7. Unfortunately, there’s no
shortage of individuals with
just enough statistical and
software knowledge to be
data-dangerous. For many
organizations, a mediocre
data scientist may be worse
than none at all.
9. The goal is to make all of the
organization — not just the geeks
and quants — more conversant
in how to align probability,
statistics, technology and
business value creation
11. Small teams not addressing big
problems or grand challenges
but an imperative to generate
insights that could get the
organization doing something
interestingly valuable really fast.
12. One team, did something as simple as comparing
a certain class of tweets from their best customers
with their competitor’s customer’s tweets. The
overlaps and differences immediately suggested
ways to better target and take-away rivals’
customers beyond social media.
13. An industrial products company
started monitoring blogs, boards,
and other social media platforms
around maintenance and service
complaints and then mapping
that data to internal client
maintenance data. The resulting
insights completely changed the
internal dialogues between sales,
customer support, maintenance
engineering.
14. The typical team was collectively less
skilled and competent than a typical data
scientist. But that collective team learned
from each other
18. Marketing
Professor, IIM
Lucknow
Created by Dibya Maheswari,
Accenture Solutions Private
Limited during an internship under
Professor Sameer Mathur, IIM
Lucknow
Dibya Maheswari,
Accenture Soln. Pvt Ltd.
Sameer Mathur