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201503 ieg preso_ramesh bhashyam_teradata_publish

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201503 ieg preso_ramesh bhashyam_teradata_publish

  1. 1. Information Excellence 2015 Spring Summit Business Analytics Ramesh Bhashyam, CTO. Teradata R&D Labs Universal Big Data Architecture
  2. 2. Information Excellence 2015 Spring Summit Business Analytics
  3. 3. Big Data Architecture Information Excellence Summit Ramesh Bhashyam Teradata R&D Labs bhashyam.ramesh@teradata.com 28 Feb 2015
  4. 4. 2 The business value of data and analytics are no longer in question… Copyright Teradata Corporation 2011 We are used to the idea of deploying new technology to improve productivity and efficiency... But data are no longer merely the by-product of process improvement, they are becoming the raw material of business.
  5. 5. 3 • More data has been created in the last three years than in all past 40,000 years. • Total data has quadrupled in the last three years. • Driven by several trends: Moore’s law, web, social media, sensors, smart phones, cameras, network of things. • Unit of Analysis has changed Increasing Complexity of Data 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 VolumeinExabyte Transactional Data Documents Web Data Sensor Data 1990 2000 2010 20202014 Transactions Interactions Observations
  6. 6. 4 © 2014 Teradata Data Plateau Source: Hortonworks Corporation
  7. 7. 5 ..and the “Internet of Things” is upon us “…the main goal of smart systems is to close the loop… this means using the knowledge gleaned from data to optimize and automate all kinds of processes… ranging from manufacturing to heading-off car collisions.”
  8. 8. 6 Complex Dimensions - Often not Considered • Value density – Measure of the extent to which data has to be processed based on form • Analytic Agility – Analytics on structured for BI versus unstructured • Concurrency of Access – Frequently used data versus infrequently used data or one time data
  9. 9. 7 • BI. ROI, reporting and dashboards • Descriptive analytics. Dividing customers into segments and profiling profitable customers • Predictive analytics. Future profitability of a customer, scoring models, and predicting outcomes such as churn • Optimization. Optimization problems and what-if scenarios => Acquire, Prepare, Analyze, Validate, and operationalize Data Driven Decision Making
  10. 10. 8 Need to Store All Data Courtesy: http://med.cornell.libguides.com/HINF5008
  11. 11. 9 • Data Lake or Data Platform • Data R&D • Data Product • All Data – Combine behavior with transactions and other customer data Deriving Value From Data
  12. 12. 10 Mind The Gap • Easy • Iterative & Fast • Integrates well with BI/Viz Tools SQL • Powerful • Batch- oriented • Requires lots of coding MapReduce • Huge Opportunity • Many Players • Emerging Market • Hybrid Products Market WhiteSpace
  13. 13. Math and Stats Data Mining Business Intelligence Applications Languages Marketing ANALYTIC TOOLS & APPS USERS DISCOVERY PLATFORM DATA WAREHOUSE ERP SCM CRM Images Audio and Video Machine Logs Text Web and Social SOURCES DATA PLATFORM ACCESSMANAGEMOVE UNIFIED DATA ARCHITECTURE Marketing Executives Operational Systems Frontline Workers Customers Partners Engineers Data Scientists Business Analysts Fast Loading Persistent Refinement Data Lake/Data Hub Exploratory Analytics Business Intelligence Predictive Analytics Operational Intelligence Data Discovery Path, graph, time-series analysis Pattern Detection Technology Independent
  14. 14. 12 Putting it all Together: Predictive Part Failure Analysis Define event-based scores patterns Operational analysis Create Predictive Models CAPTURE | STORE | REFINE Aircraft Sensor Data Maintenance Records Identify event paths that lead to part failure and safety issues over time. Discovery Platform EDW Preventative Maintenance PredictiveSafety Warnings Predictivepart lifespan analysis & optimization SQL-H SQL-H Access Hadoop data using SQL to join the data with Teradatatables
  15. 15. 13 Questions?
  16. 16. Information Excellence 2015 Spring Summit Business Analytics
  17. 17. Community Focused Volunteer Driven Knowledge Share Accelerated Learning Collective Excellence DistilledKnowledge Shared, Non Conflicting Goals Validation/ Brainstormplatform Mentor,Guide, Coach Satisfied,Empowered Professional Richer Industry and Academia About Information Excellence Group Progress Information Excellence Towards an Enriched Profession, Business and Society
  18. 18. About Information Excellence Group Reach us at: blog: http://informationexcellence.wordpress.com/ linked in: http://www.linkedin.com/groups/Information- Excellence-3893869 Facebook: http://www.facebook.com/pages/Information- excellence-group/171892096247159 presentations: http://www.slideshare.net/informationexcellence twitter: #infoexcel email: informationexcellence@compegence.com informationexcellencegroup@gmail.com Have you enriched yourself by contributingto the community Knowledge Share..

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