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Compe&&ve  Advantage  from  the  Data  Lake  Ron  Bodkin,  Founder  &  President  

Oct.  2014  1

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Big Data

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What is Big Data? •  Data sets so large and

complex that they become awkward to work with using standard tools and techniques

•  Google, Quantcast, Yahoo, LinkedIn… customer innovation in open source

Source: Hortonworks

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The Data Lake

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Swamp Reservoir

Swamp or Reservoir?

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Misconception: “Hadoop is a Giant Database”

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?

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Misconception: Hadoop is Just Archival / Power ETL

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?

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Everyone Wants to Own Data Science…

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Marketing

Line of Business Analytics

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Example: High Tech Manufacturing

•  Improve Yields and Accelerate Time to Market −  Data Access −  Deeper insights −  Proactive analytics −  Innovative analysis tools

•  Buy in through −  Cross-functional center of excellence: IT & business −  Agile w/ releases every 8-12 weeks: test & learn −  Close collaboration with users to develop & iterate −  Teach users −  User success propagates

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Governance – Project Prioritization

New Data Sets

Use Cases from

Strategy

New Apps /

Reports

New Analytics

Working Steering

Committee

Executive Steering

Committee

1.  New Data Set 1 2.  New Use Case 1 3.  Use Case from

Strategy

Prioritized List

Business Experts

Business impact vs. ease of delivery, etc.

Nominated by…

Prioritized by…

Core Platform Updates

Sponsored by…

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•  Use of multi-channel data for web, mobile, call center with customer profile and transaction history

•  Insight into customer journeys •  Data science models to improve offers, customer

lifetime value, and insight into brand-damaging fraud

•  Data science process driving business and technology collaboration

•  Improved customer experience •  Reduced support costs

Example: Leading Financial Asset Manager

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•  Picking the wrong starting point −  ho hum, big bang, boil the ocean

•  Insufficient funding •  Immature Governance •  Siloed Organization •  Data Politics •  Change Management •  Skills Gap •  Business as usual •  Not fitting in with existing analytic ecosystem

Pitfalls

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Big Data Center of Excellence: Business and IT Collaboration

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The Big Data Adoption Journey

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Traditional B.I.

Contain Cost & Scale •  Behavioral data •  Integrated reference

data

Agile Analytics §  Actionable insights §  360 degree views §  Speed §  Accuracy

Business Innovation §  Optimization §  Engagement §  Predictive Analytics

on Big Data

Organizational Transformation Through Data §  New products §  Operational efficiency §  New businesses §  Analytics for customers

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Conclusions

•  Build a data reservoir with governance built in •  Start with low hanging fruit: net new analytics •  Partner with the business to drive value and extend

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