This document discusses the limitations of people analytics and big data in capturing important human factors. Specifically, it notes that some people may experience random emotional problems from manager pressure, short-term targets, or illness that are difficult to predict. Additionally, big data can find random correlations that merely appear significant but are actually just noise. The strongest predictor of future behavior is often a person's past performance, best evaluated by those who know them well, rather than isolated metrics.

1. What People Analytics Can’t Capture
• This presentation is made as
a part of data analytics
internship under Professor
Sameer Mathur(IIM lucknow)

2. Big data:
Big data, like any other, is only as
good as the questions being asked.
And some algorithms can make
unhelpful assumptions.

3. What people analytics cant capture:
“Statistics means never having to say you
are certain.”
We can’t accurately predict people who can
experience random emotional problems like
• Managers over pressure
• Short term targets
• Sudden illness

4. In any massive data analysis, for
instance, there will be random
correlations that look “significant”
but actually are noise, not signal.

5. FACT:
The strongest predictor of a person’s future
behavior is their past performance itself. And that
performance gets evaluated best by people who
know that person well.

6. Big data needs a hard outcome metric
for performance, but the most readily
available metrics may not actually be
the most important variables in
organizational flourishing.

7. INSIGHTS:
The fact remains that data to
determine on the context such as
‘people’ is very tricky and difficult to
lead to an apt prediction.

8. MANAGERIAL RELEVANCE:
. Using an outcome metric like an
executive’s earnings
performance, while ignoring his
role as a boss and his impact on
the morale, loyalty, focus, and
stress levels of his direct reports,
may result in a false indication of
who’s really the best boss.