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For most of us, making data-driven business decisions is a four-step process. First, you collect the data. Next, you mine it, which just means some combination of tools and data scientists look for patterns and correlations between different kinds of data. Third, those discoveries get pumped into the dashboards and visualizations that managers get to see. From there, it s up to the manager to interpret what the dashboard is telling them and make their decision.
The problem there is that the data you ve collected, and the patterns your tools and data scientists have discovered, now define the decisions you can make. A simplified example: Say PCMag collects loads of data on which articles have performed the best in terms of how many clicks a particular article or group of articles has received. Then our database engines rumble to life, groups the best articles together, and builds pretty visualizations so we can understand what was found. What we re looking at lets