Rabbits have a natural tendency to dig holes to provide a hiding place or to keep cool in hot weather or warm on chilly days. Many homeowners get extremely frustrated when trying to find the culprit or when trying to prevent this natural burrowing behavior.
How does this apply to data analysis? A rabbit will dig many holes, but will only be in one, making the other holes less relevant. Businesses generate lots of data, but only some of it is relevant and has value to be extracted from it, while the rest is “noise.”
Business owners and executives are faced with too much data and therefore often focus on the data that shows bad news. Since they assume the data is as accurate as possible, they might spot a single line in a report that impacts a minuscule portion of the overall data and then request research on the causes. This is a rabbit hole. An analyst will dig through huge data sets for days, only to find that the line in the report that triggered his research was the result of a mistake that was corrected ten minutes after it was discovered by the development or marketing team.
So as analysts, we need to do our best to focus our efforts on digging in the right place. This is a key skill every analyst or consultant must develop or they will spend countless hours researching deep dark areas of the “so what” or “nice to have” data points that don’t add much value.
Analysts have the ability to steer the business in the right direction by using the right, relevant data. The analyst needs to show the value of the data as well as communicate the insignificance of the noise (empty rabbit hole). The business owner must feel confident in making data driven decisions, even when there are anomalies within the data set. The analyst’s role is to show that if the data is 70% “clean” then it is absolutely good enough to make business decisions. Without setting these expectations, the requests for researching these insignificant data points will multiply… like rabbits.
How do you identify a rabbit hole?
- If the data is statistically insignificant… it might be a rabbit hole
- If the data only appeared or spiked for a brief time period… it might be a rabbit hole
- If the data is coming from a single source… it might be a rabbit hole
- If the data structure is inconsistent (case sensitivity or misspelling)… it might be a rabbit hole
- If the trend is decreasing over time… it might be a rabbit hole
- If it cannot be repeated… it might be a rabbit hole
Of course we could spend hours arguing each bullet and adding other scenarios, but how much time do we want to spend “wrapped around this axle,” “beating this dead horse” and stuck in “analysis paralysis.” If there is anything that will kill an analyst’s motivation, it is being overloaded with research requests that offer little value. And how much time and money should be spent going down a rabbit hole only to uncover that a server went down in Albuquerque for 14 minutes at 3:24am EST on November 16, 2015? What decision can be made from this discovery?
Stop wasting time. With SEO/SEM projects, display or email marketing efforts, conversion optimizations, ROI analysis, new revenue stream identification and third-party tool evaluations, there is really no time to spend in a rabbit hole. There are future contracts and resource allocations that depend on good analysis to drive business decisions that lead to jobs, expansion, and revenue growth. Analysts will leverage their experience, set expectations, focus on the relevant data, be able to extract the value, drive data-based decision making, and ultimately take marketing programs to a much higher level.
Now that you know what you know, put it to use, keep it simple and Stop Making Analytics So Hard.
We can help you optimize the way in which you utilize your analysts and get them refocused on what matters to your bottom line. We can also offer technical assistance by showing you the best methodologies on pulling data, creating dashboards, and getting the right info to the right decision makers’ hands. Contact us to speak with a data analytics experts.