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
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
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
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.
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.
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.