The document discusses how a working knowledge of data analytics can help leaders make better decisions. It emphasizes that the most important skills are "thinking skills" rather than technical abilities, and that a working knowledge means being able to evaluate data quality and understand where analytics can provide value. It also stresses that analytics must be integrated into business plans and start with well-defined problems. Understanding how data is generated is crucial to avoid issues like reverse causality and to account for strange results based on domain knowledge. Overall, the document argues that managers need not be data science experts but should have enough knowledge to effectively oversee analytics use in their organizations.

1. A Leader’s
Guide to Data
Analytics

2. A working
knowledge of data
science can help
you
lead with
confidence.
-Florian Zettelmeyer
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3. Insights from the
Article
“Where there is data smoke, there is
business fire.”
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4. “ Thinking skills “
The most important skills in
analytics is not technical skills, it
is “Thinking skills”. Managing
well with analytics does not
require a math genius or master
in computer science, instead it
require just a “ working
knowledge” of computer
science .
“ Working Knowledge “
A “Working knowledge “ means
being able to separate good data
from bad, & knowing where
precisely analytics can add value .
 It can help leaders turn
analytics into genuine insight.
 It can also save managers
making decisions based on
faulty assumption.
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Separating Data

5. “ Start with
the
problem “
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6. Data generation is a strategic imperative .
◎Analytics is not a separate
business practice, it has to be
integrated into the business
plan itself.
◎Whatever a company chooses
to measure , the result will
only be useful if the data
collection is done with
purpose.
◎Like all scientific inquiries,
analytics needs to start with a
question or problem in mind.
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7. Understanding Data-generation process
There is a view out there that because analytics is based on
data science , it somehow represents disembodied truth,
which is wrong.
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8. 8

9. Reverse
causality
Understanding data-
generation process can
also uncover the
problem of reverse
causality .
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10. Using Domain Knowledge
Analytics is not simply a
matter of crunching
numbers in vaccum.
◎ Managers can use their
knowledge of business
to acount for starange
results.
◎Asking questions like
“Knowing what you
know about your
business, is there a
plausible explanation for
that result? “ can help .
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11. Know it-Just don’t think it
Decision making in the business
world is being
revolutionized in the same way
that healthcare is with the
widespread adoption of
“evidence-based medicine.”
◎Beyond being the
gatekeepers of their own
analytics, leaders should
ensure that this knowledge is
shared across their
organization—a disciplined,
data-literate company is one
that is likely to learn fast and
add more value across the
board. 11

12. How it is
relevant to a
manager ?
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13. “Data is the new oil”
As big data & analytics bring about this revolution, managers
with the working knowledge of data science will have an edge.
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14. ◎Managers cannot rely on data scientist to
take a lead, ultimately, it is managers job to
choose which problem need to be solved &
how the company should incorporate
analytics into its operations.
◎You cannot judge the quality of the
analytics if you don’thave a clear idea of
where the data came from. Hence,
knowledge of data data generation process
to a manager is crucial.
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15. Thanks!
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-Nishant Kumar