I’m not sure how a term from construction and dentistry became so ubiquitous in the world of data analysis. Perhaps because when you are “drilling down” into data, you are going deeper. Drilling down means viewing data at increasing levels of detail. (By the way, the word for viewing data at decreasing levels of detail is called “rolling-up”, a culinary term?)
Applications such as Tableau and Qlik Sense allow you to create interactive data visualizations, which means users can use filters to drill down into the data. If, for example, you see an overall downward trend in program participation, you might want to see if the trend holds for subgroups of participants such as women, men, or those in certain age groups.
Why is drilling down important? Because it helps you to identify both strengths to build on and problems to address. An overall upward trend hides problems in subgroups. Perhaps participants in a certain age group are not doing as well as others in a substance abuse program. Conversely, overall negative results hide positive findings. For example, although on average the wages of participants in an employment program have gone down, they may have increased for a subgroup who entered the program after a certain date.
If, after using various filters, it appears that results vary significantly across a certain type of category, you might want to create several small visuals and place them side by side to more easily make comparisons among subgroups (for more on this, see Tip #25).
Bottom line: Don’t only look at the forest. Check out groups of trees to get the whole story.
See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data.