SUCCESSFUL
PEOPLE
ANALYTICS
Y
ou come to a fork in the road. The sign indicates
100 travelers have taken the left fork and 14 have
fallen to their death; it also shows that 50 travelers
have taken the right fork and 8 have fallen to their
death. Which road do you take?
Welcome to the world of HR analytics.
The answer to this “which path” puzzle is one you probably won’t
learn in a statistics class, and it demonstrates something HR
leaders need to know: the road to analytics success isn’t always
paved with data scientists.
Now, back to the decision: which path? The natural reaction
is to take the path with a lower percentage of deaths. But wait!
You might (correctly) suspect that the difference is not statistically
signifi cant. 'oes this mean it doesn’t matter which way you go? 1o,
it still matters, and the reasoning behind the answer will solve many
problems in people analytics.
The typical HR professional should be learning methods to
enhance decision clarity.
WHAT QUESTION ARE WE TRYING TO ANSWER?
If you were asking the question, “Is there strong proof that one
path is safer than the other?” then the answer would be “no”. But
that’s not the question is it? The real question is, “Which path
should we chose, given that we have to pick one?” In this case,
we can only look at the best available evidence: the percentage
of deaths. We should take the path with fewer deaths per traveler
even though the evidence we have is weak.
If other evidence about path safety comes along, or if one
path is more costly than the other, then we may change our
decision. In the absence of that additional evidence we still
have to forge ahead. We don’t need a calculation of statistical
signifi cance to make our choice.
This simple story encapsulates four important elements of
successful people analytics:1. We need to be clear about what question we are trying to answer.
2. We need to gather the best available evidence—which, even if it
not good, will be better than no evidence.
3. We need to assess the quality of the evidence so we can make
an informed judgment.
4. Often, basic math is all we need to inform our judgment.Of these points, it is the fi rst one that matters most. We don’t do
analytics as a textbook exercise, we do it to make a business deci-
sion. When we are clear about the decision, then the rest follows.WHAT TO DO? In my analytics workshops, HR professionals
are often relieved that I’m not teaching statistics. There is a role
for statistics, but that’s not what the average HR person needs to
learn. The typical HR professional should be learning methods
to enhance decision clarity—i.e., be trained in asking the right
questions. That’s the single biggest driver of analytics success.
Secondly, they should be trained to use the basic math skills
they already have. We can go a long way to better decision making
with counts, percentages and estimates; people need to recognize
the value of this basic math.
Finally, they need to understand when to call on extra skills
sets for problems that can’t be answered with simple forms of evi-
dence—and this is where we do want unleash the data scientists.
HR will fi nd that most of the wins in analytics fall somewhere
between the rudimentary world of HR reporting and the exciting
world of advanced statistics. To get there the average HR
professional just needs a little extra help in bringing rigor to
decision making. If they have the confi dence to choose the
statistically insignifi cant fork in the road, and e[plain why they made
that choice, then they are on the right path to analytics success.T H O U G H T L E A D E R S H I PBy David Creelman