Data science for product managers: What does it mean? What can it do for your product?
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DATA SCIENCE FOR PRODUCT MANAGERS What does it mean? What can it do for your product?
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Big Data has been a hot topic for the last couple of years. In this session, we will go beyond the hype and look at how organizations are actually using data science to find business value in Marketing, Product Design and Development of Data Science-based products. We will discuss trends and how leading Tech companies see the future of data science. You will leave this session with a simple framework to help you incorporate data science into your product strategy. This presentation was made at ProductCamp Austin #11 - 7/20/2013
Transcript of Data science for product managers: What does it mean? What can it do for your product?
DATA SCIENCE FOR PRODUCT MANAGERSWhat does it mean? What can it do for your product?
Alberto Vargas Talavera
phone: (512) 300-4975
blog: bit.ly/Azblog
twitter: @vargastalavera
Big Data
Statistical Analysis
Smart Data Database Decision Making
Predic
tive
Analys
is
Business Intelligence
Data Mining
DATA SCIENCE IS
The use of technology and data to help make better decisions.
APPLIED DATA SCIENCE IS NOT NEW
Walter Shewhart
1920s
WHAT HAS CHANGED?
WHAT HAS CHANGED?
Explosion in the creation of data and the sources of data
Web and browsing dataSmart PhonesGPSVideoGenomicsGovernment Open DataDevices with sensorFinancial transactionseMail
“… Just two days of the current global data production, from all sources — five
quintillion bytes — is about equal to the amount of information created by all the world’s conversations, ever …”
New York Times on research conducted by theUniversity of California, Berkeley.
Data is not the answer … It is what leads you to the answer.
Data Science
Objective is to Evaluate and Optimize
DesignPerformance
ToolsA/B TestingHypothesis TestingDescriptive statisticsCharts, graphs
Objective is to ForecastUser behaviorTastePreferences
ToolsPredictive ModelsRegressionDecision Trees
DESIGN OPTIMIZATION A/B TESTINGOBAMA’S GOOGLE CONNECTION, DAN SIROKER
THE WINNING DESIGN
NETFLIX AND THE $1M CONTEST
Mission: Improve recommendation
engine by 10%
NETFLIX AND THE $1M CONTEST
TARGET STORES AND PREGNANCY
Mission: Find relevant messages for each customer
TARGET
Predictive ModelDataAlberto Vargas
Build a Data Science Strategy Framework
1. Define your objective
2. Define success
3. Define what data you need
4. Define data strategies for data collection
5. Analyze the data
6. Model the data
7. Verify the analysis and modeling
8. Determine how you will integrate results into your process or
decision making.
The Framework – PM Domain
1. Define your objective
2. Define success
3. Define what data you need
4. Define data strategies for data collection
5. Analyze the data
6. Model the data
7. Verify the analysis and modeling
8. Determine how you will integrate results into your process or
decision making.
THE FRAMEWORK – STEP 1
1. Define your objective
What is the problem? What do you want to
accomplish?
THE FRAMEWORK – STEP 2
2. Define success How do you know that you
met your objective? How does success look
like? How do you measure it
quantitatively?
THE FRAMEWORK – STEP 3
3. Determine what data you need to measure or collect
What variables, factors, or models support your objectives?
THE FRAMEWORK – STEP 4
4. Define and carryout strategies to collect data
How do you get it?
Where do you get it?
When do you get it?
THE FRAMEWORK – STEPS 5 - 7
Technical Skills Data Mining Statistics DOE Regression Analysis Advanced
mathematics
THE FRAMEWORK – STEP 8
8. Define how results will be integrated into your process or decision making
THE FRAMEWORK – SUMMARY
1. Define your objective
2. Define success
3. Define what data you need
4. Define data strategies for data collection
5. Analyze the data
6. Model the data
7. Verify the analysis and modeling
8. Determine how you will integrate results into your
process or decision making.
THE FRAMEWORK – SUMMARY
THE FUTURE
Quantify the unquantifiable. Measure the hard to measure
Integrate data from different sources
Expand machine learning
RECOMMENDED READING
Video: The importance of A/B testing: Dan Siroker
http://www.youtube.com/watch?feature=player_embedded&v=yY-HBW5CNcg
Video: How Target gets the most out of it guest data, Andrew Pole
http://www.rmportal.performedia.com/node/1373
Book: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
Eric Siegel, Thomas H. Davenport
Book: The Signal and the Noise: The Art and Science of Prediction
Nate Silver
Book: Moneyball: The Art of Winning an Unfair Game
Michael Lewis
