Jack Hetrick, FACHE Network Director Ronald Stertzbach, P.E. Capital Asset Manager August 9, 2011
Soylent Mean Data Science is Made of People Kim Stedman @KimSted Cameran Hetrick @CameranHetrick.
89
Soylent Mean Data Science is Made of People Kim Stedman @KimSted Cameran Hetrick @CameranHetrick
-
Upload
tyra-reede -
Category
Documents
-
view
215 -
download
3
Transcript of Soylent Mean Data Science is Made of People Kim Stedman @KimSted Cameran Hetrick @CameranHetrick.
Soylent MeanData Science
is Made of People
Kim Stedman @KimStedCameran Hetrick @CameranHetrick
Data Science is of the people,by the people, for the people
Use data to discover truthsthat cause changesthat improve the stuff we make.
The Goal of All This
The Three Futures of Data
#1
#2
Nope
Still Don’t Know
#3
90%
Of the world’s data has been created in the last two years
Source: IBM
30% of companies have invested
in big data technology
Source: Gartner’s 2013 Big Data Survey
8% of companies
have deployed big data solutions
Diffusion of Innovation
Innovators Early
AdoptersEarly
MajorityLate
MajorityLaggards
2.5% 13.5% 34% 34% 16%
WE ARE HERE
Source: Gartner’s 2013 Big Data Survey
Top Challenges of Big Data
80% of USA lives within 20 miles of a Starbucks
That’
That’s Not Data Science
That’s Just DATA
Gartner Hype Cycle: Big Data
2011
2012
2013
What’s Broken
What’s Broken
We’ve got 99 problemsand our tools ain’t one
Use data • We can’t find data scientists to hire• Nobody has the right training yet
To discover truths• There’s too much data & we don’t know where to start.• We can’t get the $$ for headcount or tools.
That cause change• Standalone data studies are rarely actionable.• Our KPIs make people act the opposite of what we
wanted.
That improves the stuff we make• Our results take on horrible lives of their own
Use data • We can’t find data scientists to hire• Nobody has the right training yet
To discover truths• There’s too much data & we don’t know where to start.• We can’t get the $$ for headcount or tools.
That cause change• Standalone data studies are rarely actionable.• Our KPIs make people act the opposite of what we
wanted.
That improves the stuff we make• Our results take on horrible lives of their own
Use data • We can’t find data scientists to hire• Nobody has the right training yet
To discover truths• There’s too much data & we don’t know where to start.• We can’t get the $$ for headcount or tools.
That cause change• Standalone data studies are rarely actionable.• Our KPIs make people act the opposite of what we
wanted.
That improves the stuff we make• Our results take on horrible lives of their own
Use data • We can’t find data scientists to hire• Nobody has the right training yet
To discover truths• There’s too much data & we don’t know where to start.• We can’t get the $$ for headcount or tools.
That cause change• Standalone data studies are rarely actionable.• Our KPIs make people act the opposite of what we
wanted.
That improves the stuff we make• Our results take on horrible lives of their own
We are smrt.
We should solve the things.
Use data• We can’t find data scientists to hire• Nobody has the right training yet
Hacking Skills
Statistics / Mathematics
Business Knowledge
Good Luck
Hacking Skills
Statistics / Mathematics
Domain Expertise
Good Fucking Luck
Visualization
Human Computer Interaction
Stat
istic
s / M
ath
Visualization Tools
Co
mm
un
ication
Sto
rytelling
Data M
anip
ulatio
n
Business Strategy
Big Data Software
Business Knowledge
Machine Learning
Data Warehousing Natural Curio
sity
Problem Solving
Data
Lead
ersh
ip
”Will you be my unicorn?”
no
Not every future data scientist
is a former computer scientist
or statistician
• We can’t find data scientists to hire
• We can’t find data scientists to hire
Hire people from diverse backgrounds into complimentary roles within your data team.
“By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical
skills as well as 1.5 million managers and analytics with the
know-how to use the analysis of big data to make effective decisions”
McKinsey & Company: Big Data: The next frontier for competition
Analytic Rigor is a Thing
Why isn’t everyone
talking aboutthis book
• Nobody has the right training yet
• Nobody has the right training yet
So train people.
- Train soft skills people in tech tools.
- Train hard skills people in research methods and social analysis.
- Train organizations in data use.
Use data
To discover truths• It’s too much data. We don’t know where to start.• We can’t get the $$ for headcount or tools.
That cause changes• Standalone data studies are rarely actionable.• Our KPIs incent people to act in useless ways.
Use data
To discover truths• It’s too much data. We don’t know
where to start.• We can’t get the $$ for headcount or
tools.
Revenue – Cost ____________________________
Profit
1. Increase customers
2. Increase frequency
3. Sell more products
4. Increase price
REVENUE DRIVERS
Process1. Translate each driver into a KPI
2. Understand what moves your KPIs
3. Teach your organization
4. Identify the focus
Goals Hypothesis Prioritize.
1. Potential Impact ($$$)
2. Actionability
3. Threshold for Action
Yes, Continue No, Return
• It’s too much data.We don’t know where to start.
• It’s too much data.We don’t know where to start.
Have goals.
Start with the studies that will have the biggest impact, that you can actually act on.
• We can’t get the $$ for headcount or tools.
• We can’t get the $$ for headcount or tools.
Track your value.
Data is about feedback loops. We are not exempt. Asses your team’s effectivenessat meeting your goal.
Use data
To discover truths
Use data
To discover truths
That cause changes• Our KPIs incent people to act in useless ways.• Standalone data studies are rarely actionable.
A B
A B
Numbers make people
act different
• Our KPIs incent people to act in useless ways.
• Our KPIs incent people to act in useless ways.
Start with how you want people to serve the business.
Then turn that into KPIs. Where you want two groups to act different from each other give them different KPIs.
Yes, Continue No, Return
Launch a test
Big data is a new phase in an ongoing
research tradition
Yes, Continue No, Return
Launch a test
Measure results
Did it meet the goal?
Yes, next improvement
No, iterate, reset or quit
• Standalone data studies are rarely actionable.
• Standalone data studies are rarely actionable.
Conduct studies within a larger business process.
Translate hypothesis into data questions and use the right tool for the job.
Use data
To discover truths
That cause changes
Use data
To discover truths
That cause changes
That improve the stuff we make.
• Our results take on horrible lives of their own
Exp
ertis
e
Exposure
DataTeam
DataPerson
DataPerson
DataPerson
• Our results take on horrible lives of their own
• Our results take on horrible lives of their own
Stay involved.
A data team is not just programmers & statisticians. We are a change agency. We must take responsibility for the changes we drive.
Use data
To discover truths
That cause changes
That improve the stuff we make.
1. We can’t find data scientists to hire2. Nobody has the right training yet
3. There’s too much data & we don’t know where to start.4. We can’t get the $$ for headcount or tools.
5. Our KPIs make people act the opposite than we want.6. Standalone data studies are rarely actionable.
7. Our results take on horrible lives of their own
1. Hire people with complimentary skill sets. 2. Train people at multiple levels.
3. Have goals. Use them to triage research. 4. Track your efficacy and your ROI.
5. Choose your KPIs by how you want people to act. 6. Use the right tool for the job. It’s not always quant.
7. Stay involved. Take responsibility for change.
Or
But wait. There’s more.
@KimSted
@cameranhetrick
