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.

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 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 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 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 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 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 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 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 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 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 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 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 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 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Fundamental Data Principles to
Support Analytics
Usama’s Obvious Data Axioms

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 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 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 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 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 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 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 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 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014

24. 24 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014

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 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Reality Check
So what should Users of Analytics in
Big Data World Do?

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 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
The Data Lake -- according to Waterline
We loaded the Data!
Congratulations
Now What?

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 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Reality Check
So where do analysts and data
Scientists spend all their time?

31. 31 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Data Analysis Vs. Data Prep
Let’s Mine
the Data

32. 32 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Reality Check
So what do technology people worry
about these days?

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 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 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Analysis & Programming Software
PIG
HIPI

36. 36 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Hadoop Stack

37. 37 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014

38. 38 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014

39. 39 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014

40. 40 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014

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 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 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. Case Studies:
1. Context Analysis (unstructured data)
2. Yahoo! predictive modeling

45. 45 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Understanding Context

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 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
Biggest BigData in Advertising?
Understanding Context for Ads

48. 48 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
The Display Ads Challenge Today
What Ad would you
place here?

49. 49 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
The Display Ads Challenge Today
Damaging to Brand?

50. 50 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
The Display Ads Challenge Today
What Ad
would you
place here?

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. 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 Keynote talk – DSAA 2014, Shanghai – Copyright Usama Fayyad © 2014
NetSeer: Intent for Display
• Currently Processing 4 Billion Impressions per Day

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. 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. 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. 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. Case Studies:
1. Context Analysis (unstructured data)
2. Yahoo! Predictive Modeling

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. 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. 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. 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. 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. Recency Matters, So Does Intensity
Active now… …and with feeling

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. 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. 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. 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. 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. 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. RapidMiner in Big Data
Case Study

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. 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
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. SO WHAT IS THE
BIG DATA STORY?

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. 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. Big Picture on Big Data Analytics
Observations and
Concluding Remarks

79. Retaining New Yahoo! Mail Registrants
Often,
Simple is Very Powerful!

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. “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. 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. 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. The early days of
mass auto
production

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. Application Dates
Internships: Just launched
Contact : Faizan Chaudhary (faizan.chaudhary@barclays.com) and Tushar
Wadaskar (tushar.wadaskar@barclays.com)

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 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