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Itag usama bigdata-6-2015-full

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Usama Fayyad keynote talk at iTAG Meeting in London

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Itag usama bigdata-6-2015-full

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 34 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015 Analysis & Programming Software PIG HIPI
  35. 35. 35 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015 Hadoop Stack
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  40. 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. 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. 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. 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. 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. 45 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015 Biggest BigData in Advertising? Understanding Context for Ads
  46. 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. 47 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015 The Display Ads Challenge Today Damaging to Brand?
  48. 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. 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. 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. 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. 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. 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
  54. 54. 54 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015
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  69. 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. 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. 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. 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. 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. 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. 75 talk at iTAG Meeting, London, June 4, 2015 – Copyright Usama Fayyad © 2015 Concluding Thoughts
  76. 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. 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

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