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Data Science: Driving Smarter Finance and Workforce Decsions for the Enterprise

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Hadoop Summit 2015

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Data Science: Driving Smarter Finance and Workforce Decsions for the Enterprise

  1. 1. Smarter Decisions Using Data Adeyemi Ajao Vlad Giverts
  2. 2. Reactive – Operational Reporting Operational Reporting for Measurement of Efficiency and Compliance, Data Exploration and Integration, Development of Data Dictionary Level 1 Predictive Analytics Development of Predictive Models, Scenario Planning, Risk Analysis and Mitigation, Integration with Strategic Planning Level 4 Strategic Analysis Segmentation, Statistical Analysis, Development of “People Models”, Analysis of Dimensions to Understand Cause & Delivery of Actionable Solutions Level 3 Proactive – Advanced Reporting Operational Reporting for Benchmarking and Decision-making, Multidimensional Analysis and Dashboards Level 2 Prescriptive Analytics Machine Learning, Prescribe Recommended Actions, Specify Interrelated Effects of Decisions Level 5 Evolution of Analytics Source: Bersin & AssociatesSource: Bersin & Associates
  3. 3. State of the Art Data Analytics System Ad-Hoc initial analysis Expensive Software Taxonomy-Building & Semantic Grouping Unsupervised Learning Clustering Supervised Learning Optimization Deep Learning Algorithms Natural Language Processing Developers Predictive Applications xxx xxx Hardware Upgrades Prescriptive Applications Versioning Machine Learning Algorithms Data Cleansing/ Transformation Multi-Source Data Integration Expensive Software Data Scientists Creating a State of the Art Analytics System VERY HARD AND EXPENSIVE! How hard can it be? Data Aggregation Data Classification Implementation of Algorithms Application Creation Continuous Machine Learning Application UpdatesMaintenance
  4. 4. Taxonomy-Building & Semantic Grouping Ad-Hoc initial analysis Unsupervised Learning Clustering Prescriptive Applications Versioning Data Cleansing/ Transformation Multi-Source Data Integration Maintenance Expensive Software Supervised Learning Optimization Deep Learning Algorithms Natural Language Processing Developers Predictive Applications xxx xxx Hardware UpgradesMachine Learning Algorithms Expensive Software Data Scientists Application Updates Data Aggregation Data Classification Application Creation Continuous Machine Learning Implementation of AlgorithmsInsight Apps
  5. 5. Some Examples
  6. 6. HCM Example: Top Performer Retention Business Question Addressed: Which of my top performers are at risk of leaving? To help VPs of HR and lower-level managers retain top talent by: • Building insights from performance metrics • Integrating with Recommended Candidates to ensure prospective talent has strong potential • Suggesting improvements to reduce talent flight risk Key Objectives Performance Reviews Identify top performers With high flight risk Take actions to keep performers Reduce Flight Risk Cycle for Maintaining Top Talent
  7. 7. 8 SYMAN and Industry Trees Example: Too many “nurses”
  8. 8. HCM Example: Top Performer Retention Normal Department Abnormal Department
  9. 9. ▪ Model was 40% more accurate than the manager ▪ With 25% overlap between the manager and the model Tangibly more Accurate Predictions High Risk (By Model) Terminated High Risk (By Manager) 55 215 137 550
  10. 10. Retention Prediction Quality WORKDAY CONFIDENTIAL Period Total Random Prediction Model Prediction Termed (Apr – June) 300 6 110 (38%) Termed (July-Nov) 585 22 (3.7%) 169 (30%)
  11. 11. Workday Analytics Connect Any type – structured and unstructured Any size, including Big Data Inform Intuitive, interactive visualizations Contextual Predictive and Prescriptive Act Same global platform Same security model Accessible anywhere
  12. 12. Retention Risk: Tech Stack
  13. 13. Retention Risk: Tech Stack Application
  14. 14. Retention Risk: Tech Stack Application Spark HDFS Elastic Search YARN ML Pipeline Kafka Indexing
  15. 15. Indexing & Information Retrieval Application Elastic Search Kafka Indexing
  16. 16. Indexing & Information Retrieval Application Elastic Search Kafka Indexing
  17. 17. Big Picture Application Spark HDFS Elastic Search YARN ML Pipeline Kafka Indexing
  18. 18. Machine Learning Application Elastic Search ML Pipeline
  19. 19. Machine Learning Application Elastic Search ML Pipeline
  20. 20. Machine Learning Application Elastic Search ML Pipeline
  21. 21. ML Pipeline Snapshot Data
  22. 22. ML Pipeline Snapshot Data Feature Extraction
  23. 23. Data and “Features” Tenure Time in Current Function Pay Range Penetration Manager Attrition Rate Num Promotions Avg Time Between Promotions
  24. 24. ML Pipeline Snapshot Data Feature Extraction
  25. 25. ML Pipeline Feature Extraction Model Training Snapshot Data
  26. 26. ML Pipeline Feature Extraction Model Training Model Validation Snapshot Data
  27. 27. Training and Validation Barry Raise: $1,000 2014 2016 Ravi Left :( John Left :( Albert Promoted! Yury Hired Tejas Changed Teams
  28. 28. Training and Validation Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015
  29. 29. Training and Validation Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015 TRAINING VALIDATION
  30. 30. ML Pipeline Feature Extraction Model Training Model Validation Snapshot Data
  31. 31. ML Pipeline Feature Extraction Model Training Model Validation Snapshot Data Evaluation
  32. 32. Evaluation Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015
  33. 33. Evaluation Q3 2015 Q4 2015 Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015
  34. 34. Evaluation PREDICTION Q3 2015 Q4 2015 Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015
  35. 35. ML Pipeline Feature Extraction Model Training Model Validation Snapshot Data Evaluation
  36. 36. ML Pipeline Feature Extraction Model Training Model Validation Snapshot Data Evaluation Publish Results
  37. 37. Machine Learning Application Elastic Search ML Pipeline
  38. 38. Indexing Application Elastic Search Kafka Indexing
  39. 39. Questions? Application Spark HDFS Elastic Search YARN ML Pipeline Kafka Indexing
  40. 40. Thank You! Q & A

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