Analysis of "A Leader’s Guide to Data Analytics"

1. A Leader’s Guide to
Data Analytics
BY FLORIAN ZETTELMEYER

2.  In recent years, data science has become an essential
business tool.
 Companies are now able to measure every aspect of
their operations in granular detail.
 But many businesses are hesitant to get involved in
what they see as a technical process.

3. For Florian Zettelmeyer, managers should not
view analytics as something that falls beyond
their purview.

4. “The most important skills in analytics are not
technical skills,” he says. “They’re thinking skills.”

5. Managing well with analytics does not require a math
genius or master of computer science; instead, it requires
what Zettelmeyer calls “a working knowledge” of data
science

6. What can a working
knowledge do?
 A working knowledge of data science can help leaders turn
analytics into genuine insight
 It can also save them from making decisions based on faulty
assumptions.
 This means being able to separate good data from bad, and
knowing where precisely analytics can add value.

7. “When analytics goes bad,” Zettelmeyer says, “the number
one reason is because data that did not result from an
experiment are presented as if they had,” he says. “Even for
predictive and prescriptive analytics, if you don’t
understand experiments,” he says, “you don’t
analytics.”

8. 1. Start with the problem

9.  Managers collect data without knowing how they will use it.
You have to think about the generation of data as a strategic
imperative.
 “You can’t just hope that the data that gets incidentally
created in the course of business is the kind of data that’s
going to lead to breakthroughs,” Zettelmeyer says.

10. 2. Understand the Data-
Generation Process

11.  “You cannot judge the quality of the analytics if you don’t
have a very clear idea of where the data came from.”
 Most managers share a common behavioral bias: when results
are presented as having been achieved through complicated
data analytics, they tend to defer to the experts.

12.  Because analytics often boils down to making comparisons
between groups, it is important to know how those groups are
selected
 the data-generation process can also uncover the problem of
reverse causality.

13. 3. Use Domain Knowledge

14.  Zettelmeyer recommends asking the question: “Knowing what
you know about your business, is there a plausible explanation
for that result?”
 In addition to making sure that data are generated with
analytics in mind, managers should use their knowledge of the
business to account for strange results.

15.  Beyond being the gatekeepers of their own analytics, leaders should
ensure that this knowledge is shared across their ganization—a
disciplined, data-literate company is one that is likely to learn fast
and add more value across the board.
 “If we want big data and analytics to succeed, everyone needs to
feel that they have a right to question established wisdom,”
Zettelmeyer says. “There has to be a culture where you can’t get
away with ‘thinking’ as opposed to ‘knowing.’”

16. A working knowledge of
data science can help you
lead with confidence.
By: Harshit Sahni