Large Scale Data Analytics
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Presentation to research scholars at SSN College Of Engineering during Research Scholars Symposium
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Large Scale Data Analytics
- 1. Large Scale Data Analytics Shankar Radhakrishnan shankar.r3@cognizant.com linkedin.com/in/connect2shankar
- 2. Scenario • Insurer uses meteorological data for pricing model • At present data from 2000 weather stations are collected for analysis • Plan is to use 10,000 weather station data ( or more ) • Stochastic simulation needs to run to ID pattern in weather data, to determine pricing • Volumetric : peta-bytes of information ( for 1 region ) 2
- 3. Trends 3
- 4. Data Analytics Is Mostly About $$, Customers, Markets 4
- 5. How Widespread Is Data Analytics? 5
- 6. Expectations On Payback Period ( Aggressive ) 6
- 7. Large Scale Data Analytics 7 “Involves using different algorithms, distributed platforms, tools and techniques to analyze big data and provide actionable insights”
- 8. Big Data “ Data sets that are very large in volume and complex “ 8 New platforms, tools and techniques have emerged to manage Big Data We broke away from traditional ways to process and analyze them
- 9. Data Structures Vector, Matrix, Or Complex Structure Free Text Image or Binary Data Data “bags” Iterative Logic Or Complex Branching Advanced Analytic Routines Rapidly Repeated Measurements Extreme Low Latency Access to all data required Search Ranking X X X X X X Ad Tracking X X X X X X X X Location or Proximity Tracking X X X X X Social CRM X X X X X X X Document Similarity Testing X X X X X X X X Genomic Analysis X X X X X Customer Cohort groups X X X X X X Fraud Detection X X X X X X X X X Smart Utility Metering X X X X X X Churn Analysis X X X X X X X Satellite Image Analysis X X X X Game Gesture Analysis X X X X X X X X Data Bag Exploration X X X X X X 9
- 10. Business Interests : Well Informed Customer Executive 10 Speech to Text Conversion Voice Data Unstructured data Analytical System Customer Persona • Customer Persona - Demographics, Top interactions, Channel Preferences, Dissatisfies • Customer Lifetime Value • Recent Contact History • Customer Sentiment & Trend during the call Customer’s state of mind Sentimental Analysis Social media Depositions Complaints Other Channel information (ATM, Branch) Big Data Warehouse Traditional Warehouse Decision Engine • Customer Executive Dashboard presents all intelligence required to make a decision • The decision engine also presents important decisions to be taken for the particular customer issue
- 11. Well Informed Customer Executive… Customer calls BankingCallCenter Executive understands the customerproblemExecutive authenticates customer and pulls up CustomerPersona Executive reviews risk of attrition against Customer LifetimeValue Executive reviews Last 5 call center and banking transactions Executiveviews customer’s state of mind (riskof attrition )through a barometer chart Analytical Solution - Converts Speech to textAnalytical engine listens to customer voice Suggested top 5 Actions required DecisionEngine Executive performs below actions based on his analysis and recommendations from Decision engine 1. Reversal of overdraft fee 2. One time fee waiver on Cheque book (predicting customer need based on historic usage cycles ) 3. Cash back Reward card for a minimum spend of $X through debitcard 4. Offer interest revision for investment products or mortgage 5. Promote new mutual funds or credit cards based on customer willingness Analytical engine monitors sentiment Executive analyzes Customer Persona (demographic / Preferences / Satisfiers / dissatisfiesetc ) 11
- 12. Business Interests : Fraud Prevention 12 Envisaged Benefits ▪ New fraud patterns can be identified by building ‘analytical models’ to run against historical data ▪ ‘Web crawling’, ‘Contextual text analysis’, ‘Natural Language Processing’ allows fraud behavior identification from social media. It may increase Fraud detection success rate ▪ ‘Real time’ models to capture behavioral patters and do pattern analysis against History data to evaluate Fraud case validity. The model learns by self and updates ‘Fraud pattern master sets. ▪ Brings ‘artificial intelligent’ fraud pattern detection and analysis ▪ ‘Real time’ (in the order of .5-1 minute refresh rate) alerts to Fraud analysts about ‘self learned’ fraud patterns based on new customer behavior patterns Big Data Usage ▪ Formation of key value groups to the order of XcY (where X no. of attributes that are relevant to Fraud and Y is no. of attributes that should be combined to identify patterns) ▪ High speed history data loading from source systems ▪ Efficient Real time fraud detection by identifying patterns through customer behavioral events and processing them over X yrs. of history data – e.g. using HBase Scenario Formation of Fraud pattern reference tables using ▪ Real time data coming from different departments like IVR, WEB, Customer profile, Transactions etc ▪ Real time Mining and analysis of history data to form prior patterns (no. of years in range to 50-100 TB)
- 13. Fraud Pattern Detection… 13 Legacy Fraud Data Customer Profile Data IVR Audio Data Web / Online Card Transaction Data Fraud Pattern Master Table Fraud Analyst History Data Processing to determine Fraud Patterns over X years Real-time Customer Behavior Analysis for Fraud Detection Customer Behavior Change Events Customer Behavior Change Events Customer Behavior Change Events Real time Analysis of behavior patterns over historical data Real time update to Master Table on New Fraud Patterns Real time alert to Fraud Analyst RDBMS RDBMS (JSON Files) RDBMS Customer Behavior Change Events
- 15. Benefits 15 BenefitsIndustry Financial services ▪ Customer Insights – Integrating Transactional data (CRM/Payments) and unstructured Social feeds ▪ Regulatory Compliance – Risk exposures across asset classes, LOBs and firms ▪ Fraud Detection in Credit Cards & Financial Crimes (AML) in Banks Travel, Hospitality & Retail ▪ Customer centricity – Customer behavior analysis from Omni channel retailing & Social feeds ▪ Markdown Optimization – Improve markdown based on actual customer buying patters ▪ Market basket analysis – Narrow down market basket analysis by demographics Life Science ▪ Improve targeting & predictions – Automatic Detection of Adverse Drug Effects (ADEs) ▪ Patient data analysis – Longitudinal Patient Data (LPD) analysis ▪ Predictive Sciences – Analyze Preclinical Side Effect Profiles of Marketed Drugs Healthcare (Payers & Providers) ▪ Cost of Care – Drug effectiveness & Cost of Care Analysis based on electronic Health Records (EMR) ▪ Self Service Healthcare – Increase in mHealth & eHealth to allow consumer access to health information ▪ Claims Analytics – Analyze insurance claims data for fraud detection & preferred treatment plans Communication, Media & Entertainment ▪ Discover churn patterns based on Call data records (CDRs) and activity in subscribers’ networks ▪ Digital Asset Management (DAM) – Analyze & capitalize digital data assets Manufacturing ▪ Proactive Maintenance & Recommendation – Sensor Monitoring for automobile, buildings & machinery ▪ Energy Efficiency – Leveraging Smart meters for utility energy consumption ▪ Location or Proximity Tracking – Location based analytics using GPS Data Hi-Tech ▪ Extend and complement conventional information supply chain with big data path ▪ Predictive analysis and real time decision support
- 16. Hadoop 16
- 17. Hadoop - HDFS 17
- 21. Hadoop 21
- 28. Analytics - Trends • Big Data Analytics In The Cloud • AWS, AWS-Redshift • Hadoop • Enterprise Data Operating System • Data Analytics Platform • SQL on Hadoop • NoSQL • IoT ( Internet of Things ) 28 • Multi-polar Analytics • Predictive Analytics ( Spark ) • In-memory Analytics • Data Lake • Deep Learning • Machine Learning • Neural Networks • Data Monetization
- 29. Q & A
- 30. Thank You ! “Any Sufficiently Advanced Technology Is Indistinguishable From Magic “ - Arthur C. Clarke