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Big data in marketing at harvard business club nick1 june 15 2013

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Practical Approach to BIG DATA and USE CASES in big data

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Big data in marketing at harvard business club nick1 june 15 2013

  1. 1. June 15, 2013 AXEOR and IBM Present… Capitalize on the Power of Big Data to Transform Marketing Presented by: Nick Kabra, Advisor to Axeor and IBM Big Data Practice
  2. 2. The Big Question about BIGGGGGG DATA:  BIG data is a big buzzword to make big money by solving seemingly BIG ….. PROBLEMS….  The 6Vs…. Can there be more…  Volume  Velocity  Variety © 2013 IBM Corporation 2  Veracity  Visualization  Value
  3. 3. 3 By BRUCE BARTLETT, The Fiscal Times June 14, 2013 Ever since former CIA employee Edward Snowden leaked information about the top secret PRISM program – a government system for monitoring vast amounts of electronic data – people have been asking what, exactly, the government does with all that data. Read more at http://www.thefiscaltimes.com/Columns/2013/06/14/Is-PRISMs-Big-Data-about-Big- Money.aspx#SuEj0mSZMJuXXFhw.99 NSA PRISM
  4. 4. 4 APPLICATIONS……
  5. 5. 5 •Big Data in Dodd Frank reporting- •5 years data to be saved, USI, LEI, UPI, UCI – no format. •Reproduce the data in specific time for authorities •How do you report to DCM, SEF, DCO •Different reporting formats for SEC, CFTC, FED and newly formed •Data to be sent to SDRs, SEF in different formats and fields •FATCA Dodd-Frank Act… Volcker… FATCA
  6. 6. 6 Insights for Valuation Building an investment recommendation platform Make investment recommendations and investment decisions Twitter and FB feeds, Hoovers online Equity /Bond research houses Kiplinger, the street, finviz, smartmoney, seeking alpha Bloomberg, Reuters, Telekurs, Telerate, Markit, Used Tableau, Pentaho, Platfora, Acunu, Elastic Search for filtering, heat maps, Pareto chart, cascading filters and co- relations. Find the outliers. Recommend to hedge funds in real-time. Used Drill.
  7. 7. 7 Trading, Risk, Regulatory Reporting Discovery-to-Decision Making using operational insights with minimal latency via visualization This program is a convergence of BIG data, data discovery, business intelligence and analytics. Implement a common trade and asset representation across all asset classes and functions. It includes end-to-end trade capture through risk management to the subledger as a “Single Source of Truth”. Architecting, Designing and Implementation using data collection, Hadoop, messaging, algorithms, analytics and visualization technologies. The END GOAL…
  8. 8. 8 Too Many/MUCH of EVERYTHING… BOMBARDMENT??? Too many Databases, too many technologies, too many tools, too many analytics methods… ?? ???
  9. 9. 9 SO WHERE DO YOU START Technical Requirements Consider the Cloud What hardware you need *Master Node and Secondary Master Node *Slave Nodes *Network, RAM, CPU, Application server, Power and utility costs etc What software will you need Unix system (Redhat, Ubuntu, CentOS etc.) JVM or JRE Apache Hadoop (packaged version from Cloudera, MapR, Hortonworks, Big Insights or plain vanilla NOSQL and Columnar database (from among the 150 odd) – Cassandra, MongoDB, Accumulo, Riak, neo4J, hypergraphDB, orientDB, Analytics DB– Teradata, netezza, greenplum etc MySQL database – for queries Analytics – Descriptive, predictive, prescriptive
  10. 10. 10 SO YOU Decided… NOW WHAT…. Scoping your Big DATA Engagement Identify the use case – Proof of Value Identify the team *You need senior management support – someone powerful *Decision makers and Business owner, budget *Line of Business- PM, Data owner, SME *Tech team-infrastructure head, hadoop admin, security, DB team, BA, PM, architect, developer, QA Identify the data sources Size the H/W, Cluster, Cloud, Replication etc. Stakeholders buy-in, project plan, evangelize, risk assessment Deploy –H/W, S/W, network, monitoring –Ganglia or Nagios, security, DB, BCP Collect Data – from data sources, connectors, push or pull, data aggregation, integration, Visualization -Use various tools or build your own(Make/buy), annotations, extensible, ease of use Analytics – Descriptive, predictive, prescriptive, A/B Deepen Insights- Go-live, Find outliers, drill deeper, iterations, interpret root causes, validate results Measure ROI – trends, performance, ROO, RONS
  11. 11. Example Problem: Marketing Campaign  Jane is an analyst at an e- commerce company  How does she figure out good targeting segments for the next marketing campaign?  She has some ideas… …and lots of data User profiles Transaction information Access logs
  12. 12. Traditional System Solution 1: RDBMS  ETL the data from MongoDB and Hadoop into the RDBMS – MongoDB data must be flattened, schematized, filtered and aggregated – Hadoop data must be filtered and aggregated  Query the data using any SQL- based tool User profiles Access logs Transaction information
  13. 13. Traditional System Solution 2: Hadoop  ETL the data from Oracle and MongoDB into Hadoop – MongoDB data must be flattened and schematized  Work with the MapReduce team to write custom code to generate the desired analyses User profiles Access logs Transaction information
  14. 14. Traditional System Solution 3: Hive  ETL the data from Oracle and MongoDB into Hadoop – MongoDB data must be flattened and schematized  But HiveQL queries are slow and BI tool support is limited – Marshaling/Coding User profiles Access logs Transaction information
  15. 15. What Would Google Do? Distributed File System Batch processing Interactive analysis NoSQL GFS MapReduce Dremel BigTable HDFS Hadoop MapReduce ??? HBase Build Apache Drill to provide a true open source solution to interactive analysis of Big Data
  16. 16. Why Apache Drill Will Be Successful Resources • Contributors have strong backgrounds from companies like Oracle, IBM Netezza, Informatica, Clustrix and Pentaho Community • Development done in the open • Active contributors from multiple companies • Rapidly growing Architecture • Full SQL • New data support • Extensible APIs • Full Columnar Execution • Beyond Hadoop Bottom Line: Apache Drill enables NoSQL and SQL Work Side-by-Side to Tackle Real-time Big Data Needs
  17. 17. 17 Vertical and Horizontal Domains
  18. 18. 18 Some Current Applications Some Current Applications Vertical Refine Explore Enrich Retail & Web • Log Analysis • Ad Optimization • Cross Channel Analytics • Social Network Analysis • Event Analytics • Dynamic Pricing • Session & Content Optimization • Recommendation Engines Retail • Loyalty Program Optimization • Brand and Sentiment Analysis • Dynamic Pricing/Targeted Offer Intelligence • Threat Identification • Person of Interest Discovery • Cross Jurisdiction Queries Finance • Risk Modeling & Fraud Identification • Trade Performance Analytics • Surveillance and Fraud Detection • Customer Risk Analysis • Real-time upsell, cross sales marketing offers Energy • Smart Grid: Production Optimization • Grid Failure Prevention • Smart Meters • Individual Power Grid Manufacturing • Supply Chain Optimization • Customer Churn Analysis • Dynamic Delivery • Replacement parts Healthcare& Payer • Electronic Medical Records (EMPI) • Clinical Trials Analysis • Supply Chain Optimizations • Insurance Premium Determination Page 2
  19. 19. 19 APPENDIX……
  20. 20. 20 Retail Banking Consumer Lending/ Card Services Commercial Banking Investment Banking/ Asset Management Insurance Market Mix Modeling Acquisition and Behavioral Scorecards Credit Risk Management Asset Liability Matching Pure Premium Modeling Customer Satisfaction & Experience Collections Management Stress Testing and Scenario Analysis Portfolio Optimization Price Optimization Management Credit Risk Management Market Research Analytics Market Research Analytics Interest Rate Risk Management Catastrophe Management & Reinsurance Channel Optimization Call Center Optimization Daily/Weekly Risk Measurement and Reporting VaR Modeling Loss Modeling Portfolio Stress Testing Web Analytics BASEL Compliance / Monitoring Regulatory Compliance Agency Incentive Compensation Customer Lifetime Value Fraud Management Tools Pricing Collateralized Debt Lapsation Modeling Cross-sell Targeting Banking
  21. 21. 21 Customer Acquisition CRM Marketing Risk Management Operations Fraud Control & Collection Card Services Product Feature Selection Customer Life Time Value Market Profiling & Sizing Behavioral Scorecard Staffing Analysis Collections Scorecard Merchant Performance Scorecard Product Pricing Offer Optimization / Customization Market Mix Optimization Risk Based Pricing ATM/Branch Positioning Delinquency and Roll Rate Analysis Merchant Fraud Modeling Acquisition Scorecard Customer Loyalty Tracking Market Spend Optimization Credit / Market Risk Evaluation Financial Reporting and Analysis Payoff / Foreclosure Analysis Merchant Acquisition Cross Sell Response Models Churn / Attrition Analytics Promotion Effectiveness Portfolio Health Tracker Channel Process Management Self-cure Propensity Analysis Merchant Retention Analysis Segmentation and Targeting Customer Experience / Value Customer / Brand Equity Portfolio Stress Testing Cross-channel Synergies Recovery Maximization Call Center Optimization Contact Strategies Optimization Survey Score Analysis Product Positioning Capital Allocation Investment Planning Fraud Pattern Recognition Customer Behaviour Analysis KPI Measurement & Reporting Reactivation / Silent Attrition Key Driver / Trigger Analysis Model Risk Management Asset Liability Management Fraud Detection / Investigation Framework Customer Lifetime Value Retail Banking
  22. 22. 22 Risk Management Research Data Management & Performance Reporting Marketing & CRM Portfolio Stress Testing & Risk Assessment End-to-end Market / Equity Research & Opportunity Identification Data Aggregation and Quality Assessment Customer Segmentation and Analysis Fraud Analysis Financial Analysis of Target Companies for M&A Performance Analytics and Reporting Segment Performance and Reporting Optimal Asset Allocation Strategy Financial Analysis of Assets and Portfolio Interactive Dashboards Segment P&L Analysis and Forecasting Servicing Rights Valuation Market and Industry Specific Analysis and Reporting Reporting and Analysis Support for End Customers Cross-sell / Up-sell Strategy Cash Flow Modeling And Forecasting Financial and Compliance Reporting Driver Analysis for Customer Satisfaction Instrument Valuation / Pricing Issue Resolution Workflows Corporate Banking
  23. 23. 23 Investment Banking
  24. 24. 24 Trade Services Data Management Record Keeping Corp. Actions Risk Management Reconciliation Compliance Reporting Slippage costs Pricing data analytics Tax reclaims Event monitoring VaR modeling Failed trades Trade surveillance Executive reports Security selection Reference data Managed accounts Unusual trade activity Asset class concentration Claims review Exeption management Trade reports Execution quality Market data analytics Cash Management Competitive intelligence Inventory management Cash movement Do Not trade lists Ad Hocs reporting Trade sizing decisions Trade data analysis Cross selling opportunities Risk attribution models Event driven monitoring Performance measurement Value add across the entire range of services Predictive work Reporting solutions Tax mgmt. Trade support Modal validation Handling big data sets Support services Cost effective reporting dashboards Handling big data sets Dashboards Architecture setup Cleaning functions Inquisitive analytics Reporting Text mining solutions Performance measurement Data monetization Scalability solutions Monitoring systems Internal fraud monitoring Claims handling Investment Banking Services
  25. 25. 25 Pricing / Risk Management Marketing Distribution Channels Underwriting Claims Management Investment Management Corporate Loss Modeling RFM Direct Response Marketing Product Selection Fraud Detection Fraud Detection Early Reinsurance Recoverable Tagging Yield Management Pure Premium Models Campaign Reporting Productivity Automated Underwriting Severity Forecasting Reinsurance Optimization Hedging Strategies Competitive Market Analysis Market Planning Production Forecasting Straight through processing Claims Staffing Optimization Asset Liability Matching Price Optimization Management Market Mix Modeling Goal-Setting Expense Management Productivity Analysis Portfolio Optimization Class Plan Development Advertising Lift Measurement Agency Segmentation Expense Management Customer Lifetime Value Performance Measurement Loss Cost Driver Analysis Customer Satisfaction & Experience analytics Claims Customer Satisfaction Analysis Insurance

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