This document discusses building a competitive advantage from a data lake. It recommends building a data reservoir with governance to hold large and complex datasets. Organizations should start small by working with business partners to develop new analytics from low-hanging fruit projects that provide value. This will help drive adoption and extension of the data lake approach over time.

1. Compe&&ve
Advantage
from
the
Data
Lake
Ron
Bodkin,
Founder
&
President
Oct.
2014
CONFIDENTIAL | 1
1

2. 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
CONFIDENTIAL | 2
Big Data
2
Source: Hortonworks

3. CONFIDENTIAL | The Data Lake
3

4. CONFIDENTIAL | Swamp or Reservoir?
Swamp Reservoir
4

5. CONFIDENTIAL | Misconception: “Hadoop is a Giant Database”
5
?

6. CONFIDENTIAL | Misconception:
Hadoop is Just Archival / Power ETL
6
?

7. CONFIDENTIAL | Everyone Wants to Own Data Science…
7
Marketing
Analytics Line of Business

8. CONFIDENTIAL | 8
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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9. CONFIDENTIAL | 9
Governance – Project Prioritization
New
Data
Sets
Use
Cases
from
Strategy
New
Apps /
Reports
New
Analytics
Working
Steering
Committee
Executive
Steering
Committee
Prioritized List
1. New Data Set 1
2. New Use Case 1
3. Use Case from
Strategy
Business
Experts
Business impact vs. ease
of delivery, etc.
Nominated by…
Prioritized by…
Core
Platform
Updates
Sponsored by…
9

10. CONFIDENTIAL | Example: Leading Financial Asset Manager
• 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
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11. CONFIDENTIAL | Pitfalls
• 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
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12. CONFIDENTIAL | Big Data Center of Excellence:
Business and IT Collaboration
12

13. CONFIDENTIAL | 13
The Big Data Adoption Journey
13
1
2
3
4
5
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

14. CONFIDENTIAL | 14
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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