Revisited from the Foresaw archive. The argument and structure have been updated for this edition.
A business can have more data and still struggle to make a decision. The missing piece is often not another pipeline or dashboard. It is a clear relationship between the information collected and the action someone needs to take.
Before asking how to analyze everything, ask which decision should become better.
Name the decision and its owner
Choose a question with a real consequence: which orders need attention, where capacity should increase, or why a customer journey is failing. Identify the person who can act on the answer and how often the answer is needed.
This narrows the required data and gives the work a way to demonstrate value.
Agree on the meaning
A metric needs more than a name. Define its unit, time window, exclusions, and source. Make the assumptions available to the people using it.
When two teams disagree about a number, treat the difference as a question about definitions and provenance before treating it as a dashboard defect.
Build quality into the path
Check that expected records arrive, relationships remain valid, and important values stay within meaningful boundaries. Make failures visible to an owner who can resolve them.
Quality also includes freshness. A correct result delivered after the decision is made may be operationally useless.
Close the loop
Follow the analysis into the workflow. Did somebody act differently? Could they explain why? What evidence would show the decision was better?
Prediction and automation can extend this foundation, but they do not replace it. Shared definitions and traceable data make both analytics and AI easier to evaluate.
Adapted from “The Era of Big Data: Leveraging Data Analytics for Business Success.”
First published on Foresaw. Reworked for this edition.