Ian Pepper gave his talk on the Future of Business Intelligence and what he believed where important trends in the market.

1. The Future of Business Intelligence
Helping yourself to insightIan Pepper, 27/03/17

2. What I’m going to cover
• Me
• Future of BI?
– Data Lakes
– Self-service

3. Who is Ian Pepper?
• Head of Strategy and Integration for Novartis
– Big Data, Analytics, Blockchain, RPA and IoT
• Previously was CTO and co-founder of a bunch
of companies
@ianpepper linkedin.com/in/ianpepper

4. Disclaimer
This presentation was prepared in a
personal capacity. The opinions
expressed here are my own and do not
reflect the views of Novartis.

5. Data Lakes

6. The future of data
management and BI?

7. Often more of a Data
Swamp

8. Why?

9. As business leaders what do
you really need to know?Really?

10. Tips from the Field
• Don’t ingest data unless you are sure you’re going
to use it
• Governance is a must
• Metadata is key, repeat metadata is key!
• Deploy multiple SQL-on-Hadoop tools for
flexibility (Drill, Impala, ...) – Logical warehouse
• Use Spark for processing (inc. SparkSQL)
• Data warehouses and marts are not dead!

11. We have a Data Lake. So, how
do we extract
real value?

12. Often that’s this guys responsibility
Introducing the Data Wizard or Data Scientist

13. Jargon Buster
• How to talk to a Data Scientist
– Data Extraction, Preparation, Model Building,
Testing, Validation and Deployment, Rinse and
Repeat
– Leading to Actionable Insight (Value)

14. Flavours of Analytics/BI
The Advanced Stuff
or the Future!

15. But, we have a problem where
are all the data scientists?
Can we do without them?

18. Data Availability and Tooling are
key to success

19. Self-Service tools do the
heavy lifting.
So we can focus on the
problem

20. Characteristics of Self-Serve
• Point and click (code optional)
• ML and Model Library
• Seamless data blending and preparation
• In-database joins (server side push down)
• Auto-create MapReduce and Spark jobs
• Visualization (e.g. produce Tableau output)

22. Remember – data with a
lot of information

23. … and a lot of noise.

24. The ability to hear the signal
from the noise is the key…

25. Thank you!