Thomas Reby, Ebay spoke at the CIO Event (dot) com

1. Thomas Reby
Remodeling the Information Office

2. Nowadays, buzzwords such as LoMoSo, Consumerization and Web 2.0 are redefining the
digital landscape through massive data generation and a key question is emerging: What
do we do with all this information?
First, the challenge was to store it. The answer to that came by innovative solutions
deployed to manage data in centres scalable well beyond what “conventional thinking”
would have imagined 10 years ago. Enter the clouds, virtualization and other
technologies aiding the mission of stockpiling data.
Second, the challenge is to process it. And again, pioneering solutions are being
developed to analyse, model and visualize it. Witness the arrival of Big Data – a Thomas Reby
challenge that is still keeping us busy. www.linkedin.com/in/thomasreby
But we may be missing a point. Stored and processed information is not knowledge. It Head of Knowledge
lacks context. And actionable knowledge is what we (and our customers) need to be
successful. This might be the challenge for the remodelled CIO.
Management
eBay Inc.

3. Focus on:
 Emerging forces behind Big Data
 Business questions to answer
 Defining a need over an ability
 Value-added role for the CIO

4. Grounding - Why all this data?
 Consumer accessibility and demand
 Digital interaction - contribution capability expected
 LoMoSo - location-based, mobile, social
 ”Add Comment” is expected…

5. Challenge 1: Where do we put it?
 DW consolidation - we ran out of space or people
 Globalization - apples to apples
 "Eternal migration"
 Technical problem to solve

6. Challenge 2: How do we process it?
 Unified reporting - users got tired of differences
 "I wanna see" - driven by the customer viewpoint
 Sizing limitations - first technical, then human
 Still a technical problem to solve

7. Some open questions…
 What is true "demand" as opposed to "growth"? rational
 Are we analysing or merely reporting? intelligence
 Is the data bigger than the user focus area? holistic
 Do we have the context of the information? scientific
 Is it worth it? accountable

8. What’s needed?
 Transformation: data + context = knowledge
 Context generation: tagging, people, gamification
 Linking: tie explicit resources to business outcome
 Independent science: people, process, tools, statistics

9. Critical thinking – knowledge to take action
 Data science lab is missing
 Rationalization is a great excuse
 Extra-departmental datasets have wow-factor
 Causation seldom just appears

10. Key takeaways
 Big data is not just a technical problem
 Turning data into knowledge requires human context
 Value is determined by associated business outcome
 Filling the science gap is critical

11. Q&A
…and thank you!