The business world is increasingly adopting the Moneyball principle of using data to predict and gain a competitive advantage in healthcare, telecommunications, retail, media, energy, and many other industries. Some argue that organizations that do not possess strong data and the skills to create value out of it will not survive. How can companies leverage data - sometimes described as the “new gold” - for consumer insights, improved processes or new product ideas? Can data assets be leveraged effectively for the overall business?

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How to extract significant
business value from big
data

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Lutz

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Disclaimer
This presentation is solemnly my
opinion and not necessarily the
opinion of my employer Harvard,
Linkedin or Cornell.

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Agenda
The right Ask
Data is King
Team-Work: Discover an Ask
Lunch
Decision Tree
Team-Work: Your Model
Pitfalls with Data
Break
Technology
Team-Work: Become Data Driven?
BI vs. Data Science
Build A Team

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Why is there such hype?

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PREDICTING

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The ones who predict:
image by Mike under Creative Commons

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McK Study forecasted:
10 Times More Managers
per Data Savvy Person

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?

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Actionable Insights

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ASK the right Questions.
MEASURE the right data – even if it
is not Big data.
Take Actions and LEARN from them.
?

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Google had the right Question
is difficult to find

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Fisheye Learning

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Already Known Asks
byrgiesekingunderCreativeCommons(CCBY2.0)
Who should get an
E-Shot?
Territory Planning
for my Sales Force
Budget Planning of
Marketing Spent
Online Product
Recommendation
Real Time Betting
for Ad-spaces
Customer
Segmentation
Social Media
Influencers
Call Center
Routing based on
Questions
Capacity
Forecasting
…. and more

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The “So-What” Test

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Data by itself
is
USELESS
Information by itself
is
USELESS
Only action counts!

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Data by itself
is
USELESS
Information by itself
is
USELESS
Let’s connect...

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Recommending

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A good ‘so what’?
DatabyLinkedIn
Example: Laboratory Manager?

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A bad ‘so what’
300+ Million Member at LinkedIn
60.000 with a Job Title that might fit
19.000 who switched after 3 to 8 years
24 who had the same career path

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Benchmarking in Health Care

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Benchmarking is Overhyped

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Recommendations: Your Focus
People You May Know
Groups You May
Like
Ads in Which You May Be Interested
Companies You May Want to Follow
Pulse
Similar Profiles

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Recommendation in Health Care

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Many Good Examples
Benchmark Recommendations

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Agenda
The right Ask
Data is King
Team-Work: Discover an Ask
Lunch
Decision Tree
Team-Work: Your Model
Pitfalls with Data
Technology
Team-Work: Become Data Driven?
BI vs. Data Science
Build A Team

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“Data is the new oil”
- World Economic Forum

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“DATA IS THE NEW OIL”
Oil Mine the oil
Use the oil
Goal

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V OF “BIG DATA”
Data at scale
(TB, PB … )
Data in many forms
(Structured,
unstructured ...)
Speed
(Streaming, real
time, near time ..)
Uncertainty
(imprecise, not always
up-to-date ..)

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1st. Round Monopoly
Photo by William Warby under the Creative Commons (CC BY 2.0)

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$3.2 billion

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Prediction
Photo by KOMUnews under the Creative Commons (CC BY 2.0)
Boring could be the New Sexy!

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Data Might (Not) Be
A Barrier To Enter

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Data Might (Not) Be
A Barrier To Enter

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Public Data is Not

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DATA
Categorical
• Ordinal: Monday, Tuesday, Wednesday
• Nominal: Man, Woman
Quantitative:
• Ratio: Kelvin, Height, Weight
• Interval: Celsius, Fahrenheit
Structure:
• Structured
• Unstructured
• Semi-structured / Meta data
Read more: “On the Theory of scales of measurement”
S.Stevens 1946

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Data Is King
but not all data is equal.

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The Tale of “Social Media” Data
Source:‘AskMeasureLearn’byO’ReillyMedia

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Structured Data Is Often
Better
New York Weather in April 2013
Source: ‘Ask Measure Learn’ by O’Reilly Media

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Sometimes,
it’s worth it.
RE @dave_mcgregor: Publicly
pledging to never fly @delta again.
The worst airline ever. U have lost my
patronage forever du to ur
incompetence
Completely unimpressed with
@continental or @united. Poor
communication, goofy reservations
systems and all to turn my trip into a
mess.
@SouthWestAir I know you don't
make the weather. But at least pretend
I am not a bother when I ask if the
delay will make miss my connection

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But Data Is King
This will give birth to devices (i.e., the Star Trek Tricorder)
that allow you, the consumer, to self-diagnose, anytime,
anywhere.

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Agenda
The right Ask
Data is King
Team-Work: Discover an Ask
Lunch
Decision Tree
Team-Work: Your Model
Pitfalls with Data
Technology
Team-Work: Become Data Driven?
BI vs. Data Science
Build A Team

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About Innovation
ByAlistairCroll

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The Media industry has changed! The retail industry has change! The
Education sector is changing! Finance Industry and healthcare sector are
under attack. Which industry will be next?

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Team Work
Photo by Creative Sustainability under the Creative Commons (CC BY 2.0)
A. The Ask
○ is it actionable? “So What?
○ is it Benchmarking / is it Recommendation
B. The Data
○ do only you have this data?
○ do you have a feedback loop?

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LUNCH

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Agenda
The right Ask
Data is King
Team-Work: Discover an Ask
Lunch
Decision Tree
Team-Work: Your Model
Pitfalls with Data
Technology
Team-Work: Become Data Driven?
BI vs. Data Science
Build A Team

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Are Retail Banks ‘dead’?

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Decision Trees Step by Step
by Maciej Lewandowski under Creative Commons (CC BY-SA 2.0)

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Split Apples & Mandarins

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What Is The Target
Variable?

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What Is The Features To
Describe The Target?

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What Is The Features To
Describe The Target?
• Weight: light, medium, heavy - or x gram
• Size: round or not
• Color:green, orange, red
• Surface: flat or porous surface
• …

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Which Feature Works
Best?
● The variable with the most important information
about target variable.
● Which variable can split the group as
homogeneous with respect to the target variable.
(pure vs. inpure)

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Color Red?
Color Orange?
Split on Color Red
vs. Split on Color
Orange
Which One Is
Better?

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We Need A Way To
Describe Chaos
"ClaudeElwoodShannon(1916-
2001)"bySource.Licensedunder
FairuseviaWikipedia

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ENTROPY
Entropy is a measure of disorder.
Entropy only tells us how impure one
individual subset is.

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ENTROPY &
PROBABILITY
entropy = -p1 * log (p1) - p2 * log (p2) - ….

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● Highest Entropy
Reduction
● Highest Information
Gain

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1st. Entropy Without Split
entropy =
-p1 * log (p1) - p2 * log (p2)
Apple: 8 out of 15
p(apple)= 8/15
Mandarines: 7 out of 15
p(mandarine)= 7/15
ENTROPY (Without Split):
-p(apple)*log(p(apple)) -p(mandarins)*log(p
(mandarines))
= 0.996791632 = 1
very impure

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Color Red?
Color Orange?
entropy =
-p1 * log (p1) - p2 * log (p2)
ENTROPY (After Split on Red):
= 8/15* ENTROPY (Split on Red=’no’)
+ 7/15* ENTROPY (Split on Red=’yes’)
= 0.43 + 0.28 = 0.71
INFORMATION GAIN
= Entropy (Before) - Entropy (After) = 1 - 0.71 = 0.29
ENTROPY (Split on Red=’
no’):
= -6/8*(log2
(6/8))-2/8*(log2
(2/8))
= 0.81
ENTROPY (Split on Red=’yes’):
= -6/7*(log2
(6/7)) -1/7*(log2
(1/7))
= 0.59
ENTROPY (Split on
Orange=’yes’):
= -6/6*(log2
(6/6))
= 0
ENTROPY (Split on Orange=’no’):
= -8/9*(log2
(8/9))-1/9*(log2
(1/9))
= 0.50
ENTROPY (After Split on Orange):
= 6/15* ENTROPY (Split on Orange=’no’)
+ 9/15* ENTROPY (Split on Orange=’yes’)
= 0 + 0.23 = 0.23
INFORMATION GAIN
= Entropy (Before) - Entropy (After) = 1 - 0.23 = 0.77

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INFORMATION GAIN (IG)
Information gain measures how much a
given feature improves (decreases) entropy
over the whole segmentation it creates.
How important is this feature for the
prediction?

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Decision Tree
Color Orange? ROOT NODE
LEAFS

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Decision Tree
Color Orange?
Decision Tree Structure

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Which Feature Would
Be Better?

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Heavy?
Always Start
With Highest IG

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Hyperplanes

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Hyperplane
(2 dimensions)
Mandarines Red Green
LightHeavy

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Agenda
The right Ask
Data is King
Team-Work: Discover an Ask
Lunch
Decision Tree
Team-Work: Your Model
Pitfalls with Data
Technology
Team-Work: Become Data Driven?
BI vs. Data Science
Build A Team

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Back To The Lending Industry

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BIG ML
Competitors:
● Algorithms.io
● SnapAnalytx
● wise.io
● Predixion Software
● Google Prediction
API

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Real Data Set

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Build Database
How Do You
Deal With
Categorical vs.
Numeric
Variables in
Decision
Trees?
screenshot from bigML tool

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Configure And Build
Model
Select The
Objective Field
- What To Train
The Model On?
That is the Row
ID - surely no
impact.
screenshot from bigML tool

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screenshot from bigML tool

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screenshot from bigML tool

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Highest Information Gain
screenshot from bigML tool

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Using The Model
screenshot from bigML tool

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Using The Model
screenshot from bigML tool

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Using The Model
screenshot from bigML tool

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Found 2,470 New Instances
screenshot from bigML tool

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How Can I Improve
Now Quality?

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Agenda
The right Ask
Data is King
Team-Work: Discover an Ask
Lunch
Decision Tree
Team-Work: Your Model
Pitfalls with Data
Technology
Team-Work: Become Data Driven?
BI vs. Data Science
Build A Team

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Pitfalls with Data
Data & Ethics
More Data & Overfitting
Confidence & Cut-off
Cause & Correlation
…

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How Did They Improve Scoring?

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Social Network Info
Could Social Network improve the quality of our
prediction?
Who is more credit-worthy?
a. Tim whose friends are all very credit worthy
b. Tom whose friends are not creditworthy

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Ethical?

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Nobel Worthy!
Muhammad Yunus
Photo by University of Salford under
Creative Commons CC BY 2.0

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In the EU insurers will no
longer be allowed to take the
gender of their customers into
account for insurance
premiums:
● young men's premiums
will fall by up to 10%
● young women's premiums
will rise by up to 30%
by: BBC News: http://www.bbc.com/news/business-12608777
Not Everything That Is Possible Is
Legal

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Pitfalls with Data
Data & Ethics
More Data & Overfitting
Confidence & Cut-off
Cause & Correlation
…

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The Tale of Big Data

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Overfitting
To tailor a model to training data at the expense of
being generalizable for previously unseen data
points. The model becomes perfect in describing
noise and spurious correlations.
TRADE OFF
Complexity of a Model & Overfitting Likelihood

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How Trustworthy Is
This Prediction?
• 45 instances
• 59% confidence
screenshot from bigML tool

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The Need for Domain
Knowledge

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Pitfalls with Data
Data & Ethics
More Data & Overfitting
Confidence & Cut-off
Cause & Correlation
…

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Give Credit or Not?
49% Confidence
screenshot from bigML tool

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CONFUSION MATRIX
Pregnant
(60)
Not pregnant
(940)
Pregnant
(A)
true positive
(B)
false positive
Not
pregnant
(C)
false negative
(D)
true negative
Classifier
Reality

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Agenda
The right Ask
Data is King
Team-Work: Discover an Ask
Lunch
Decision Tree
Team-Work: Your Model
Pitfalls with Data
Technology
Team-Work: Become Data Driven?
BI vs. Data Science
Build A Team

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How Invented Big Data
Infrastructure?

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How Invented Big Data
Infrastructure?

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Issue Of Yahoo
CENTRALIZED SYSTEMS ARE EXPENSIVE
• diminishing returns in power (overhead issue)
• exponential cost to scale.
• slow to transport (ETL) the data
Scan 1000 TB Datasets on a 1000 node cluster:
• Remote Storage @ 10 MB/s = 165 min
• Local Storage @ 200 MB's = 8 min
MAKE SYSTEMS FAULT TOLERANT
1000 nodes - a machine a day will break

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The Vision
CHEAP Systems
• can run on commodity hardware
Computation are done DECENTRAL
• ability to ‘dispatch’ a task
• parallelize work-streams
Fault TOLERANT
no matter where and when break is not an issue

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How To Access HDFS
Hadoop Storage (HDFS /
HBase / Solr)
Map Reduce

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Via The Normal Languages
Hadoop Storage (HDFS /
HBase / Solr)
Map Reduce
MapReduce
Hive
Pig/Casscading
Giraph
Mahout
SQL Like
Scripting Like
Graph Oriented
ML Engine

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Pro & Con
Hadoop Storage (HDFS /
HBase / Solr)
Map Reduce
MapReduce
Hive
Pig/Casscading
Giraph
Mahout
SQL Like
Scripting Like
Graph Oriented
ML Engine
Store
ETL:
Extract /
Transform /
Load
DB / Key Value Store
Visualize
Pro:
way better than traditional BI
Con:
Heavy tech involvement. 12-18
month for non-tech company to
implement a schema

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Pro & Con
Hadoop Storage (HDFS /
HBase / Solr)
Map Reduce
MapReduce
Hive
Pig/Casscading
Giraph
Mahout
SQL Like
Scripting Like
Graph Oriented
ML Engine
DB / Key Value Store
Visualize
New Approaches:
● Spark
● Tez
● Flink

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Agenda
The right Ask
Data is King
Team-Work: Discover an Ask
Lunch
Decision Tree
Team-Work: Your Model
Pitfalls with Data
Technology
Team-Work: Become Data Driven?
BI vs. Data Science
Build A Team

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Why Is It So Hard To Become Data Driven

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Ingredients of Data Products
The question?
Ask
The need?
The Why? Measure
The Data?
The features?
Team
All of them are necessary - None of them is sufficient!
The algorithms?
The right Skills?
Collaboration
111

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How To Ingest Ideas
Hack - Days & Incubator
Internal Process
External Competition
Close Collaboration between
Business & Data Scientists“All we do is Data” - Jeff Weiner
112

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What Would You Need To Do To Be
A Leader In Data

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Agenda
The right Ask
Data is King
Team-Work: Discover an Ask
Lunch
Decision Tree
Team-Work: Your Model
Pitfalls with Data
Technology
Team-Work: Become Data Driven?
BI vs. Data Science
Build A Team

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Old vs. New
Old School Today / Big data
Data Amount Gigabytes & Terabytes Petabytes & Exabytes
IT Infrastructure Centralized Decentralized / Parallelized
Data Types Structured Structured & unstructured
Schema Stable schema Schema on the fly
When and How is the ASK
formulated?
Set ask Ad-hoc ask

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How to build a Data Team

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Data Scientist

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Data Scientist Confusion

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New Ways To Automate

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Data Scientist
BI Analyst
Engineer
Product Manager
Communication Skills Domain Knowledge

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You Learned
image by Mike under Creative Commons
• The Ask is the most Important part -
you need Domain Knowledge
• Data Science is NO Rocket Science
• Data is King & There is Monopoly
Game happening
• Data Can be misleading
• Data is a Team Sport

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Thank You

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What to MEASURE?
• Error
• Correlation
• Cost
&
• Privacy
Workbook “Measure” at LutzFinger.com