Lars Hamberg discussed the potential of big data and predictive analytics in the investment industry at the Banking Summit in December 2015, addressing the hype versus reality of big data. Despite its initial failures to meet expectations, he emphasized that emerging trends, such as data explosion and machine learning, will enable big data to transform industries and create significant opportunities. The presentation outlined successful use cases and highlighted the impending shift in the competitive landscape, urging banks and other sectors to embrace advanced data analytics.

1. Lars Hamberg
Predictive Analytics
in The Investment Industry
BIG DATA & ANALYTICS FOR BANKING SUMMIT
December 1 & 2, 2015 | New York

2. STRATEGIC SCOPE
OVERVIEW & OUTLOOK

3. QUESTION:
Has Big Data failed

4. QUESTION 1:
Has Big Data failed
…to deliver on its hype?

5.
Big
data
defini'on?

6.
Big
data
defini'on?
“too
large
OR
complex
for
tradi'onal
processing
methods”

7.
general blur ?
buzzwords and distinction?
big
data
analy'cs
or
advanced
analy'cs
?
predic've
analy'cs
or
big
data
predic've
analy'cs
?
machine
learning
?

8.
modern companies
want to know everything – what about banks?
structured
data
advanced
analy'cs
staying
ahead
of
the
connected
clients
predic've
analy'cs
machine
learning

10. QUESTION 2:
Why has Big Data failed
to deliver on its hype?

12. B I G D A T A – V I S I O N R U N N I N GA H E A D O F R E A L I T Y ?

13. BIG DATA HYPE CYCLE
Trigger
Enlightenment
Plateau of Productivity
Peak of Inflated Expectations
Trough of Disillusion

14. BIG DATA HYPE CYCLE
Trigger
Enlightenment
Plateau of Productivity
Peak of Inflated Expectations
Trough of Disillusion

15. BIG DATA HYPE CYCLE
WHY ARE WE HERE?
Trough of Disillusion

16. BIG DATA HYPE CYCLE
THE CHALLENGE OF
DEALING WITH THE REALITY
OF THE BIG UNSTRUCTURED
DATA STACK
Trough of Disillusion

17. In the big data stack
Unstructured
80-90%

18. 2.5+
million
words
in
english

19. QUESTION 3:
Will Big Data deliver on
its hype?

20. Yes!

21. Why?

22. Confluence of trends

23. Confluence of trends
1. Data explosion - 24m doubling rate

24. Confluence of trends
1. Data explosion - 24m doubling rate
2. Smartphone user behavior – 150x/day

25. Confluence of trends
1. Data explosion - 24m doubling rate
2. Smartphone user behavior – 150x/day
3. Breakthrough technologies: Machines
are learning to ”read and understand”
unstructured data on a large scale

26. Confluence of trends
1. Data explosion - 24m doubling rate
2. Smartphone user behavior – 150x/day
3. Breakthrough technologies: Machines
are learning to ”read and understand”
unstructured data on a large scale (!)

27. Confluence of trends
= the necessary conditions for
Big Data to deliver - and even
”over-deliver” - on its hype and
to transform many industries,
including the Investment
Industry

28. Breakthroughs

29. Understanding
the
meaning
of
words…

30. Big
Data
Predic5ve
Analy5cs
in
Produc5on
and
in
Distribu5on

31. Big
Data
Predic5ve
Analy5cs

32. Magic
or
OSINT?

34.
Magic
or
OSINT?

35. Big
Data
Predic5ve
Analy5cs
in
Produc5on
and
in
Distribu5on

36. The mysteries of monetizing vast streams
of unstructured language data…
From data… …to dollars?

37. MUCH BETTER TEXT ANALYTICS
MUCH BETTER PREDICTIONS
MUCH BETTER SENTIMENTS

38. Magic
or
OSINT?
wha5smonitor.com

39. Big
Data
Predic5ve
Analy5cs
in
Produc5on
and
in
Distribu5on

40. I
am
special,
but
not
very
different…

41. Watson
Personality
Profile
(IBM)
Seman'c
Profile
(Gavagai)

42. Use cases from financial industry
Distribution of investment products with
massive increases in profitability – Task &
Question
Common denominators for success
Common denominators for failure

43. Use cases from financial industry
Production of investment products –
alpha creation with big data predictive
analytics – Task & Question
Common denominators for success
Common denominators for failure

44. QUESTION 4:
Impact of Big Data
on your business?

45. Huge (transformative)
Imminent (surprising)
Winners & Losers (during the
shift – like in all major shifts)

46. Key take-aways:
Big Data has so far failed to deliver on its promise/hype – BUT
will over-deliver on its hype: Unsupervised, learning, scalable,
systems that understand the Big Data stack of language data,
enabling useful sentiment analysis and useful prediction
analytics on vast amounts of data
Predictive analytics, profiling, alias matching – use cases show
huge potential for disrupting most industries…Expect a
transformative shift in competitive landscape across most
industries – including banking
Banks – expect margin pressure, use case success stories in
analytics & opportunities for incumbents in and outside “non-
bank services” – uniquely positioned as distribution partners
Everybody can be a winner in this imminent shift – get involved
in advanced data analytics!

48. Lars Hamberg
Predictive Analytics
in The Investment Industry
BIG DATA & ANALYTICS FOR BANKING SUMMIT
December 1 & 2, 2015 | New York