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How Macy's creates operational insights on Hadoop

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How Macy's creates operational insights on Hadoop

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How Macy's creates operational insights on Hadoop

  1. 1. How Macy’s creates Operational Insights on Hadoop
  2. 2. Macy’s, Inc. Background  Macy’s, Inc. is one of the nation’s premier omnichannel retailers  Fiscal 2015 sales of $27.1 billion  Operates 870 stores in 45 states  Brands: Macy’s, Macy’s Backstage, Bloomingdale’s, Bloomingdale’s Outlet, Bluemercury as well as macys.com, bloomingdales.com, bluemercury.com  Ships products to over 100 countries  Workforce includes over 157,000 employees
  3. 3. Digital Growth  World went digital  Macys generated its first billion-dollar month of sales from digital platforms in December 2015  Filled nearly 17 million online orders at macys.com in November/December 2015 an increase of about 25% over previous year  Based on significant new fulfillment capacity, site functionality, and aggressive digital marketing
  4. 4. Why Hadoop at Macy’s?  Traditional data architecture is inflexible and not nimble  Inability to tap into historical data  Severe compute capacity limitations  Significant cost implications to scaling  Unstructured data sources
  5. 5. Why BI on Hadoop?... Why Not?!  Single data architecture can cater to a comprehensive list of use-cases  Integrated eco-system of data, process, and tools  Analytics, Experimentation, and Production can be collocated  Low total cost of ownership
  6. 6. What does it mean to be Operational?  Ability to move quickly from testing/experimentation cycle to production  Reliable data quality, governance, and security  Acceptable levels of stability and robustness to meet SLAs  Automation to the nth degree
  7. 7. The Blueprint
  8. 8. The Blueprint
  9. 9. Need a Robust Experimentation Framework Problem Statement • What issue are we trying to solve for? • Why is it important to the business? Size of Problem • What’s the $ impact? • % customers affected? • What can/can’t we influence? Hypotheses • What’s the root cause? • What change will have the best ROI? • Are there alternatives? Supporting Data • Validate (or adjust) our hypotheses • Rule out false positives Tests • What’s the safest way to test our riskiest assumptions? • Who/when/how? Predictors • What variables are most highly correlated with our problem and/or solution? KPIs / Success • What outcome would we define as “success” • What’s our response to success/failure? Key “Who provides?” Team 1 Team 2 Team 3
  10. 10. Data Domains Orders Customers Products Clicks Marketing External Big Data Repository In-memory Data De-dupe AggregationTransformation Blending Tools Campaign Management/ Optimization Statistical Analysis Consumers Merchandizing Marketing Product Management Analytics Data Scientists Advanced Analysis/ Modeling Data Visualization/ Data Mining Other Business groups Storage and Enrichment Data Management Data Security
  11. 11. Growing pains Challenge  Significant time spent on data engineering  Long analytic iteration times  Inability for analysts to collaborate Solve  Need to establish a virtual semantic layer  Seamlessly integrate with existing tools  Deploying in-memory Big-Data OLAP tool
  12. 12. How to drive adoption? Quality Release SocializeTrain Measure
  13. 13. Adoption Checklist Center of Operations + Center of Evangelism  Confidence in data quality  Data governance, and security  Standardized release process  Socialize and Train  Monitor adoption (Qualitative and Quantitative)
  14. 14. Keys to Success  Laser focus in delivering business value  Keep process overheads at check  Continuous operational improvement  Tolerance to a maturing solution for the greater good  Flexible resource model
  15. 15. Thank You!! Seetha Chakrapany Analytic & CRM Solutions

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