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1 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
BigData, Old Data, to AllData:
Predictive Analytics i...
2 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Outline
• Big Data all around us
• The CDO role and D...
3 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
What Matters in the Age of Analytics?
1.Being Able to...
4 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Why Big Data?
A new term, with associated “Data Scien...
5 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
The 4-V’s of “Big Data”
• Big Data is Characterized b...
6 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Male, age 32
Lives in SF
Lawyer
Searched on
from Lond...
7 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Male, age 32
Lives in SF
Lawyer
Searched on from
Lond...
8 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
The Distinction between “Classic Data” and
“Big Data”...
9 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Text Data: The Big Driver
• We speak of “big data” an...
10 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
IT Log & Security
Forensics & Analytics
Automated De...
11 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
A few words on:
The Chief Data Officer
Why are compa...
12 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Why a Chief Data Officer?
• There is a fundamental r...
13 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Why data is important
Every customer interaction is ...
14 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Fundamental Data Principles to
Support Analytics
Usa...
15 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Data Axioms
1. Data gains value exponentially when
i...
16 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Data Axioms
2. Fusing Data together from
disparate/i...
17 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Data Axioms
3. Standardisation is essential
• for su...
18 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Data Axioms
4. Data governance and policy must be
ce...
19 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Data Axioms
5. Recency Matters  data streaming in
m...
20 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Data Axioms
6. Data Infrastructure Needs:
• rapid re...
21 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Data Axioms
7. Data is a primary competency and not
...
22 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Reality Check
So I am a marketer, how do I use
BigDa...
23 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
24 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
25 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 © LUMA Partners LLC 2014
Gamification
SOCIAL LUMAsca...
26 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Reality Check
So what should Users of Analytics in
B...
27 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Load the Data into a Data Lake
Your life will be so ...
28 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
The Data Lake -- according to Waterline
We loaded th...
29 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
From a Data Lake to Amazon Browser
Amazon Simple Sea...
30 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Reality Check
So where do analysts and data
Scientis...
31 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Data Analysis Vs. Data Prep
Let’s Mine
the Data
32 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Reality Check
So what do technology people worry
abo...
33 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
To Hadoop or not to Hadoop?
when to use techniquesre...
34 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Drivers of Hadoop in Large
Enterprises
Cost of Stora...
35 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Analysis & Programming Software
PIG
HIPI
36 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Hadoop Stack
37 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
38 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
39 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
40 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
41 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Reality: If Storage is Biggest Driver of
Hadoop Adop...
42 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Turning the three Vs of Big Data into Value
Understa...
43 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Many Business Uses of Predictive Analytics
Analytic ...
Case Studies:
1. Context Analysis (unstructured data)
2. Yahoo! predictive modeling
45 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Understanding Context
46 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Reality Check
So who is the company we think is
best...
47 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Biggest BigData in Advertising?
Understanding Contex...
48 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
The Display Ads Challenge Today
What Ad would you
pl...
49 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
The Display Ads Challenge Today
Damaging to Brand?
50 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
The Display Ads Challenge Today
What Ad
would you
pl...
CONFIDENTIAL
Why did Google Serve
this Ad?
51
this is how NetSeer
actually sees this
content
NetSeer:
SOLVING ACCURACY ISS...
CONFIDENTIAL
high MPG
ford
low emission
fuel efficiency
ECONOMY CARS
economy
vehicles
microscope
lenses
reading
glasses
au...
53 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
NetSeer: Intent for Display
• Currently Processing 4...
54 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Problem: Hard to Understand User
Intent
Contextual A...
CONFIDENTIAL
http://diabetes.webmd.com/default.htm
NETSEER CONNECTING THE DOTS
55
Understanding intent of user online acti...
CONFIDENTIAL
http://www.healthline.com/health/type
-2-diabetes/insulin-pumps
NETSEER CONNECTING THE DOTS
56
Understanding ...
CONFIDENTIAL
Search: Sulfonylureas
NETSEER CONNECTING THE DOTS
57
Understanding intent of user online actions and connecti...
Case Studies:
1. Context Analysis (unstructured data)
2. Yahoo! Predictive Modeling
Yahoo! – One of Largest Destinations on the Web
80% of the U.S. Internet population uses Yahoo!
– Over 600 million users p...
Yahoo! Big Data – A league of its
own…
Terrabytes of Warehoused Data
25 49 94 100
500
1,000
5,000
Amazon
Korea
Telecom
AT&...
Behavioral Targeting (BT)
Search
Ad Clicks
Content
Search Clicks
BT
Targeting ads to
consumers whose recent
behaviors onli...
Male, age 32
Lives in SF
Lawyer
Searched on
from London
last week
Searched on:
“Italian
restaurant
Palo Alto”
Checks Yahoo...
How it works | Network + Interests +
Modelling
Analyze predictive patterns for purchase
cycles in over 100 product categor...
Recency Matters, So Does Intensity
Active now… …and with feeling
Differentiation | Category specific
modelling
time
intensityscore
time
intensityscore
IntenseClickZone
Example 1: Category...
Differentiation | Category specific
modelling
time
intensityscore
Intense Click Zone
Example 1: Category Automotive
Differ...
Automobile Purchase Intender Example
A test ad-campaign with a major Euro automobile manufacturer
 Designed a test that s...
Mortgage Intender Example
We found:
1,900,000 people looking
for mortgage loans.
+122%
CTR Lift
Mortgages Home Loans Refin...
Experience summary at Yahoo!
• Dealing with one of the largest data sources (25
Terabyte per day)
• Behavioral Targeting b...
Lessons Learned
A lot more data than qualified talent
 Finding talent in BigData is very difficult
 Retaining talent in ...
RapidMiner in Big Data
Case Study
RapidMiner’s Strengths
7272
• Open Source Community & Marketplace – Crowd-sourced innovation,
quality assurance, market aw...
30,000+ Downloads per Month
SELECT LIST OF RECIPIENT ORGANIZATIONS
7373
Government & DefensePharma & Healthcare
Consulting...
74
PayPal
Who > world leading
online payment services
provider
Solution > Customer
feedback and voice of the
customer anal...
SO WHAT IS THE
BIG DATA STORY?
So the data is naturally moving to
Hadoop...
Situation:
–The data is moving to Hadoop for Cost (storage) and
Convenience ...
Radoop – RapidMiner on Hadoop
Opportunity:
–Avoid expensive data movement
–Leverage convenient data transformation
–Thous...
Big Picture on Big Data Analytics
Observations and
Concluding Remarks
Retaining New Yahoo! Mail Registrants
Often,
Simple is Very Powerful!
Integrating Mail and News
Data showed that users often check their mail
and news in the same session
–But no easy way to ...
“In the news” Module on Mail Welcome Page
Increased retention on Mail for light users by 40%!
–Est. Incremental revenue o...
Nordstrom: Queries with No
Matches
Julie Bornstein, Web Marketing Director
–What are my customers looking for and not
fin...
Nordstrom: Queries with No
Matches
Print Ad Campaign
Models happen to be sporting a
navel ring
Nordstrom does not sell ...
The early days of
mass auto
production
Today’s Auto:
It just works!
No need to understand what happens
when you turn on ignition
Very complex inside, but all sim...
Application Dates
Internships: Just launched
Contact : Faizan Chaudhary (faizan.chaudhary@barclays.com) and Tushar
Wadaska...
A Sampling of Technology Opportunities at Barclays:
We seek world-class technologists, data scientists, problem-solvers ,D...
88 Imperial College – DSI Distinguished Lecture– Copyright Usama Fayyad © 2014
Usama Fayyad
Faizan.chaudhary@barclays.com
...
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Usama Fayyad talk at IIT Madras on March 27, 2015: BigData, AllData, Old Data: Predictive Analytics in a Changing Data Landscape

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Title: BigData, AllData, Old Data: Predictive Analytics in a Changing Data Landscape

Abstract:
The landscape of the platform, access methodologies, shapes, and storage representations has changed dramatically. Much of the assumptions of a structured data world dominated by relational databases have been rendered obsolete. Today’s data analyst faces big challenges and a bewildering environment of technologies and challenges involving semi-structured and unstructured data with access methodologies that have almost no relation to the past. This talk will cover issues and challenges in how to make the benefits of advanced analytics fit within the application environment. The requirement for Real-time data streaming and in situ data mining is stronger than ever. We demonstrate how many of the critical problems remain open with much opportunity for innovative solutions to play a huge enabling role. This opportunity extends equally well to Knowledge Management and several related fields.

Published in: Data & Analytics
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Usama Fayyad talk at IIT Madras on March 27, 2015: BigData, AllData, Old Data: Predictive Analytics in a Changing Data Landscape

  1. 1. 1 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 BigData, Old Data, to AllData: Predictive Analytics in a Changing Data Landscape Usama Fayyad, Ph.D. Chief Data Officer – Barclays Twitter: @usamaf Talk at IIT Madras Chennai, India– March 27, 2015
  2. 2. 2 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Outline • Big Data all around us • The CDO role and Data Axioms • Some of the issues in BigData • Introduction to Data Mining and Predictive Analytics Over BigData • Case studies • Summary and conclusions
  3. 3. 3 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 What Matters in the Age of Analytics? 1.Being Able to exploit all the data that is available • not just what you've got available • what you can acquire and use to enhance your actions 2. Proliferating analytics throughout the organization • make every part of your business smarter • Actions and not just insights 3. Driving significant business value • embedding analytics into every area of your business can significantly drive top line revenues and/or bottom line cost efficiencies
  4. 4. 4 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Why Big Data? A new term, with associated “Data Scientist” positions: • Big Data: is a mix of structured, semi-structured, and unstructured data: – Typically breaks barriers for traditional RDB storage – Typically breaks limits of indexing by “rows” – Typically requires intensive pre-processing before each query to extract “some structure” – usually using Map-Reduce type operations • Above leads to “messy” situations with no standard recipes or architecture: hence the need for “data scientists” – conduct “Data Expeditions” – Discovery and learning on the spot
  5. 5. 5 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 The 4-V’s of “Big Data” • Big Data is Characterized by the 3-V’s: – Volume: larger than “normal” – challenging to load/process • Expensive to do ETL • Expensive to figure out how to index and retrieve • Multiple dimensions that are “key” – Velocity: Rate of arrival poses real-time constraints on what are typically “batch ETL” operations • If you fall behind catching up is extremely expensive (replicate very expensive systems) • Must keep up with rate and service queries on-the-fly – Variety: Mix of data types and varying degrees of structure • Non-standard schema • Lots of BLOB’s and CLOB’s • DB queries don’t know what to do with semi-structured and unstructured data.
  6. 6. 6 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Male, age 32 Lives in SF Lawyer Searched on from London last week Searched on: “Italian restaurant Palo Alto” Checks Yahoo! Mail daily via PC & Phone Has 25 IM Buddies, Moderates 3 Y! Groups, and hosts a 360 page viewed by 10k people Searched on: “Hillary Clinton” Clicked on Sony Plasma TV SS ad Registration Campaign Behavior Unknown Spends 10 hour/week On the internet Purchased Da Vinci Code from Amazon “Classic” Data: e.g. Yahoo! User DNA
  7. 7. 7 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Male, age 32 Lives in SF Lawyer Searched on from London last week Searched on: “Italian restaurant Palo Alto” Checks Yahoo! Mail daily via PC & Phone Has 25 IM Buddies, Moderates 3 Y! Groups, and hosts a 360 page viewed by 10k people Searched on: “Hillary Clinton” Clicked on Sony Plasma TV SS ad Spends 10 hour/week On the internet Purchased Da Vinci Code from Amazon How Data Explodes: really big Social Graph (FB) Likes & friends likes Professional netwk - reputation Web searches on this person, hobbies, work, locationMetaData on everything Blogs, publications, news, local papers, job info, accidents
  8. 8. 8 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 The Distinction between “Classic Data” and “Big Data” is fast disappearing • Most real data sets nowadays come with a serious mix of semi-structured and unstructured components: – Images – Video – Text descriptions and news, blogs, etc… – User and customer commentary – Reactions on social media: e.g. Twitter is a mix of data anyway • Using standard transforms, entity extraction, and new generation tools to transform unstructured raw data into semi-structured analyzable data
  9. 9. 9 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Text Data: The Big Driver • We speak of “big data” and the “Variety” in 3-V’s • Reality: biggest driver of growth of Big Data has been text data – Most work on analysis of “images” and “video” data has really been reduced to analysis of surrounding text Nowhere more so than on the internet • Map-Reduce popularized by Google to address the problem of processing large amounts of text data: – Many operations with each being a simple operation but done at large scale – Indexing a full copy of the web – Frequent re-indexing
  10. 10. 10 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 IT Log & Security Forensics & Analytics Automated Device Data Analytics Failure Analysis Proactive Fixes Product Planning Advertising Analytics Segmentation Recommendation Social Media Big Data Warehouse Analytics Cost Reduction Ad Hoc Insight Predictive Analytics Hadoop + MPP + EDW Find New Signal Predict Events 100% Capture Big Data Applications and Uses
  11. 11. 11 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 A few words on: The Chief Data Officer Why are companies creating this position?
  12. 12. 12 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Why a Chief Data Officer? • There is a fundamental realisation that Data needs to become a primary value driver at organizations • We have lots of Data • We spend much on it: in technology and people • We are not realising the value we expect from it • A strong business need to create the CDO role: • Traditional companies are not following, but adopting the model that actually works in other data-intensive industries • CDO has a seat at executive table: the voice of Data • Data done right is an essential element to unify large enterprises to unlock value form business synergies
  13. 13. 13 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Why data is important Every customer interaction is an opportunity to capture data, learn, and act Data capture/ opportunity to learn Actions instant car loan product offering is displayed in the app Connie logs into mobile App to check her balance. We know she is looking for a car loan CRM data is used to pre-calculate Connie’s borrowing limit for a car loan Internal and external data sources, predictive models identify cross- sell opportunities Connie is offered a competitively priced ‘bespoke’ offer for car servicing / MOT Experimenting with different tools for different groups of Connie gets a better user experience in real- time during every session personalised journey based on pre- calculated limit for the car loan amount Customer Interactions (branch visits, telephone calls, digital, mobile, sales) Big Data platform Customer Interaction CRM Predictive Analytics Multivariate Testing Targeted offers during browsing Product Discovery Cross-sell Measure feedback per session Millions of customer interactions per day
  14. 14. 14 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Fundamental Data Principles to Support Analytics Usama’s Obvious Data Axioms
  15. 15. 15 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Data Axioms 1. Data gains value exponentially when integrated and coalesced. – When fragmented: dramatic value loss takes place; – increased costs; – reduced utility/integrity; – and increased security risks
  16. 16. 16 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Data Axioms 2. Fusing Data together from disparate/independent sources is difficult to achieve and impossible to maintain Hence only viable approach is: • Intercepting and documenting at the source • fusing at the source • controlling lifecycle and flow
  17. 17. 17 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Data Axioms 3. Standardisation is essential • for sustained ability to integrate data sources and hence growing value; • for simplifying down-stream systems and apps • For enforcing discipline as a firm increases its data sources
  18. 18. 18 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Data Axioms 4. Data governance and policy must be centralised • needs to be enforced strongly else we slip into chaos and a Babylon of terms/languages • An Enterprise Data Architecture spanning structured and unstructured data
  19. 19. 19 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Data Axioms 5. Recency Matters  data streaming in modelling and scoring • Often, accuracy of prediction drops quickly with time (e.g. consumer shopping) • Value of alerts drop exponentially with time… • Ability to trigger responses based on real- time scoring critical • Streaming, real-time model updates, real- time scoring
  20. 20. 20 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Data Axioms 6. Data Infrastructure Needs: • rapid renewal & modernization: the pace of change and development of technology are very rapid – Design for migration and infrastructure replacement via abstraction layers that remove tech dependencies • Encryption and Masking: Persisting unencrypted confidential and secret data (even within secure firewalls) is an invitation for problems and risks
  21. 21. 21 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Data Axioms 7. Data is a primary competency and not a side-activity supporting other processes • Hence specialized skills and know-how are a must • Generalists will create a hopeless mess • Data is difficult: modelling, architecture, and design to support analytics
  22. 22. 22 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Reality Check So I am a marketer, how do I use BigData for my business? Social Media? Sentiment Analysis?
  23. 23. 23 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
  24. 24. 24 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
  25. 25. 25 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 © LUMA Partners LLC 2014 Gamification SOCIAL LUMAscape Social Data Community Platforms Social/Mobile Apps & Games Social Networks - Other Social Search & Browsing Social Commerce Platforms Analytics Social Promotion Platforms Social Publishing Platforms Social Marketing Management Twitter Apps Facebook Gaming Facebook Apps Content Curation URL Shorteners Stream Platforms Traditional Publishers Social Business Software Content Sharing (Reviews/Q&A/Docs) Image/Video Sharing Blogging Platforms External (Customer) Facing Internal (Employee) Facing Social Ad Networks Social Intelligence Social Scoring Social TV Social Referral M A R K E T E R C O N S U M E RSocial Shopping Social Advertising Platforms Social Login/Sharing Social Content & Forums Denotes acquired company Denotes shuttered company Advocate Platforms Social Branded Video
  26. 26. 26 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Reality Check So what should Users of Analytics in Big Data World Do?
  27. 27. 27 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Load the Data into a Data Lake Your life will be so much easier as you can now do Data Acrobatics an other amazing data feats…
  28. 28. 28 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 The Data Lake -- according to Waterline We loaded the Data! Congratulations Now What?
  29. 29. 29 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 From a Data Lake to Amazon Browser Amazon Simple Search Amazon gives you facets Product details
  30. 30. 30 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Reality Check So where do analysts and data Scientists spend all their time?
  31. 31. 31 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Data Analysis Vs. Data Prep Let’s Mine the Data
  32. 32. 32 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Reality Check So what do technology people worry about these days?
  33. 33. 33 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 To Hadoop or not to Hadoop? when to use techniquesrequiring Map-Reduce and grid computing? • Typically organizations try to use Map-Reduce for everything to do with Big Data – This is actually very inefficient and often irrational – Certain operations require specialized storage • Updating segment memberships over large numbers of users • Defining new segments on user or usage data
  34. 34. 34 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Drivers of Hadoop in Large Enterprises Cost of Storage • Fastest growing demand is more storage • Data in Data Warehouses have traditionally required expensive storage technology: –$100K per terabyte per year – cost of Teradata storage – $2.5K per terabyte – much lower per year – cost of Hadoop on commodity storage
  35. 35. 35 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Analysis & Programming Software PIG HIPI
  36. 36. 36 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Hadoop Stack
  37. 37. 37 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
  38. 38. 38 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
  39. 39. 39 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
  40. 40. 40 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
  41. 41. 41 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Reality: If Storage is Biggest Driver of Hadoop Adoption; What is the next biggest? ETL • Replaces expensive licenses • Much higher performance with lower infrastructure costs (processors, memory) • Flexibility in changing schema and representation • Flexibility on taking on unstructured and semi- structured data • Plus suite of really cool tools…
  42. 42. 42 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Turning the three Vs of Big Data into Value Understand context and content • What are appropriate actions? • Is it Ok to associate my brand with this content? • Is content sad?, happy?, serious?, informative? Understand community sentiment • What is the emotion? • Is it negative or positive? • What is the health of my brand online? Understand customer intent? • What is each individual trying to achieve? • Can we predict what to do next? • Critical in cross-sell, personalization, monetization, advertising, etc…
  43. 43. 43 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Many Business Uses of Predictive Analytics Analytic technique Uses in business Marketing and sales Identify potential customers; establish the effectiveness of a campaign Understanding customer behavior model churn, affinities, propensities, … Web analytics & metrics model user preferences from data, collaborative filtering, targeting, etc. Fraud detection Identify fraudulent transactions Credit scoring Establish credit worthiness of a customer requesting a loan Manufacturing process analysis Identify the causes of manufacturing problems Portfolio trading optimize a portfolio of financial instruments by maximizing returns & minimizing risks Healthcare Application fraud detection, cost optimization, detection of events like epidemics, etc... Insurance fraudulent claim detection, risk assessment Security and Surveillance intrusion detection, sensor data analysis, remote sensing, object/person detection, link analysis, etc...
  44. 44. Case Studies: 1. Context Analysis (unstructured data) 2. Yahoo! predictive modeling
  45. 45. 45 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Understanding Context
  46. 46. 46 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Reality Check So who is the company we think is best at handling BigData?
  47. 47. 47 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Biggest BigData in Advertising? Understanding Context for Ads
  48. 48. 48 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 The Display Ads Challenge Today What Ad would you place here?
  49. 49. 49 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 The Display Ads Challenge Today Damaging to Brand?
  50. 50. 50 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 The Display Ads Challenge Today What Ad would you place here?
  51. 51. CONFIDENTIAL Why did Google Serve this Ad? 51 this is how NetSeer actually sees this content NetSeer: SOLVING ACCURACY ISSUES | AMBIGUITY, WASTE, BRAND SAFETY 51
  52. 52. CONFIDENTIAL high MPG ford low emission fuel efficiency ECONOMY CARS economy vehicles microscope lenses reading glasses autofocus bifocal refraction VISION TOOLS eye chart focus groups A/B testing consumer study surveying blind study analytics MARKET RESEARCH ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ WEBSITE.COM ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ electric vehicles service record safety rating NetSeer – How it works 52 focus <CONCEPT> DISCERNS AND MONETIZES HUMAN INTENT + Identifies Concepts expressed on a page + Disambiguates language + Builds increasingly rich profile over 52M 2.3B CONCEPTS RELATIONSHIPS BETWEEN CONCEPTS
  53. 53. 53 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 NetSeer: Intent for Display • Currently Processing 4 Billion Impressions per Day
  54. 54. 54 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014 Problem: Hard to Understand User Intent Contextual Ad served by Google What NetSeer Sees:
  55. 55. CONFIDENTIAL http://diabetes.webmd.com/default.htm NETSEER CONNECTING THE DOTS 55 Understanding intent of user online actions and connecting them to define user intent profile over time
  56. 56. CONFIDENTIAL http://www.healthline.com/health/type -2-diabetes/insulin-pumps NETSEER CONNECTING THE DOTS 56 Understanding intent of user online actions and connecting them to define user intent profile over time
  57. 57. CONFIDENTIAL Search: Sulfonylureas NETSEER CONNECTING THE DOTS 57 Understanding intent of user online actions and connecting them to define user intent profile over time
  58. 58. Case Studies: 1. Context Analysis (unstructured data) 2. Yahoo! Predictive Modeling
  59. 59. Yahoo! – One of Largest Destinations on the Web 80% of the U.S. Internet population uses Yahoo! – Over 600 million users per month globally! Global network of content, commerce, media, search and access products 100+ properties including mail, TV, news, shopping, finance, autos, travel, games, movies, health, etc. 25+ terabytes of data collected each day • Representing 1000’s of cataloged consumer behaviors More people visited Yahoo! in the past month than: • Use coupons • Vote • Recycle • Exercise regularly • Have children living at home • Wear sunscreen regularly Sources: Mediamark Research, Spring 2004 and comScore Media Metrix, February 2005. Data is used to develop content, consumer, category and campaign insights for our key content partners and large advertisers
  60. 60. Yahoo! Big Data – A league of its own… Terrabytes of Warehoused Data 25 49 94 100 500 1,000 5,000 Amazon Korea Telecom AT&T Y!LiveStor Y!Panama Warehouse Walmart Y!Main warehouse GRAND CHALLENGE PROBLEMS OF DATA PROCESSING TRAVEL, CREDIT CARD PROCESSING, STOCK EXCHANGE, RETAIL, INTERNET Y! Data Challenge Exceeds others by 2 orders of magnitude Millions of Events Processed Per Day 50 120 225 2,000 14,000 SABRE VISA NYSE YSM Y! Global
  61. 61. Behavioral Targeting (BT) Search Ad Clicks Content Search Clicks BT Targeting ads to consumers whose recent behaviors online indicate which product category is relevant to them
  62. 62. Male, age 32 Lives in SF Lawyer Searched on from London last week Searched on: “Italian restaurant Palo Alto” Checks Yahoo! Mail daily via PC & Phone Has 25 IM Buddies, Moderates 3 Y! Groups, and hosts a 360 page viewed by 10k people Searched on: “Hillary Clinton” Clicked on Sony Plasma TV SS ad Registration Campaign Behavior Unknown Spends 10 hour/week On the internet Purchased Da Vinci Code from Amazon Yahoo! User DNA • On a per consumer basis: maintain a behavioral/interests profile and profitability (user value and LTV) metrics
  63. 63. How it works | Network + Interests + Modelling Analyze predictive patterns for purchase cycles in over 100 product categories In each category, build models to describe behaviour most likely to lead to an ad response (i.e. click). Score each user for fit with every category…daily. Target ads to users who get highest ‘relevance’ scores in the targeting categories Varying Product Purchase CyclesMatch Users to the ModelsRewarding Good BehaviourIdentify Most Relevant Users
  64. 64. Recency Matters, So Does Intensity Active now… …and with feeling
  65. 65. Differentiation | Category specific modelling time intensityscore time intensityscore IntenseClickZone Example 1: Category Automotive Example 2: Category Travel/Last Minute Different models allow us to weight and determine intensity and recency Alt Behaviour 1: 5 pages, 2 search keywords, 1 search click, 1 ad click Alt Behaviour 1: 5 pages, 2 search keywords, 1 search click, 1 ad click IntenseClickZone
  66. 66. Differentiation | Category specific modelling time intensityscore Intense Click Zone Example 1: Category Automotive Different models allow us to weight and determine intensity and recency with no further activity, decay takes effect Alt Behaviour 1: 5 pages, 2 search keywords, 1 search click, 1 ad click user is in the Intense Click Zone
  67. 67. Automobile Purchase Intender Example A test ad-campaign with a major Euro automobile manufacturer  Designed a test that served the same ad creative to test and control groups on Yahoo  Success metric: performing specific actions on Jaguar website Test results: 900% conversion lift vs. control group  Purchase Intenders were 9 times more likely to configure a vehicle, request a price quote or locate a dealer than consumers in the control group  ~3x higher click through rates vs. control group
  68. 68. Mortgage Intender Example We found: 1,900,000 people looking for mortgage loans. +122% CTR Lift Mortgages Home Loans Refinancing Ditech Financing section in Real Estate Mortgage Loans area in Finance Real Estate section in Yellow Pages +626% Conv Lift Example search terms qualified for this target: Example Yahoo! Pages visited: Source: Campaign Click thru Rate lift is determined by Yahoo! Internal research. Conversion is the number of qualified leads from clicks over number of impressions served. Audience size represents the audiencewithin this behavioralinterestcategorythat has the highestpropensitytoengagewithabrandorproductandto clickon anoffer.Date: March 2006 Results from a client campaign on Yahoo! Network Example: Mortgages
  69. 69. Experience summary at Yahoo! • Dealing with one of the largest data sources (25 Terabyte per day) • Behavioral Targeting business was grown from $20M to > $400M in 3 years of investment! • Yahoo! Specific? -- BigData critical to operations – Ad targeting creates huge value – Right teams to build technology (3 years of recruiting) – Search is a BigData problem (but this has moved to mainstream)
  70. 70. Lessons Learned A lot more data than qualified talent  Finding talent in BigData is very difficult  Retaining talent in BigData is even harder At Yahoo! we created central group that drove huge value to company Data people need to feel like they have critical mass  Makes it easier to attract the right people  Makes it easier to retain Drive data efforts by business need, not by technology priorities  Chief Data Officer role at Yahoo! – now popular
  71. 71. RapidMiner in Big Data Case Study
  72. 72. RapidMiner’s Strengths 7272 • Open Source Community & Marketplace – Crowd-sourced innovation, quality assurance, market awareness. • Fully-integrated Platform – Integrated, process-based business analytics platform with focus on predictive analytics. • No Programming Required – Easy-to-use, low maintenance costs, standard platform for business analysts. • Advanced Analytics at Every Scale – In-memory, in-database and in- Hadoop analytics offer best option for every size of database. • Connectivity – More than 60 connectors (incl. SAP & Hadoop), allowing easy access to structured and unstructured data.
  73. 73. 30,000+ Downloads per Month SELECT LIST OF RECIPIENT ORGANIZATIONS 7373 Government & DefensePharma & Healthcare Consulting Oil & Gas, Chemicals Financial ServicesSoftware & Analytics Retail Manufacturing Business Services Consumer ProductsAerospace Technology Entertainment Academia
  74. 74. 74 PayPal Who > world leading online payment services provider Solution > Customer feedback and voice of the customer analysis, churn prediction and prevention, text mining and sentiment analysis SmartSoft Who > provider of solutions for preventing fraud, money laundering, and risks in financial institutions Solution > Integration of Rapid-I’s predictive analytics engine into their solutions for fraud detection and fraud prevention for the financial and telecom sectors Select Customer Stories
  75. 75. SO WHAT IS THE BIG DATA STORY?
  76. 76. So the data is naturally moving to Hadoop... Situation: –The data is moving to Hadoop for Cost (storage) and Convenience (ETL) forces –How do we get the value of predictive analytics to the data? Rather than move the data out, move the analytics to the data! –Can we minimize the need for data movement? –Data copies can become a management nightmare –Analytics on a “Business As Usual” manner require convenience 76
  77. 77. Radoop – RapidMiner on Hadoop Opportunity: –Avoid expensive data movement –Leverage convenient data transformation –Thousands of data connectors, many over semi- structured and unstructured data Why is this big news? –Leverages a naturally occurring wave –Analytics over a richer variety requires much more processing –The energy placed on data extraction and loading moves to energy applied on actual analysis and modelling 77
  78. 78. Big Picture on Big Data Analytics Observations and Concluding Remarks
  79. 79. Retaining New Yahoo! Mail Registrants Often, Simple is Very Powerful!
  80. 80. Integrating Mail and News Data showed that users often check their mail and news in the same session –But no easy way to navigate to Y! News from Y! Mail Mail users who also visit Y! News are 3X more active on Yahoo –Higher retention, repeat visits and time-spent on Yahoo
  81. 81. “In the news” Module on Mail Welcome Page Increased retention on Mail for light users by 40%! –Est. Incremental revenue of $16m a year on Y! Mail alone
  82. 82. Nordstrom: Queries with No Matches Julie Bornstein, Web Marketing Director –What are my customers looking for and not finding? –June 2002: queries for “belly button rings” –returned no matches in store –Why the sudden interest?
  83. 83. Nordstrom: Queries with No Matches Print Ad Campaign Models happen to be sporting a navel ring Nordstrom does not sell navel rings What to do???
  84. 84. The early days of mass auto production
  85. 85. Today’s Auto: It just works! No need to understand what happens when you turn on ignition Very complex inside, but all simplicity on the outside
  86. 86. Application Dates Internships: Just launched Contact : Faizan Chaudhary (faizan.chaudhary@barclays.com) and Tushar Wadaskar (tushar.wadaskar@barclays.com)
  87. 87. A Sampling of Technology Opportunities at Barclays: We seek world-class technologists, data scientists, problem-solvers ,Data systems engineers-- the team that will re-invent Financial Services Amer Sajed CEO, Barclaycard US Bassel Ojjeh Chief Data Architect, DSI Simon Gordon MD, Head of Risk and Legal Technology - DSI innovating Data and Analytics solutions within the Financial Markets industry ; understand and influence the future of the global Derivatives market. Risk Technology within Barclays are looking for world-class big data and quantitative modeling skills. A great opportunity to work on some of the hardest technical problems in the industry by using open source to catch the bad guys stealing money all the way up to helping kids saving for their college We at Barclays are unleashing the power of disruptive technology. Innovation led by data and design keeping our customers at the heart of every product we build is our mantra. Come join the revolution. We have a challenge to quadruple our business here in the US - and that can only be delivered through analytics - in marketing, risk, fraud, and operations. And, Pune is the undisputed center of the universe! Faizan Chaudhary Director, Data Systems & Insights (DSI)
  88. 88. 88 Imperial College – DSI Distinguished Lecture– Copyright Usama Fayyad © 2014 Usama Fayyad Faizan.chaudhary@barclays.com Tushar.Wadaskar@barclays.com www.barclays.com/joinus Thank You! & Questions Twitter – @Usamaf

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