The document provides a leader's guide to data analytics with two key takeaways. First, generating comprehensive data needs to be considered a strategic priority to define clear metrics and ensure high quality analysis. For example, inconsistent data on medical device exam times revealed less experienced technicians were biasing results. Second, managers must use their domain expertise to define analytical problems, help analysts develop hypotheses, and validate results by comparing metrics to expectations. As data and analytics grow in importance, managers with data science knowledge will have an advantage.

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