Data & Predictive Intelligence

No. Reporting answers what happened and, at best, why. Predictive intelligence adds two more questions: what is likely to happen next, and what to do about it. Both run on the same foundation, because a model learns from the figures your reporting produces. The techniques do differ, since a dashboard aggregates what was recorded while a trained model infers what tends to follow, but the dependency runs one way: unreliable reporting produces unreliable forecasts.

Both, though not in parallel. Pick one decision your team makes repeatedly and expensively, connect only the sources that decision needs, and put a model behind it. Run it alongside the current process for a full cycle before letting it decide anything, then compare. Widen from there until the unified platform is in place, each new source earning its way in by serving a decision. Sequencing it this way keeps the platform work funded by results it has already produced.

It depends on the model. Anything that estimates an outcome, such as demand, customer lifetime value or churn risk, needs history in which that outcome is known. Anomaly detection does not, because it looks for deviations no one labeled in advance. Two conditions always hold. Definitions must agree across systems, or the model will faithfully predict the inconsistency. And figures must be traceable, so you can show where a number came from and on what lawful basis personal data was processed. That is why data governance sits in the Predictive Intelligence Suite from W69 as a component rather than an afterthought.

Judge the decision, not the score. Record which choices came out differently once the model went live and compare the results against a period or group still running the old method, such as inventory left unsold or accounts saved before churn risk peaked. When you do test against history, hold back the most recent period and train on what came before it. A random sample of past records lets later information reach the training data, and the accuracy you measure comes out flattering rather than honest.

Building the model before assigning the decision. The dashboard goes live, the forecast holds up, and nothing changes because no one agreed which threshold triggers which action or who is allowed to take it. The second mistake is presenting an estimate as a single firm number. Every forecast carries a range and a period it applies to. Strip those away and the business reads the output as a promise, then stops trusting the system the first time reality lands outside it.