This presentation is based on the article "Data is Worthless if You Don’t Communicate It" by Thomas H. Davenport.

1. DATA IS WORTHLESS IF YOU
DON’T COMMUNICATE IT
ByThomas H. Davenport

2. Communicating is an important step…

3. Why Communicate?
▪ Communication along with some other skills are indispensable to
become a data scientist.
▪ There are tons of data that gets generated every day from across
platforms and devices.
▪ There’s a lot of technicalities involved in cleansing data and finding
out what holds importance to your line of business…..

4. Why Communicate?
…..However, that is when the real challenge is – to showcase that data
in a meaningful way to the decision makers in the business.

5. According McKinsey Global Institute
report...
we’ll need over 1.5 million more data-
savvy managers to take advantage of
all the data we generate.

6. It’s not just data show and tell.
It’s about asking, “How do we use this data
over time to fundamentally get better?”

8. “You don’t need to become a winemaker to
become a wine connoisseur.”
-Professor Xiao-Li Meng
(formerly the Chair of the
Statistics Department at
Harvard and now Dean of the
Graduate School of Arts and
Sciences )

9. Need of the hour...
Managers do not need to become quant jocks. But to fill the alarming
need highlighted in the McKinsey report, most do need to become
better consumers of data, with a better appreciation of quantitative
analysis and — just as important — an ability to communicate what the
numbers mean.

10. What shall a Manager do?
▪ As a manager, it’s not your job to crunch the numbers; but — as Jinho
Kim andThomas discuss in more detail in Keeping Up with the Quants
— it is your job to communicate them.
▪ Never make the mistake of assuming that the results will “speak for
themselves.”

11. Simple Framework for Communicating
George Roumeliotis recommends a simple framework for
communicating about each analysis:
1. Understanding of the business problem
2. Measuring the business impact
3. What data is available
4. The initial solution hypothesis
5. The solution
6. The business impact of the solution

12. Cut the Crap!
Most audiences neither understand nor appreciate details like details
on statistical methods used, regression coefficients, or logarithmic
transformations; they care about results and implications.
Don’t let it get in the way of telling a good story with your data —
starting with what your audience really needs to know.

14. THANK YOU