Quality in Information Delivery Part 1:
Common Mistakes in Information Expression (Pie Charts)
Much of what is said and written about data quality tends to focus on the front end of the data-to-information life cycle. That life cycle can be described several ways. In the sketch below, we move through a number of steps.
Proper data expression leads to potentially useful information. Such data expression is achieved by transforming raw data from digital media into a form that humans can read and understand. The result is almost always a tabular and/or graphical expression.
While some human eyes and brains can easily grasp meaning in large tabular reports, many still prefer charts or graphs. To be useful, such expressions must not be misleading or vague. It does decision-makers no good to invest money in data quality improvement at the collection stage, only to have the resulting information expressed to decision-makers in a form that is misleading or incorrect. Data quality across an enterprise cannot be complete without addressing the quality of the expression of information.
This article is the first in a series that will highlight common mistakes made in the graphical representation of quantitative data. Each installment will focus on a particular type of graphical representation and propose a better approach for information delivery.