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A Leader’s
Guide To Data
Analytics
Based on the Kellogg Insight article ‘A
Leader’s
Guide to Data Analytics’
“The most important skills in analytics are not
technical skills, they’re thinking skills”
- Florian Zettelmeyer
2 Key Takeaways for leaders of
today
1.
Generation of data needs to
be considered as a strategic
imperative
This leads to creation of an exhaustive
data set, which can serve the need of
any analysis required to solve the
Problem Statement
When the problem statement is lucid
and exhaustive data gathering task is
half done since the metrics need to be
measured are already defined
Data generation process needs to be
thoroughly understood by the
managers to have an idea about the
quality of analysis being done
For example, a hospital that wants to
replace its ultrasound machines based
on the time it takes to perform an
exam using the new devices. But the
data show a surprising result: the new
device is taking longer to use than the
older one.
It turns out that more novice
technicians, who were naturally slower
than the experienced ones, were
choosing to use the newer device, and
this skewed the data.
Being mindful of the data source
prevents incorrect conclusions from an
analysis
2.
Managers with their
business context need to
define and lead the course
of any data analytics task
A manager’s domain
knowledge can help
analysts with their
hypothesis creation task
and streamline the whole
process of problem
breakdown
While validating the results Managers
can further act as first stage QC point
since they would have ballpark figures
of important metrics handy
As big data and analytics bring about this
revolution, managers with a working
knowledge of data science will have an
edge
Disclaimer:
This presentation was created by
Raminder Singh, under an internship
by Prof. Mathur, IIM Lucknow
Thank You

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A Leader's Guide to Data Analytics: Key Takeaways

  • 1. A Leader’s Guide To Data Analytics
  • 2. Based on the Kellogg Insight article ‘A Leader’s Guide to Data Analytics’
  • 3. “The most important skills in analytics are not technical skills, they’re thinking skills” - Florian Zettelmeyer
  • 4. 2 Key Takeaways for leaders of today
  • 5. 1. Generation of data needs to be considered as a strategic imperative
  • 6. This leads to creation of an exhaustive data set, which can serve the need of any analysis required to solve the Problem Statement
  • 7. When the problem statement is lucid and exhaustive data gathering task is half done since the metrics need to be measured are already defined
  • 8. Data generation process needs to be thoroughly understood by the managers to have an idea about the quality of analysis being done
  • 9. For example, a hospital that wants to replace its ultrasound machines based on the time it takes to perform an exam using the new devices. But the data show a surprising result: the new device is taking longer to use than the older one.
  • 10. It turns out that more novice technicians, who were naturally slower than the experienced ones, were choosing to use the newer device, and this skewed the data. Being mindful of the data source prevents incorrect conclusions from an analysis
  • 11. 2. Managers with their business context need to define and lead the course of any data analytics task
  • 12. A manager’s domain knowledge can help analysts with their hypothesis creation task and streamline the whole process of problem breakdown
  • 13. While validating the results Managers can further act as first stage QC point since they would have ballpark figures of important metrics handy
  • 14. As big data and analytics bring about this revolution, managers with a working knowledge of data science will have an edge
  • 15. Disclaimer: This presentation was created by Raminder Singh, under an internship by Prof. Mathur, IIM Lucknow Thank You