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WHAT PEOPLE ANALYTICS
CAN’T CAPTURE
People analytics, also known as talent
analytics or HR analytics, refers to the
method of analytics that can help
managers and executives make
decisions
about their employees
or workforce.
CURRENT SCENARIO
The latest fad in human resources,
using big data analytics and
personality test scores is to predict
who is best for a given job – so-called
“XQ.”
INSIGHTS
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.
Managers focusing too much on
performance at expense of people can
ruin an organization in the long term
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.
MANAGERIAL RELEVANCE
Algorithms do have their own limitations.
The assumptions built in a test can
themselves discriminate certain traits and
be unfairly biased
Depending more on algorithms or
numeric values will not reduce the
problem of employee. Rather
implementing more technologies
knowing their pros and cons need to be
sort after.
Thank you….

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What People Analytics Can’t Capture by Daniel Goleman

  • 2. People analytics, also known as talent analytics or HR analytics, refers to the method of analytics that can help managers and executives make decisions about their employees or workforce.
  • 3. CURRENT SCENARIO The latest fad in human resources, using big data analytics and personality test scores is to predict who is best for a given job – so-called “XQ.”
  • 5. 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.
  • 6. Managers focusing too much on performance at expense of people can ruin an organization in the long term
  • 7. 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.
  • 9. Algorithms do have their own limitations. The assumptions built in a test can themselves discriminate certain traits and be unfairly biased
  • 10. Depending more on algorithms or numeric values will not reduce the problem of employee. Rather implementing more technologies knowing their pros and cons need to be sort after.