How to Make Really Big Mistakes
Mistake One: Dashboard clutter. Don’t mistake using a dashboard with the idea of “cramming as much data on one page as is humanly possible for my executive to see.” Experienced managers of data know that the dashboard is your instrument panel that will assist you (or an executive) in making decisions based on the gauges and indicators on the dashboard. You’re designing your instrument panel for making decisions. Don’t clutter it up with data you won’t make a decision with.
Mistake Two: Right report, wrong cut of data. You can easily have the right columns and the right rows, but it is entirely possible to make a mistake when selecting what data to use in the report. For example, your executives want to see a real-time view of all open requisitions. However, when you run your report, you select all jobs opened within the month, not all jobs currently open as of the date of the report being run. The difference can be huge. You may have opened up only 10 jobs in March, but on March 30 there are 100 jobs open. While this example is pretty obvious, others are sinisterly difficult to detect. Think about the following cuts of the same data and what they tell you on a report of interview statuses. What is the difference when you select all interview statuses where:
Only jobs that were closed within the month are reported?
Only interview statuses that occurred in the month are reported?
Only jobs that were opened within the month are reported?
Only jobs that are open right now are reported?
Only the final statuses of candidates in closed jobs are reported?
If your head is spinning, you are not alone. All five choices are completely legitimate—and all five choices answer very different questions. Reporting interview statuses on all closed jobs will give you a clear historic picture of how candidates wind their way through the interview process, allowing you to see average time between important interview stages. Reporting on statuses that occurred in a month gives you a picture of what work a recruiter did during the month. A final status report will report on how your candidates looked at their last status, which could actually be your applicant flow log.
It’s the same report, but it has a very different meaning depending on what data you select. How do you avoid the problem? Before you run your data, make sure you know what you want the report to tell you. And then check your query to make sure you’re grabbing the right data!
Mistake Three: Not articulating a clear business problem that the metric will answer. Every element on your dashboard must have a relevant question that you can use to sum up why that metric is being run. Test it for yourself on your current metrics. If you can write down exactly what question the metric answers, then you have a clear metric with a clear goal. Example: “I want to monitor learning and development expenditures in real time so I can intervene before the month ends to make sure that we didn’t overspend on sales training and underspend on management training.” You can definitely build a metric around that articulated business question. At all costs, avoid reporting on a metric merely because you have it available!
Mistake Four: Trying to accomplish too many goals with one report. A surefire way to tie up several days of thought is to try to make one report do too many things. You will add columns, improve some data, and hurt other data. We’ve all been sucked down the quicksand of a reporting challenge—being drawn in further and further to fix the problem that the last addition to the report created until you wind up with a report that doesn’t make sense. If you feel you’re spinning your wheels, stop. Back up. Reapproach your original goal.
Mistake Five: Believing that your data is a complete picture of reality. Some of what you are going to report is not black and white data. Even finance has to make decisions about reporting that fall into grey areas; certainly Human Resources is in the same situation. You may need to make choices on how to represent your data that isn’t exactly how it happened in real life. For instance, HR runs an ad for 15 different positions in 9 different departments. If you run a department level cost analysis of recruiting expenses, you are likely going to need to make a decision on how to divide up those expenses. Even if you have a method for handling this, it is still an approximation of reality at the department level. As long as you can accurately describe your assumptions to anyone that needs to understand the data, you have done well.