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Copyright 2015 Mather Economics LLC. All rights reserved.
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CLV = [(Revenues – Costs)*(Expected Lifetime)]*PV – Acquisition Cost](https://image.slidesharecdn.com/june9145pmkey2consultingmathereconomicscurrintchivzhelv2-150616215906-lva1-app6892/85/Hybrid-Data-Architecture-Integrating-Hadoop-with-a-Data-Warehouse-19-320.jpg)
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Hybrid Data Architecture: Integrating Hadoop with a Data Warehouse
Mather Economics wanted a data architecture that could integrate Hadoop and a data warehouse to provide a responsive user experience for data slicing, aggregating, and modeling on 100% of data samples. A hybrid approach was implemented that uses Hadoop for ingestion and storage and a data warehouse for transformation, integration, and dimensional modeling to support both internal analysts and external customers. This hybrid approach meets the goals of being data and technology agnostic while providing speed for analytics.
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Hybrid Data Architecture: Integrating Hadoop with a Data Warehouse
- 1. HYBRID DATA ARCHITECTUREIntegrating Hadoopwith a Data Warehouse Chuck Currin Principal Consultant @ Key2 Consulting Arvid Tchivzhel Director @ Mather Economics
- 2. INTRODUCTIONS Key2 Consulting –Who are we? Mather Economics – Who are we?
- 3. PROJECT BACKGROUND • ProjectTeam Turnover • Requirements Refactoring • Existing Code Review • Speeding up the SDLC
- 4. SO, WHAT DIDMATHER WANT? • Data Agnostic • Technology Agnostic • Responsive End User Experience • On the fly data slicing and aggregating • 100% sample sizing for Modeling
- 5. Customer Experience Needs ResponsiveData Architecture PROCESS REQUIREMENTS PYRAMID The Architecture must Support Ease of Data Extensibility and Scalability Statistical Modelling (100% Sample Size) Capability to capture any data format
- 6. TWO SEPARATE APPROACHES DWHadoop Cube Drill ETL Facts Slice Star Schema Dice Multi Dimensional Aggregate Dimensions CDC Slowly changing HUE Beeline HBase Cluster HDFS Node Python SQOOP PIG Hive
- 7.
- 8. IMPLEMENTATION PYRAMID Data Sourcing DataLake Dashboard Data Warehouse
- 9. HDFS Customer Interaction Reducing & Linking Mapping &Storage Segmenting & Staging Ingestion Transformation & Integration Dimensional Data Warehouse Analyst Interaction Relational
- 10.
- 11. DATA LAKE Mapping &Storage Segmenting & Staging Reducing & Linking
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- 13.
- 14. DATA IS OIL…BUT ORGANIZATIONS NEED GASOLINE “…Data is the new oil — it’s very valuable to the companies that have it, but only after it has been mined and processed. The analogy makes some sense, but it ignores the fact that people and companies don’t have the means to collect the data they need or the ability to process it once they have it. A lot of us just need gasoline.” Derrick Harris, Gigaom.com, March 4, 2015 Copyright 2015 Mather Economics LLC. All rights reserved.
- 15. THE GASOLINE: ANALYTICSAND OPTIMIZATION ENABLED BY BIG DATA • Digital Customer Lifetime Value (CLV) • e-Commerce flow • Advertising targeting and pricing • Editorial programming decisions • Product offerings, bundles, and pricing • Acquisition targeting • Retention strategy • Content recommendation Copyright 2015 Mather Economics LLC. All rights reserved.
- 16. INTEGRATING FORMERLY DISPARATESYSTEMS TO CREATE 360-DEGREE CUSTOMER INTERACTION VIEW Copyright 2015 Mather Economics LLC. All rights reserved. Centralized Big Data & Analytics: • Customer- Level • Online Data • Offline Data Data Analytics Customer Transactions Demographics Online Behavior Marketing Analytics Dashboard
- 17. ENTITY RESOLUTION ANDBIG DATA • Big Data and traditional warehousing are critical for successful entity resolution • Clients understand number of customers that interact through different channels Copyright 2015 Mather Economics LLC. All rights reserved. Transactions Demographics Registrations Email Campaigns Newsletter
- 18. TRADITIONAL OFFLINE METRICSCAN BE MEASURED BY ONLINE ENGAGEMENT Copyright 2015 Mather Economics LLC. All rights reserved.
- 19. CUSTOMER LIFETIME VALUE Copyright2015 Mather Economics LLC. All rights reserved. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%0 60 120 180 240 300 360 420 480 540 600 660 720 Expected Value (Area Under Curve) CLV = [(Revenues – Costs)*(Expected Lifetime)]*PV – Acquisition Cost
- 20. OPERATIONALIZING CLV • Findhigh/low value segments • Integrate with campaign targeting • Apply to pricing and product offerings • Available to customer service reps • Apply to online audience segmentation • Overlay against content, psychographics…etc. Copyright 2015 Mather Economics LLC. All rights reserved.
- 21. V a l u e Copyright 2015 MatherEconomics LLC. All rights reserved.
- 22. CUSTOMER LIFETIME VALUEHELPS MANAGE PROFITABILITY ON A CUSTOMER LEVEL Copyright 2015 Mather Economics LLC. All rights reserved.
- 23. CONCLUSION • DW andHadoop not mutually exclusive • Mather requirements met by a hybrid approach • Hybrid key to satisfying both internal/external users • Speed of dimensional data critical for analytics Copyright 2015 Mather Economics LLC. All rights reserved.