The problem is that correlation is different from causation. Correlation is when two or more things or events tend to occur at about the same time and might be associated with each other, but aren’t necessarily connected by a cause/effect relationship.Dr. Wheeler, Carson-Newman University
Now, don’t go jumping up and down calling me every bad name in the book. It’s not your fault. Most businesses are getting their analytics wrong because they’re not:
More insightful analytics might track variances, such as the difference between expected and actual results. Using this analytics helps you make better predictions as well as focusing attention on why sizable differences occurred.
Adding color to your visualizations improves understanding and focuses attention on problem areas. For instance, P&G uses a data visualization in their delivery system. The visualization color codes receivers based on whether delivery is scheduled as on-time (green), possibly late (yellow to orange), or late (red) using GPS systems on trucks. Business size (as a function of their yearly order size) is represented by the size of their block in the visualization. Thus, if a big block, say for a business like WalMart, turns yellow, managers focus on improving delivery or diverting trucks destined for other buyers to fulfill WalMart’s needs on time. When a block is green, managers feel safe in ignoring that delivery to focus on those that are more in danger of failure.
Data visualization is only successful to the degree that it encodes information in a manner that our eyes can discern and our brains can understand. Getting this right is much more a science than an art, which we can only achieve by studying human perception. The goal is to translate abstract information into visual representations that can be easily, efficiently, accurately, and meaningfully decoded.
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