2. Insights
Good critical piece by leadership expert Daniel Goleman.
He points out the possible weakness of using analytics in
assessing people and management performance.
For example, "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.”
3. Lots of ‘soft’ aspects are not easily captured with
analytics..
“character traits like integrity and
compassions(which) are surprisingly strong derives
of business success.”
“The biggest objection comes from the fact that the
strongest predictor of a persons future behaviour is
their past performance itself. And that performance
gets evaluated best by people who known that
person well.”
4. People analytics helps organizations to
make smarter, more strategic and more
informed talent decisions. With people
analytic, organizations can fins better
applicants, make smarter hiring
decisions, and increase employee
performance and retention.
6. Many business are reaping rewards from
big data analytics. But there are also
some areas of disappointment. Experts
caution that big data, like another, is only
as good as the questions being asked-
and that some algorithms can make
unhelpful assumptions.
7. STATISTICS
There’s a saying in the sciences,
“Statistics means never having to say
you are certain.” in any massive data
analysis, for instance, there will be
random c correlations that look significant
but actually are noise, not signal.
9. Big data needs a hard outcome metric for
performance, but the most readily
available metrics may not actually be the
most important variables in organizations
flourishing.
11. Managers over pressure
Short term targets
Working environment depends on
previous work done.
Managers force his people to work hard
to meet quarterly targets
12. Experts caution that big data, like any
other is only as good as the questions
being asked- and that some algorithms
can make unhelpful assumptions.
13. MANAGERIAL RELAVANCE
The more we depend on algorithm,
numeric values problems of employee
will not be reduced but we need to
implement more technologies knowing
their pros and cons.
