Analytics is a funny discipline. On one hand, we deal with idealized models of how the world works. On the other hand, we are constantly tripped up by pesky things like the real world. One of these sneaky hard things is how best to count up people at various points in time, particularly when they are liable to move around.
Within people analytics you run into this problem all the time. In other areas, it isn’t as big of a deal. Outside of working hours (sometimes maybe during working hours) I run into this when I’m in the middle of a spreadsheet full of NBA players. I’m about to create an amazing calculation when I realize that I haven’t taken player trades into consideration. George Hill, for example, starts the season in Sacramento but ends it in Cleveland.
How do you handle that? Extra column? Extra row? What if he had gotten traded again? Two extra columns? Ugh! My spreadsheet is ruined!
Fortunately, One Model is set up for this sort of point in time metric. Just tell us George Hill’s effective and end dates and the corresponding metrics will be handled automatically. Given the data below, One Model would place him in Start of Period (SOP) Headcount for Sacramento and End of Period (EOP) Headcount for Cleveland.
Along the way we could tally up the trade events. In this scenario, Sacramento records an outbound trade of Hill and Cleveland tallies an inbound trade. The trade itself would be a cumulative metric. You could ask, “How many inbound trades did Cleveland make in February?” and add them all up. Answer-- they made about a billion of them.
Putting it all together, we can say that Hill counts in Cleveland’s headcount at any point in time after Feb 7. (Over that span of time Cleveland accumulated 4 new players through trades.)
So the good news is that this is easy to manage in One Model.
|
Team |
Effective Date |
End Date |
|
Sacramento |
2017-07-10 |
2018-02-07 |
|
Cleveland |
2018-02-08 |
-- |
The bad news is that you might not be used to looking at data this way. Generally speaking, people are pretty comfortable with cumulative metrics (How many hires did we make in January?) and they are pretty comfortable with the current point in time (How many people are in my organization). Being able to dip into any particular point in time is new. You might not have run into many point in time scenarios before-- or you might have run into versions that you could work around. There is no hiding from them in people analytics. They are essential.
Never fear. We’ve got a video below walking you through some examples.
If you think this point in time stuff is pretty cool, then grab a cup of coffee and check out our previous post on the Recruiting Cholesterol graph. There we take a more intense look at point in time calculations.
Also, if you looked at the data above and immediately became concerned about the fact that Hill was traded sometime during the day on the 8th of February and whether his last day in Sacramento should be listed as the 7th or the 8th-- then please refer to the One Model career page. You’ll fit right in with Jamie :).
About One Model:
One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own. Its newest tool, One AI, integrates cutting-edge machine learning capabilities into its current platform, equipping HR professionals with readily-accessible, unparalleled insights from their people analytics data.
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