap
The next step in a company’s analytical maturity is moving from descriptive to predictive analysis.
It means looking beyond the rearview mirror to anticipate what’s coming ahead.
Predictive analysis isn’t about guessing — it’s about identifying patterns and probabilities from the stories the data is already telling.
It’s worth remembering the key insight from The Black Swan by Nassim Nicholas Taleb: the mistake isn’t in trying to predict the unpredictable, but in believing that we can. What we can do is detect patterns that carry a certain degree of probability.
Predictive models allow companies to ask more powerful questions:
- Which projects are most likely to run late?
- Which clients might not renew?
- What combination of factors increases profitability?
Three Levels of Analytical Maturity
- Descriptive: shows what happened.
- Diagnostic: explains why it happened.
- Predictive: estimates what might happen next.
Some organizations even reach a fourth level — Prescriptive — where models not only predict, but also recommend specific actions to improve outcomes.
(That’s a topic for another post. From what I’ve studied, reaching that level requires more time, more data, and more experimentation.)
What It Takes to Move Forward
Transitioning from descriptive to predictive analytics doesn’t start with new software — it starts with a new mindset.
It requires:
- Consistent, accessible data (not scattered spreadsheets and disconnected files).
- Operational context: understanding what the data really represents — this is where deep business knowledge matters.
- Collaboration between those who manage data and those who make decisions — a role that’s becoming more central in truly data-driven companies.
- Curiosity: the willingness to ask different questions, because doing the same thing only leads to the same results. And, as we know, the only constant is change.
Data analysis technology only amplifies what a company already understands about itself.
A Practical Example
Imagine a company that records project costs and timelines.
Today, it uses that data to evaluate past performance.
But with the same history, it could build a model that anticipates:
- when a project is at risk of delay,
- which type of client tends to cause bottlenecks,
- or what recurring patterns precede a drop in profitability.
It doesn’t need more data — only a better way to use the data it already has.
From Data to Behavior
Predictive analytics doesn’t replace human experience — it complements it.
It allows intuition to rest on evidence.
And with each learning cycle, both the model and the organization become sharper and more precise.
In the end, it’s not about predicting the future — it’s about recognizing it before it happens.
At Dagaa, we help companies make this transition — from describing what happened to anticipating what will.
Not by collecting more data, but by uncovering the meaning within the data they already have.
That’s where strategy meets insight — and where the future quietly begins to take shape.