Usama Fayyad gave a talk at the iTAG Meeting in London on June 4, 2015 about big data and the role of the Chief Data Officer. He discussed how data is growing exponentially in volume, velocity, and variety. Fayyad also outlined some fundamental data principles, or "axioms", including the need for data integration, standardization, centralized governance, and recency. Additionally, he noted that while loading data into a data lake seems promising, analysts actually spend most of their time preparing and cleaning data rather than analyzing it.

1. 1 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Big Data and Its New Role
Usama Fayyad, Ph.D.
Chief Data Officer
CIO Risk, Finance, & Treasury Technology
Barclays
June 4, 2015
Twitter: @usamaf
iTAG Meeting 2015
London– June 4, 2015

2. 2 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Outline
• Big Data all around us
• The CDO role and Data Axioms
• Some of the issues in BigData
• Case studies
• Context and sentiment analysis
• A case-study on IOT
• Barclays mini case studies in FinCrime, Treasury,
and Risk & Finance
• Concluding Thoughts

3. 3 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
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 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
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

5. 5 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Central Data Fusion
Engine
Ingesting, persisting,
processing
and servicing in Real-time.
Analysis
Data Fusion: can we bring all data together
for analysis and action?
TransactionsSocial Data
Trade Data
Application Logs Network Traffic
RiskMarketingFinancial CrimeFraudCyber Security
Customer and Client
Interactions

6. 6 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
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

7. 7 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
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.

8. 8 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
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

9. 9 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
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

10. 10 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
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

11. 11 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
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

12. 12 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
A few words on:
The Chief Data Officer
Why are companies creating this
position?

13. 13 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
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

14. 14 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Fundamental Data Principles to
Support Analytics
Usama’s Obvious Data Axioms

15. 15 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
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 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
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 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
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 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
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 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
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 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
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 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
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 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Impact of bad Data Governance and what we are doing
A dedicated, central data governance team,
plus a virtual community across BU’s
A group-wide data policy, which applies to all
Barclays business units globally and
mandates
A COO-level governance committee, with
representation from all BU’s and links into
governance structures within all BU’s
Standards for data such as Customer data
quality that are driving consistency across all
parts
of Barclays
Data quality improvement programmes that
are driving multiple £millions in business
benefits
No-one owns the problems, so no-one takes
any action to make things better
Differing data policies and definitions across
the organisation = confusion & financial impact
Inconsistent approaches to resolving issues
makes the problems more messy across
business areas
Bad data driving inconsistency throughout
organisations with unpredictable and
potentially significant financial impact
Organisations don’t understand or buy in to
the value of data and suffering the financial
implications

23. 23 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Driving the need for integrated processes, data & architecture
Regulatory Demand
 Data quality, completeness, lineage and
aggregation
 Company financial reporting presented at
most granular level through various
lenses: business units, legal entities,
regional, sector level, …
 Faster turnaround for increasingly
complex ad hoc data requests
 Additional sophisticated stress tests
delivered in a joined-up manner between
Risk and Finance
 Enhanced hybrid capital computation
methodologies
Business Drivers
 Better and smarter controls
 Capital precision
 Better customer experience
 Better colleagues experience
 More cost effective

24. 24 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Data Governance is BORING for most senior execs
Policy, governance
& control
Business ownership
& data stewardship
Definitions &
metadata
Permit to build
(change governance)
Reporting &
analytics
Data architecture
Data sourcing
Reconciliation
Data quality
management
Documentation,
tracking & audit
…but it shouldn’t be!

25. 25 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Reality Check
So what should Users of Analytics in
Big Data World Do?

26. 26 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
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…

27. 27 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
The Data Lake -- according to Waterline
We loaded the Data!
Congratulations
Now What?

28. 28 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
From a Data Lake to Amazon Browser
Amazon Simple Search
Amazon gives you facets
Product details

29. 29 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Reality Check
So where do analysts and data
Scientists spend all their time?

30. 30 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Data Analysis Vs. Data Prep
Let’s Mine
the Data

31. 31 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Reality Check
So what do technology people worry
about these days?

32. 32 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
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

33. 33 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
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:
–$20K to $50k per terabyte per year – cost
of premium storage
– $2K per terabyte – much lower per year –
cost of Hadoop on commodity storage

34. 34 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Analysis & Programming Software
PIG
HIPI

35. 35 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Hadoop Stack

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40. 40 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
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…

41. 41 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Turning the 3-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…

42. 42 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
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 credit worthiness of a customer requesting a loan,
secured and unsecured loans
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...
Application Log File Analytics Understanding application, network, event logs in IT

43. 43 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Case Studies:
1. Context Analysis (unstructured data)
2. Internet of Things (IOT)
3. Three use cases at Barclays

44. 44 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Reality Check
So who is the company we think is
best at handling BigData?

45. 45 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Biggest BigData in Advertising?
Understanding Context for Ads

46. 46 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
The Display Ads Challenge Today
What Ad would you
place here?

47. 47 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
The Display Ads Challenge Today
Damaging to Brand?

48. 48 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
The Display Ads Challenge Today
What Ad would
you place here?

49. 49 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Why did Google Serve
this Ad?
49
this is how NetSeer
actually sees this
content
NetSeer: SOLVING ACCURACY ISSUES | AMBIGUITY, WASTE, BRAND
SAFETY
49

50. 50 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
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
50
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

51. 51 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
NetSeer: Intent for Display
• Positive versus negative content re a particular topic

52. 52 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Problem: Hard to Understand User
Intent
Contextual Ad served by Google What NetSeer Sees:

53. 53 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Case Studies:
1. Context Analysis (unstructured data)
2. Internet of Things (IOT)
3. Three use cases at Barclays

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69. 69 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Case Studies:
1. Context Analysis (unstructured data)
2. Internet of Things (IOT)
3. Three use cases at Barclays

70. 70 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
UseCase 1: Why does a banks Treasury
department need “Big Data” or Data as a Service?
Treasury are major consumer of data from across the Barclays group
 A large proportion of Treasury’s change portfolio involves data sourcing activity - ‘mining &
refining’ from across Risk, Finance and Front Office origination of all of the firms business
lines and legal entities.
Treasury’s data infrastructure is complex and grows more so as each project
reaches completion
 Treasury alone “use and consume” 2,000 different pieces of data and we presently
source this from more than 100 separate systems, via 350 individual feeds
This complexity has an organisational cost
 It is estimated that on larger projects, data sourcing represents 25-50% project budgets.
“Mining and Refining” is a throttle to our investment.
 This gets harder as we move through the Structural Reform agenda.

71. 71 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Solvingfor these challenges is more than the
data…it is a service
 A data service for Treasury comprises the technology platform (i.e. Big Data and Hadoop), data quality, organisation
(people) and processes, to enable efficient collection and effective management of data sourced from clusters and
our own balance sheet data, to fulfil Treasury’s data demands
 This HAS to bear coherent aggregation across all of our disciplines and with our partners in Risk and Finance.
Regulators globally are placing close scrutiny on this as they examine our stress and regulatory returns.
 There are already firms who are “technically failing” stress tests because of these data challenges, leading to
prohibitive Capital and Liquidity add on requirements. Getting this right is not an option.
Organisation Data
Technology
Platform
Processes
Data
Service
What data should this service manage for Treasury:
i. Source and supply business data to all Treasury functions
ii. House Treasury’s own balance sheet data
iii. Source reference and market data from outside Treasury; collect
and manage our own reference data and rules
iv. Provide a central repository for storing data created by Treasury
functions – for input to other Treasury engines and reporting
A service is more than just a database:
i. A dedicated organisation with clearly defined processes and
tools is needed to source, manage and provide the data
consumed by Treasury
ii. Facilitate the consolidation of mechanical and repeatable data
operations into a central function
iii. Enable a reporting service, consisting of a best in class reporting
platform, to provide Treasury with easy access to our own data
and simplified reporting

72. 72 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
SystemLayer
Big Data Platform (Hadoop - Scoop, Scala, Hive, Hue), SAS, Tableau, Solr, etc
Single Sanctions
Payment View
Centralised Watch
List Depository
Single Customer
View Centralised
SAR Depository
Enabling a holistic
view of Financial
Crime Risk
Enhanced
Financial Crime
Intelligence
capabilities
Data&Analytics
Layer
Customers
(Due Diligence, ID&V,
Accounts, Risk
Rating)
Transactions
(Wire Transfers,
Checks, Trades)
Payments
(Cross-Border,
Domestic, IBAN,
Internet)
Intelligenc
e
Court Orders
Fraud Data
Use Case 2: Financial Crime Intelligence - Big Data in Action
• Statistical & Threshold-based
alerting
• Early warning & notification
• Near repeat pattern analysis
• Load forecasting and cyclical
patterns
• Behavioral targeting
• Ability to handle billions of
records
• Trending and intelligence
dashboards
• Real time access to data
• “information fabric” integrates
heterogeneous data and content
repositories
• Terrorism Activity
• Human & Drug Trafficking
• Standard Financial Crime Reports
(SAR, STR, CTR)
• Sanctions Circumvention
• FinCEN & other Regulatory Reports
• Law Enforcement, Industry Forums
• Fraudulent
transactions
• Hawala
• Money Laundering
• E-Commerce
Fraud
• Online Bank Theft
• Inside Trading
Sanctions, ABC &
AML
Policies and
Standard
Regulatory Agencies
and Law Enforcement

73. 73 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Big Data Analytics – Proactive mitigation measure
to FinCrime
• Examining Large amount of data – billions of records
• Appropriate information
• Identification of hidden patterns, unknown
correlations
• Competitive advantage
• Better business decisions : strategic and operational
Deeper Analytics
• Analyse billions of records across
various business units
• “information fabric” integrates
heterogeneous data and content
repositories
• Develop Financial Crime
intelligence that leads to the
identification of risks across crime
areas
Different Types of Analysis
• Mapping – spatial / temporal
densities
• Trending and intelligence dashboard
• Statistical & Threshold-based
alerting
• Early warning & notification
• Near repeat pattern analysis
• Load forecasting and cyclical
patterns
• Behavioral targeting
Faster Interaction
• Real time access to data
• Through intuitive workflow
• Reduced dependency on IT
• Speed in reproducing and sharing
analysis
• High-performance, scalable
analytics
• Portable across enterprise
platforms
• Rapid Data Gathering Accelerates Analytics Impact
• Query Optimization for Timely Business Insight
• Data Federation Provides the Complete Picture
• Data Discovery Addresses Data Proliferation
• Data Abstraction Simplifies Complex Data
• Analytic Sandbox and Data Hub Options Provide Deployment
Flexibility
• Data Governance Maximizes Control
• Layered Data Virtualization Architecture Enables Rapid
Change
Centralised
Watch List
Depository
Enabling a
holistic view of
Financial
Crime Risk
Single Customer
View Centralised
SAR Depository
Single
Sanctions
Payment
View

74. 74 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Use Case 3: Big Data and IFRS9
Admin & Control
Functions
Shared Data Platform
Risk & P&L
Financial
Reporting
FCCredit RiskMarket RiskPC
IBWealthCorporateCardsRBB
Price TestingReporting Reporting VaR Calc
CA, CL,
HF, BB,
Risk
Personal,
Corporate,
Risk
Trades,
Vals,
Risk
Trades,
Vals,
Risk
Trades,
Vals,
Risk
Reporting MCCalc RWA CalcReporting
Re-Run
Explain
PnL Posting
Reference Data
Sources
Market Risk
Trades
Credit Risk
Trial Balance
Instrument Static
Netting & Collateral
Book Hierarchy
Africa
Trades,
Vals,
Risk
IFRS
IFRS
Reporting Calcs
Loans
validate
Adjust
TheRiskandFinanceBig DataPlatformwill drive;
• Optimisation and Integrity across Risk, Finance&Treasurydata and processes
• Processes runoff single set of standardised andconsistent data
• Shared processes leveraging commonplatforms
• Maximising efficiencyand quality of execution
• IntegratedReporting and Business Partneringservices
• End to end process and control framework

75. 75 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Concluding Thoughts

76. 76 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Where does this leave us?
• What matters in the Age of Analytics?
1. Being Able to exploit all the data that is available
2. Proliferating analytics throughout the organization
3. Driving significant business value
• Where are we today?
1. New Data Landscape via Hadoop
2. Confusion by marketers, analysts, and technical community
3. Struggling with basics of managing data because of the new
flexibility
• What are the real issues?
1. Data management and governance
2. Talent for Data and Data Science is rare but critical
3. Data and Analytics are specialisms that need management
and know-how: not for generalists…

77. 77 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
Usama Fayyad
Usama.Fayyad@barclays.com
www.barclays.com/joinus
Thank You! & Questions
Twitter – @Usamaf