Big Data and the Future of Analytics Presentation from the 2016 CFO Symposium, in Adelaide, Australia. What does this mean for your business?

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3. Big Data and the Future of Analytics
Presented by: John-Paul Della Putta

4. Our Agenda for Today
Evolving Analytics
From 1.0 to 3.0
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The Rise of Big Data
What it is, how it can benefit you
Big Data Technology
What it is, what you need to know
Using Big Data & Analytics?
Becoming analytics driven
Enhancing your Analytics
Introducing KPI Pulse
The Future
Some Predictions

5. Evolving Analytics
Analytics 1.0 to 3.0

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Analytics has Changed

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From Analytics 1.0 to 3.0

8. Analytics 1.0
Good old fashioned reporting & BI
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 Descriptive analytics
 Generally backward facing and showing what’s
happened and why
 Often there’s a considerable lag between
event and insight
 Traditional BI and Reporting – still relevant
today and won’t be going away soon

9. Analytics 2.0
The Rise of Big Data
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Looking at much larger, broader data sets, often un-structured data
Emergence of new technologies which deal with large volumes of often un structured data
Rise of the Data Scientist

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11. Analytics 3.0
Closing the loop
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= Traditional Analytics
+ Big Data Technology
+ (possibly Big Data)
+ Prescribed Action

12. Example – Remote Gas Well Maintenance
What action will be taken automatically?
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13. Lower Costs
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 Railroad sensor notices that a
wheel is too hot and sends a
signal to stop the train
 A team is sent to perform repairs
 Possible derailment is avoided

14. How operational analytics ‘operate’

15. It’s not analytics for operations

16. Descriptive
Summarise and describe
what’s happened in the past
Predictive
Predict what’s likely to
happen in the future
Prescriptive
Determine actions to take
and make the future happen

17. Recommended for You
An example of operational analytics
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Redshift is Amazon’s Hadoop as a service

18. Internet Of Thing
Be careful – lots of noise, not much signal
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19. Mini Crash
The Dark Side of Operational Analytics
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20. Google Flu-trends
Over-states actual flu incidents by 50%
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21. Big Data
Some real world examples

23. Siri was a pentagon labs experiment
Uses big data and machine learning etc.
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24. Recommendation Engines
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Average of many algorithms

25. Where Big Data is Used
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Recommendations Risk, Fraud & Security Marketing

26. Better decisions
5 – 6%
INCREASE

27. Big Data 101
A crash course

28. Not Your Usual Data
Big data’s not just big – it’s different
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Different Data
Big Data is exciting because of all the
new information it contains

29. 3+ V’s
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Volume Velocity Variety

30. Instant Web Data
How many Tweets / second?
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31. Unstructured Data
E.g. Emails
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32. The Irony of Big Data
The Questions are Simple
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Is this person a good credit risk?
Is this movie worth watching?
Is this too much to pay for this property?

33. Hadoop
101

34. Hadoop
Key attributes
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Open source (cost effective)
Stores data in 3 places (no backup)
Not a database, a file system (no loading)
Suitable for large amounts of unstructured data
Scalable – Can be distributed over many computers
(Yahoo has a 42,000 node implementation)

35. It’s pretty complex..
Sorry, I wish it wasn’t, but it is…
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36. Better Decisions
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Performance and market value.
We find that DDD is associated
with a 5 – 6% increase in their
output and productivity, beyond
what can be explained by
traditional inputs and IT
5 – 6% Increase

37. Lowering Credit Risk

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39. How do You as a CFO Use Big Data?
Will you be deploying your own Hadoop Cluster?
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40. Renting Data
Not ready to build your own Hadoop cluster?
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41. Qlik Data Market

43. The Way to a Man’s Heart is Through his Stomach
The Way to a CFO’s Heart, is Through Their Budget
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45. "Information is the oil of the 21st century, and
analytics is the combustion engine” (Peter
Sondergaard, Senior Vice President, Gartner)

46. End users want to be able to quickly
analyse their own data
CFO’s don’t wait for IT

47. Qlik User-driven Analytics

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In-Memory
Instant Insights

52. Not Big-Data
Good old-fashioned human
research
Is Yelp paying too little?
Are they being too greedy?
I Don’t know – need more data

53. Back to the real world

54. Introducing KPI Pulse
Or Analytics 2.5 
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56. KPI’s on anyone’s mobile phone
No need to login to the server or have a Qlik license

57. Click to drill down to the details
See key details and reports
PDF / Excel
reports

58. Chat between users
Managers can ask why performance metrics aren’t met

59. Summary & Recap
What we learnt

60. So what did you learn?
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If you were paying attention…
Analytics is changing
Moving from descriptive analytics
through Predictive to Prescriptive.
Big Data has 3V’s
Big Data is different data and It’s
being used everywhere. You might
end up renting it.
KPI Pulse
You can get real time analytics
pushed out to your users’ mobile
phones
Hadoop isn’t a DB
It’s not a database, it’s a
filesystem and it’s complex.
You can use it without building it

61. We started with science fiction