Strategic decision making using Big Data


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The country manager of Teradata India spoke at the QlikView Business Discovery World Tour in India on the growing importance of Big Data and the need for user-driven BI. View the presentation and how QlikView Business Discovery & Teradata are looking at Big Data & Business Discovery.

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Strategic decision making using Big Data

  1. 1. STRATEGIC DECISION MAKING USING BIG DATADinesh JainCountry Manager, Teradata India
  2. 2. Agenda• Big Data Current State and Challenges• Big Data Journey - How to start?• Big Data Scope – An Indian Perspective• Big Data Business Cases – BFSI – Telecom• Big Data Analytics – What should be the underlying architecture? – 3 Pillars of Big Data Analytics• What you need to do next…
  3. 3. Big Data – Current State and Challenges Confusion on business cases which are valid in an Indian scenario Clarity on types of BIG Data Lack of clarity on what is the right data architecture to follow for the best ROI Clarity on types of possible BIG Data technologies Lack of clarity on implementation options and what is the right model for you
  4. 4. Big Data Journey…How to start?The four commandments… 1 Become the first mover! If not, you will be already late… 2 Start small and identify convincing business4 cases Learn to let go of stuff that does not work 3 Once value is established… Expand !!
  5. 5. Big Data ScopeAn Indian Perspective • Telecom • Banking BIG Data scope 1. Network performance • Auto monitoring, • Insurance BIG Data scope 2. Fraud detection 3. Customer churn detection • Retail 1. 2. Customer 360 Anti-fraud and risk 4. Credit risk analysis 5. Micro- segmentation Analytics Maturity 3. Cards analytics 6. Social media analytics 4. Telematics BIG Data scope 5. Sentiment analytics 1. Web analytics 2. Recommendations Traditional analytics 3. Life cycle marketing Traditional analytics 1. Operational KPI’s 4. Sentiment analytics 1. Operational KPI’s 2. Handset analytics 2. Warranty analytics 3. Next best offers 3. Cross-sell/ up-sell 4. Usage analytics Traditional analytics 4. Regulatory reporting 5. Campaign management 1. Operational KPI’s 5. Forecasting 2. Trend analysis 6. Event based marketing 3. Claims analytics 4. Supply chain analytics 5. Regulatory reporting Low Medium High Sector Maturity
  6. 6. Big Data Business Cases
  7. 7. BFSI: Business Use Cases Customer 360 Customer’s transaction and interaction footprint Actions • FB: “My money transfer’s stuck for 3 days”, Finally my own house!”, “ I am getting married, yippee”, “Likes” Ritu • Offer a packaged account with Kumar, “Moving to Mumbai” • Linked in: Job change to VP @ Mumbai home loan and education loan @ • Twitter: “ Made my car down payment”, following HBS, relationship pricing Social Stanford, “ Hate service of XYZ bank” • Convert salary account to high Media save account • She complains that she has been provided an add-on card • Offer timely apology for issues/ without her applying for it grievances Call center • She complains about a fund transfer 3 times across • Offer car insurance at best rates last few days • Reverse any charges • Offer marriage planning services- • Her internet banking usage falls rapidly • Her regular salary credits stop happening offer from Ritu Kumar for marriage • She makes some high value transfers ensemble Internet • She makes a payment to enroll for GMAT • Offer gold @ special prices Banking • WEBLOGS • Offer vouchers @ xyz mallCustomer • Offer specialized help for home • She adds her dad as a payee loan transfer to your bank • She makes large ticket payments every month to a real • Offer concierge services for travel Mobile Banking estate firm bookings • Help her in finding apartment in Mumbai • Her card transactions are dropping fast • Help her get utilities in Mumbai • Her purchase behavior suddenly changes ATM/POS • She requests for FD liquidation • She checks if she can move her home loan to your bank Branch
  8. 8. BFSI: Business Use CasesSample output: Common paths to account closure Most Favored PathsEvents Outcome Multiple Fee Reversal and Viewing Product/Rates and Offers happens in the last mile for Account Closure. A list of such customers who are in the midst of displaying this pattern is prepared. This would be a business insight output from the Big Data analysis.
  9. 9. BFSI: Business Use CasesLocation, Behavior, Navigation Based Fraud Protection • Is there account activity happening across multiple channel locations in a specific time-span warranting further review? • Distance calculation between subsequent access locations and flagging off transactions which are physically impossible. E.g. first access from Hyderabad and second access from Russia in 5 minutes • Is there a sudden change in the customer’s buying behavior? • Customers buying pattern follows a ratio: • 30% Rental, 10% entertainment, 20% shopping and suddenly it changes to 80% shopping • Customer usually transacts in a certain set of shops, outlets and it suddenly changes to a different pattern • Is there an identifiable pattern of navigation that a customer follows and is a certain access very different from that pattern? • Customer logs in and clicks the account summary details link and then clicks funds transfer and or FD opening, etc. Suddenly the pattern changes to change mobile phone number, initiate transfer to new beneficiary, password change, etc.
  10. 10. BFSI: Business Use Cases Sample output: Graph Analysis for Fraud Detection & Prevention• Identify complex networks of relationships - Users to transactions - Transactions to means of payment - Identities to individuals• Fraud identified by graph relationships - Clusters of connections - Patterns of activity between connections• Understand impact of fraud - Trace flow of money and goods - Identify users impacted by fraud
  11. 11. Telecom: Business Use CasesSocial network analysis -Churn prevention through pattern recognition• Core Business Challenge – Negative experience with the core network product can spread through a subscriber’s network – Identifying the negative experiences and providing remedies is essential – Proactive and/or knowledgeable responses to at-risk customers can• Business Questions Answered – Which key subs are likely to churn? – Who within the subs network is also at risk?
  12. 12. Telecom: Business Use Cases Network System Optimization – Prediction of network quality• Core Business Challenge – Prediction of the next failure point is elusive – Network traffic patterns materially impact customer experience by impacting accessibility and retain ability• Business Questions Answered – What is the ‘normal’ condition of the network by geography and time? – What deviations from ‘normal’ constitute a threat to service?• Insights Gained – Analysis of massive data sets creates new benchmark or ‘fingerprint’ of network performance – Deviation patterns trigger immediate response or preventative actions – Previously unknown vulnerabilities informing network investment
  13. 13. Big Data Analytics : ARCHITECTURE ?
  14. 14. What is needed for effective discovery?UDA Unified Data Architecture : Addressing variety of big data sources • UDA is the only truly integrated analytics solution unifying multiple technologies that work behind the scenes in the travel value chain • UDA is a cohesive and transparent architecture that leverages best-of-breed and complementary values of data warehousing and data discovery and open source Hadoop • UDA represents a ‘room-to-grow’ approachDiscovery Analytics Innovation Analytics Cross Channel Purchases  Event Triggers Golden Path analysis  Marketing Integration Attrition Paths DISCOVERY INTEGRATED  Customer Behavior Analytics Fraudulent Paths PLATFORM DATA WAREHOUSE  Customer Segmentation Attribution analytics  Customer profitability Customer Sentiment Analysis  Customer valuation  Unique customer ID  Sessionization  Cross Platform Aggregation  BUT Also many new data types CAPTURE | STORE | REFINE EMAIL CUSTOMER FEEDBACK & USER SOCIAL COMPETITIVE CHANNEL FLT & MTCE AIRPORT CAMPAIGNS DATA GEN CONTENT MEDIA DATA DATA DATA OPERATIONS
  15. 15. The 3 Pillars of Effective Big Data Analytics Data Scientists Business Analysts Marketing Front-Line Workers Engineers Customers / Partners Executives Operational Systems Pillar 1:The storage LANGUAGES MATH & STATS DATA MINING BUSINESS INTELLIGENCE APPLICATIONS DISCOVERY INTEGRATED PLATFORM DATA WAREHOUSE Step 1: Data Capture • Store high volume Big Data from detail web interactions, mobile applications, search engines, industry competitive sources and social media in Hadoop “Cold” data store • Cleanse data and preprocess for CAPTURE | STORE | REFINE subsequent analysis EMAIL CUSTOMER FEEDBACK & USER SOCIAL COMPETITIVE CHANNEL FLT & MTCE AIRPORT CAMPAIGNS DATA GEN CONTENT MEDIA DATA DATA DATA OPERATIONS
  16. 16. The 3 Pillars of Effective Big Data Analytics Data Scientists Business Analysts Marketing Front-Line Workers Pillar 2: Engineers Customers / Partners Executives Operational SystemsThe Discovery Platform LANGUAGES MATH & STATS DATA MINING BUSINESS INTELLIGENCE APPLICATIONS Step 2: Discovery • Use Connectors for Hadoop to extract individual customer data from Hadoop into discovery platform • Use SQL-H to query less frequently used data in Hadoop DISCOVERY INTEGRATED • Use nPath to explore patterns to PLATFORM DATA WAREHOUSE determine consumer most frequent paths to purchase • Use Graph Analysis to identify strength of connection in social network • Analytics Portfolio contains 50 commonly used routines CAPTURE | STORE | REFINE EMAIL CUSTOMER FEEDBACK & USER SOCIAL COMPETITIVE CHANNEL FLT & MTCE AIRPORT CAMPAIGNS DATA GEN CONTENT MEDIA DATA DATA DATA OPERATIONS
  17. 17. The 3 Pillars of Effective Big Data Analytics Data Scientists Business Analysts Marketing Front-Line Workers Pillar 3: Engineers Customers / Partners Executives Operational Systems TheWarehouse Step 3: Innovation LANGUAGES MATH & STATS DATA MINING BUSINESS INTELLIGENCE APPLICATIONS • Move discovery results into using connectors • Use combination of SQL & SQL MR analytics to create new customer data analytic sets • Use increasingly rich 360o view of customer to drive continuous DISCOVERY INTEGRATED product innovation PLATFORM DATA WAREHOUSE  Create new marketing and social interactions using Event Based Management triggers  Initiate real-time and right time messaging, based on travel cycle or life cycle  Integrate expanding user CAPTURE | STORE | REFINE generated feedback, content, gamified participation, etc EMAIL CUSTOMER FEEDBACK & USER SOCIAL COMPETITIVE CHANNEL FLT & MTCE AIRPORT CAMPAIGNS DATA GEN CONTENT MEDIA DATA DATA DATA OPERATIONS
  18. 18. What you need to do next…
  19. 19. Thank you!