A leader's guide to data analytics

1. A Leader’s Guide to Data
Analytics
Based on insights from Florian Zettelmeyer

2. DATA HAS BECOME AN
ESSENTIAL BUSINESS
TOOL.

3. BUT LEADERS ARE OVERWHELMED BY
THIS NEW TREND AND CONSIDER IT AS
A TECHNICAL PROCESS, SEPARATE
FROM CORE BUSINESS PROCESS.

4. FLORIAN
ZETTELMEYER
DIRECTOR, DATA ANALYTICS,
KELLOGG SCHOOL OF MANAGEMENT
“Managers should
not view analytics
as something that
falls beyond their
purview. “The most
important skills in
analytics are not
technical skills,
they’re thinking
skills.”

5. INSIGH
T 1
MANAGERS NEED NOT TO BE A
DATA SCIENTIST, A WORKING
KNOWLEDGE OF ANALYTICS

6. WORKING
KNOWLEDGE
Working knowledge of
data saves a manger
from faulty decisions
made on bad data. It
can help a leader turn
analytics into genuine
insight
Noun. working
knowledge (uncountable)
A knowledge of how to make
something work without any deeper
understanding of why it works, or of
how to fix it if it breaks. Candidates
must have a working knowledge of
standard Windows applications.

7. WORKING
KNOWLEDGE
Without knowing the
purpose of why data is
being collected or what
problem it intend to solve,
a data is useless.
Start with
the
problem.

8. Insight 2
If a leader decides that he wants to
target consumers interested in
fashion, next thing he has to do is
gather data on preference and liking
of that customers.

9. “You have to
think about the
generation of
data as a
strategic
imperative.”
Analytics is not a separate practice, but it has to be integrated
with business plan.

10. It is manager’s job to align analytics with
business plan as they have domain
knowledge
“A data scientist will give you statistics on
how many customers prefers a particular type
of outfit. Now it is manager’ decision how to
target this customers.”

11. WORKING
KNOWLEDGE Without knowing from
which method data is
collected will cause trouble
for a company.
A process of understanding
a Good and a Bad data
starts with questing the
source of data itself.
Understand the
Data-
Generation
Process

12. “You cannot
judge the quality
of the analytics if
you don’t have a
very clear idea of
where the data
came from.”

13. WRONG
ASSUMPTIONS
Insight 3
While collecting data, if
manager assumes a
parameter wrong, purpose
of collecting data is itself a
void. It involves sampling
errors, wrong hypothesis,
invalid instrument for data
collection etc.

15. WORKING
KNOWLEDGE
A manager wants to analyse
effect of Email marketing. It
shows that because of email
marketing, sales are up.
However, manager ignored
other underlying causes such
as targeting regular and loyal
customers, season etc. which
does not justify the outcome
Assigning a wrong
cause to an effect
which craves a
way for bad
decision making.

16. WORKING
KNOWLEDGE
“Knowing what you
know about your
business, is there a
plausible explanation
for that result?”
Use Domain Knowledge to
analyse whether this data
will be helpful in your
business. Data scientists do
not have domain knowledge
as a manager hence there is
s significant difference
between all the insights and
recommendations.

17. Manager does not need to use tools to
analyse data but think how it will be
useful in the growth of an
organisations.

18. Thank
you!
Prepared by: Abhishek