Capabilities / Analytics
Numbers the leadership team can act on.
A forecast, metric or model is useful when it informs a decision. The leadership team needs to understand what the number means, what it leaves out and how much uncertainty the decision can tolerate.
At Holland & Barrett, I built business intelligence, data science and engineering teams whose work supported forecasting, supply-chain decisions, purchasing and range planning. I bring that experience to examining the evidence behind leadership decisions.
Define what the number means
Agree the question, population, time period and limitations. Identify missing data and conflicting definitions before making a chart look convincing.
A clear, reproducible calculation is often more useful than a sophisticated model that nobody can challenge.
Match the method to the problem
Descriptive analysis, forecasting and machine learning have different costs and failure modes. Choose the simplest approach that supports the decision, and test it against a meaningful baseline.
Where the evidence is insufficient, a focused investigation can test whether the analysis is sound enough to support a decision. Agree what needs testing and when to stop before committing further time or budget.
Put the result to work
Agree who will act, what they can change and when the outcome will be reviewed. Distinguish activity measures from business results. If the analysis cannot be explained or reproduced, revisit it before relying on it.
The published analysis of Amazon bestsellers is one historical example of exploring structure in a large dataset.