This document discusses the role of data science and insights in helping businesses and individuals make better decisions. It argues that having large amounts of data alone is not sufficient - what matters more are the insights generated from the data. The document outlines the typical phases of a data science project from asking the right questions to deploying models and iterating. It acknowledges challenges in healthcare data but sees opportunities given the importance and potential impact of using data science in healthcare.

1. Better Living
Through Data
Science Scott
Nicholson
@scootrous
snicholson@
accretivehealth.com
lnkd.in/scott

2. Helping people
and businesses
make better
decisions

3. Does big data help people make better decisions?
No, insights do.
BD is a realization that we can do more with data than we previously
thought, just as much as it is about more data being available
Companies in 2000 who didn’t know what to do with their “small”
data won’t be any better off with big/huge/fat data today.
It’s about insights, and data scientists are well-suited to create
them.
I’d prefer an brilliant Excel/SQL guru who asks the right questions
than a deeply technical ‘big data’ engineer who focuses on elegance
and algorithms.

4. What is data
science?
Project phases
Today
Where do you find
people who can do
it?

5. /Hila
“Data Scientist”
means different
things to different
people

6. /Hila
“Data Scientist”
means different
things to different
people
Credit: Drew Conway

7. /Hila
“Data Scientist”
means different
things to different
people
Credit: Hilary Mason

8. “Data Scientist”
means different
things to different
people

9. My definition of a data scientist:
Someone who uses data to solve
problems end-to-end, from asking
the right questions to making
insights actionable.

10. End-to-end data science: five stages
Ask the Choose Extract & Deploy,
Build a
right your clean learn,
model
questions approach your data iterate

11. One of the hardest
things to find in a
data scientist
Phase 1
Ask the Right
Questions

12. Do we always
need to build a
model?
Phase 2
Choose an
Approach

13. Leverage other
disciplines and
intuition

14. Is building a
model the first
thing you should
do?
Credit: Sam Shah

15. The g(l)ory of data
science: most of
the work is here
Phase 3
Extract and
Clean Data

16. ddd
ddd
In the trenches, dirty jobs, porta-potty
Vs
Luxury, rocket science, fast cars

17. Health Care
EHR is not
designed for
data extraction

18. LinkedIn
On the frontier,
but still difficult
to do agile data
Grab better/new logos

19. For most
problems, a wheel
has already been
invented…
Phase 4
…just recognize
Model the wheel!
Building
Example: missing
charges on bill

20. Always use
workhorse
models first
Online advertising: logistic regression in production at Yahoo for a long time

21. LinkedIn
Skills universe

22. LinkedIn
Skills universe

23. Health Care
Networked data
also common

24. Focus on quick solutions to identify bogeys and get feedback
Think like Eric Ries Agile Data
Photo of sand trap?
dd

25. Deployment and
execution of
predictive models
is crucial
Phase 5
Deploy,
Learn, Iterate Iteration is key,
especially in an
agile analytics
framework

26. LinkedIn
Subscriber churn
prevention emails

27. Health Care
Population health
management &
quality of care

28. LinkedIn
Build a viewer
app
Picture of viewmaster

29. Well that’s great but who is going to do all of that wo
Who is good at this stuff?

30. Just as physicists moved to
Wall Street to be quants and
then on to online advertising
and consumer web, there will
be a significant talent
migration into health care in
the next few years.

31. But huge
opportunities

32. One of the fundamental
problems of our time
18% of GDP! 0.01% is giant
revenue potential
Data availability and
richness only increasing
But huge
The right people are opportunities
realizing data and data
science are core to the
solution.

33. Take-aways

34. Data science is
industry-agnostic

35. There are many
challenges, but
this is just the
beginning.

36. EHR data extraction and
updates difficult
Implementation barriers
There are many Nothing scales
challenges, but
Privacy issues
this is just the
beginning. Data aggregation difficult
Not all hospitals are
Stanford, Vanderbilt, etc.

37. What can we do
about these
challenges?

38. Daily/hourly decision
support?
Communicate value
of data mining to
patients
What can we do
SMART, roll-your-own
EHRs
about these
challenges?

39. bit.ly/accretive-data-science-job
Thank you!
(we’re hiring)
Scott
Nicholson
@scootrous
snicholson@
accretivehealth.com
lnkd.in/scott