This document discusses trends in big data, including what big data is, how it is used, and its impact. It notes that big data refers to large volumes of diverse data from sources like social media, sensors, and scientific experiments. Examples are provided where analyzing big data in real-time has provided insights, such as how sensor data helped a sailing team optimize performance. The document also discusses how big data is changing organizations to be more outwardly focused and responsive to customers, and how IT systems need to integrate big data with traditional data warehouses and business intelligence tools to provide fast access and answers.

2. Trends in Big Data
René Kuipers
Principal Consultant Big Data & Analytics
@rjlkuipers

3. Overview
• Wat is it (or not) ?
• What to do with it ?
• How to deal with it ?
• Impact on IT?

4. Big Data
Wat is it (or not)

5. Big data: what is it (or not)

6. Big data: what is it (or not)
• Social data.
• Sensor data.
• unstructured data.
• A hype
• Hadoop
• Real-time
• ………………
• The four V's: Volume, Variety, Velocity, Value

7. Big data: what is it (or not)

9. Wat exactly is the trend ?
• “a lot of data” isn’t new:
• Governments
• Stock exchange.
• Real-time isn’t new:
– Railway management
• Level of detail increases
• Data is no longer in possession
• Open Data intiatives.

11. Example 1:
on-board sensor data
• America’s Cup 2013
• On-board sensors
• Off-board sensors
• 30.000 dp/s
• Oracle Team USA won 9-8 after 8-1 down, because of ‘insight’.

12. Example 2:
medical
• personalized healthcare
• n=1 treatment.
• Bring down cohort-size by
means of more personal
data.

13. Example 3:
scientific
• CERN (LHC)
• 120.000 sensors
• 4 GB data/sec

14. Example 4:
online ads
• Real-time profiling
• Context search
• Display ad
• Billing

15. Big Data Social
Cloud Mobile
Een unity of 4

16. The value of data

17. Value of data
• The higher up in an
organization, the lower the
value of an individual
datapoint.
• The lower (more operational)
in an organization, the higher
the value of an individual
datapoint.
• Big data offers more (real-time)
insight on
operational level than on
strategic level.

18. • Big Data is mainly a CX ‘thing’.
• 360-degree view of
• Patient
• Customer
• Supplier
• …
• …

19. Implications
Organization Technical

20. Implications
Organization
– Outward.
– Openness, transparency
– Extreme short communication (outward)
– Customer focus, short reactiontimes.

21. Customerfocus
#klm #fail

22. Implicaties
Organization Technical

23. How to embed Big Data into traditional DWH/BI ?

24. How to embed Big Data into traditional DWH/BI ?

25. How to embed Big Data into traditional DWH/BI ?

26. DWH and BI
changes

27. In-house
data
External
data

28. Datawarehouse, Big Data and Business Intelligence
– Storage of Big Data: Hadoop
– Storage of ‘traditional’ data: RDBMS
– necessity: transparant access.
– necessity: high-end usertools: Self Service BI.
– necessity: fast back-end.

29. Future

30. More data More
questions
Faster
answers

31. In-memory

32. Conclusions
– Big Data
– Large Volume (Volume)
– Fast (Velocity)
– In different shapes and sizes (Variety)
– Huge information potential (Value)
– Data grows exponentially
– Data is extern
– Data must lead to faster insights
– Fundamentallly datawarehouse redesign
– In-memory
– Demands organizational changes