Hadoop Summit 2015

1. Smarter Decisions Using Data
Adeyemi Ajao
Vlad Giverts

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

4. 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

5. 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

6. Some Examples

7. 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

8. 8
SYMAN and Industry Trees
Example: Too many “nurses”

9. HCM Example: Top Performer Retention
Normal Department Abnormal Department

10. ▪ 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

11. 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%)

12. 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

13. Retention Risk: Tech Stack

14. Retention Risk: Tech Stack
Application

15. Retention Risk: Tech Stack
Application
Spark
HDFS
Elastic
Search
YARN
ML
Pipeline
Kafka
Indexing

16. Indexing & Information Retrieval
Application
Elastic
Search
Kafka
Indexing

17. Indexing & Information Retrieval
Application
Elastic
Search
Kafka
Indexing

18. Big Picture
Application
Spark
HDFS
Elastic
Search
YARN
ML
Pipeline
Kafka
Indexing

19. Machine Learning
Application
Elastic
Search
ML
Pipeline

20. Machine Learning
Application
Elastic
Search
ML
Pipeline

21. Machine Learning
Application
Elastic
Search
ML
Pipeline

22. ML Pipeline
Snapshot
Data

23. ML Pipeline
Snapshot
Data
Feature
Extraction

24. Data and “Features”
Tenure
Time in Current Function
Pay Range
Penetration
Manager Attrition
Rate
Num Promotions
Avg Time Between
Promotions

25. ML Pipeline
Snapshot
Data
Feature
Extraction

26. ML Pipeline
Feature
Extraction
Model
Training
Snapshot
Data

27. ML Pipeline
Feature
Extraction
Model
Training
Model
Validation
Snapshot
Data

28. Training and Validation
Barry
Raise: $1,000
2014 2016
Ravi
Left :(
John
Left :(
Albert
Promoted!
Yury
Hired
Tejas
Changed Teams

29. Training and Validation
Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015

30. Training and Validation
Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015
TRAINING VALIDATION

31. ML Pipeline
Feature
Extraction
Model
Training
Model
Validation
Snapshot
Data

32. ML Pipeline
Feature
Extraction
Model
Training
Model
Validation
Snapshot
Data
Evaluation

33. Evaluation
Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015

34. Evaluation
Q3 2015 Q4 2015
Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015

35. Evaluation
PREDICTION
Q3 2015 Q4 2015
Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015

36. ML Pipeline
Feature
Extraction
Model
Training
Model
Validation
Snapshot
Data
Evaluation

37. ML Pipeline
Feature
Extraction
Model
Training
Model
Validation
Snapshot
Data
Evaluation
Publish
Results

38. Machine Learning
Application
Elastic
Search
ML
Pipeline

39. Indexing
Application
Elastic
Search
Kafka
Indexing

40. Questions?
Application
Spark
HDFS
Elastic
Search
YARN
ML
Pipeline
Kafka
Indexing

41. Thank You!
Q & A