Big Data: Del Mito a la Realidad


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Presentación Leandro Ruiz, Director Preventa Regional para CLA en Teradata en el 14º Congreso Internacional de Tecnología para el Negocio Financiero.
2 y 3 de julio de 2014

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Big Data: Del Mito a la Realidad

  1. 1. Leandro Ruiz Teradata BIG DATA: FROM HYPE TO REALITY
  2. 2. 2 7/14/2014 Teradata Confidential What Is BIG DATA?
  3. 3. 3 7/14/2014 Teradata Confidential BIG DATABIG DATA WEBWEB PetabytesPetabytes CRMCRM TerabytesTerabytes GigabytesGigabytes ERPERP ExabytesExabytes INCREASING Data Variety and ComplexityINCREASING Data Variety and Complexity User Generated Content Mobile Web SMS/MMS Sentiment External Demographics HD Video Speech to Text Product/ Service Logs Social Network Business Data Feeds User Click Stream Web Logs Offer History A/B Testing Dynamic Pricing Affiliate Networks Search Marketing Behavioral Targeting Dynamic Funnels Payment Record Support Contacts Customer Touches Purchase Detail Purchase Record Offer Details Segmentation DECREASING Value Density in the DataDECREASING Value Density in the Data Big Data: From Transactions to Interactions Behavioral Analytics ALL DATA
  4. 4. 4 7/14/2014 Teradata Confidential New Data Structures Across Industries Finance:
  5. 5. 5 7/14/2014 Teradata Confidential Flow DATA -> INSIGHTS -> ACTIONS Big Data is an Evolution not a Revolution Flow BIG DATA -> INSIGHTS -> ACTIONS Predictions Events Patterns Hypothesis Testing Strategic Actions Operational Actions Is the Ultimate USE of Big Data Different? No.
  6. 6. 6 7/14/2014 Teradata Confidential Analysts Recommend: Shift from a Single Platform to an Ecosystem “We will abandon the old models based on the desire to implement for high-value analytic applications.” "Logical" Data Warehouse
  7. 7. 7 7/14/2014 Teradata Confidential Discovery Platform Data Warehouse/ Business Intelligence Advanced Analytics The Problem Proliferation of advanced analytics environments has resulted in fragmented data, higher costs, expensive skills, longer time to insight
  8. 8. 8 7/14/2014 Teradata Confidential Discovery Platform The Problem Integrated Discovery Platform (IDP) Data Warehouse/ Business Intelligence Advanced Analytics The Solution Proliferation of advanced analytics environments has resulted in fragmented data, higher costs, expensive skills, longer time to insight Integrated discovery analytics provides deeper insight, integrated access, ease of use, lower costs, better insight SQL Framework Access Layer Pre-Built Analytics Functions
  9. 9. 9 7/14/2014 Teradata Confidential Discovery Platform Requirements 1 2 3 4 All Data Multiple Analytic Methods Diverse Enterprise Analysts Rapid Exploration
  10. 10. 10 7/14/2014 Teradata Confidential What do you want to discover?Recommend Analysis Influence Analysis Website Analysis Satisfaction Metrics BusinessValue TX Data IX Logs Review Text Social Graph Events Time Emails Text All Data: Web Analytics
  11. 11. 11 7/14/2014 Teradata Confidential Predictive Analytics Influencer Analysis (6X) Percentile Analysis Churn Analysis Behavioral Analysis (+25%) BusinessValue Statistics Path and Time Text SQL Graph Text Better Predictive Results Multiple Analytic Methods: Attrition
  12. 12. 12 7/14/2014 Teradata Confidential Diverse Enterprise Analysts Business Analysts Apps Data Scientists and Analysts SQL, BI Tools Developers Workbench, IDE, Library
  13. 13. 13 7/14/2014 Teradata Confidential Rapid “Iterative” Exploration Data Scientist Business User Rapid Exploration Discovery More and Fail Fast ? Data Acquisition 1 Data Preparation 2 Visualization 4 Analysis 3
  14. 14. USE CASES
  15. 15. 15 7/14/2014 Teradata Confidential Customer Interactions Across Multiple Channels Teller Withdrawal Teller ComplaintATM Deposit Online Transfer Cancel accountEmail Complaint Call Center Inquiry
  16. 16. 16 7/14/2014 Teradata Confidential What if you had a 360 degree view of all interactions you are having with the customer and could proactively identify high value customers at risk of leaving in the next 5 days?
  17. 17. 17 7/14/2014 Teradata Confidential • Customerize – Identify the customer in the data • Sessionize – Identify the session occurrence in time • Sojournize – Stich together sessions to recreate cross-channel journey 07:05:32 09:20:23 09:25:32 11:05:48 1:05:06 1:35:12 1:42:58 1:45:14 3:05:58 4:15:22 Omni-channel Customer Journey
  18. 18. 18 7/14/2014 Teradata Confidential Events Preceding Account Closure
  19. 19. 19 7/14/2014 Teradata Confidential Finding Signal in the Noise SELECT * FROM npath ( ON ( SELECT … WHERE u.event_description IN ( SELECT aper.event FROM attrition_paths_event_rank aper ORDER BY aper.count DESC LIMIT 10) ) … PATTERN ('(OTHER|EVENT){1,20}$') SYMBOLS (…) RESULT (…) ) ) n; Interactive Analytics Reducing the “Noise” to find the “Signal”
  20. 20. 20 7/14/2014 Teradata Confidential Finding Signal in the Noise SELECT * FROM nPath ( ON (…) PARTITION BY sba_id ORDER BY datestamp MODE (NONOVERLAPPING) PATTERN ('(OTHER_EVENT|FEE_EVENT)+') SYMBOLS ( event LIKE '%REVERSE FEE%' AS FEE_EVENT, event NOT LIKE '%REVERSE FEE%' AS OTHER_EVENT) RESULT (…) ) n; Reducing the “Noise” to find the “Signal” Fee Reversal seems to be a “Signal”
  21. 21. 21 7/14/2014 Teradata Confidential Delivering Outstanding Customer Experiences What if I knew that this customer was likely to leave? One could… • Apologize • Offer an explanation • Reverse the $5 fee Jan 5: Reverse Fee Request Jan 10: Request Made Again Jan 15: Request Made AgainJan 7: Request Made Again Jan 20: Account Closed
  22. 22. GRAPH ANALYSIS Enhancing Churn Prediction
  23. 23. 23 7/14/2014 Teradata Confidential Social Networks & Graph May Leave Likely to Leave Happy Customer Happy Customer Happy Customer Power of Social Networks • People interact with each other’s behavior; influence each other • People make decisions in a social network context • Ignoring social network context means you’re missing a major influencer on your customers’ choices Graph models relationships between objects like people products and processes Why Graph?
  24. 24. 24 7/14/2014 Teradata Confidential Advanced Churn Analysis Today Statistical Analysis + Multi Channel Behavioral Path Analysis Churn Potential Statistical Model Behavioral Model = Best in Class Churn Analytics WHAT IF I COULD GRAPH THESE CUSTOMERS? + Sentiment Analysis Sentiment Score + =
  25. 25. 25 7/14/2014 Teradata Confidential Graph Churn Social Network Nodes High Churn Risk Low Churn Risk
  26. 26. 26 7/14/2014 Teradata Confidential Enhanced Churn with Social Graph Analysis Best in Class Churn Scores without Social Graph analysis Social Graph visualization can help visualize associations and areas to investigate Apply graph analytics, such as “Closeness” and “LocalClusteringCoefficient” to calculate and provide new insight on strong relationships!
  27. 27. 27 7/14/2014 Teradata Confidential Better Churn Scores on All Your Customers + = + =+ Churn “Social Graph Visualized” Churn “Social Graph Analyzed”
  28. 28. 28 7/14/2014 Teradata Confidential Single SQL-MR/GR Statement in Aster + =+ ASTER DISCOVERY PLATFORM TERADATA ASTER DATABASE Conduct behavioral and social network churn analysis with prebuilt functions Generate enhanced churn ranking scores Graph Analytics at Scale on All Customers
  29. 29. 29 7/14/2014 Teradata Confidential Questions and Answers Questions and Answers Thank You!