1. The document discusses how the second wave of big data will automate actions based on data-driven insights and perspectives, unlike the first wave which showcased analysis. 2. It provides examples of how companies are using technologies like machine learning, artificial intelligence and sensors to gain insights from non-structured data and improve industries like finance, transportation and supply chain management. 3. The use of robo-advisors and automated investment services is expected to grow significantly in the coming years to better serve investors.

1. W H AT T H E S E C O N D WAV E O F
B I G D ATA W I L L D O F O R F I N T E C H
T 3 F I N A N C E F E S T * N O V E M B E R 8 , 2 0 1 5
D AV I D A F E R I AT, C O - F O U N D E R , M A N A G I N G PA RT N E R
A C T I O N A B L E M A R K E T I N T E L L I G E N C E

2. “ I N T H E N E X T 1 0 Y E A R S , E V E RY O N E W I L L B E
U S I N G S O M E F O R M O F A U T O M AT E D I N V E S T M E N T
S E R V I C E . T H I S I S L I K E E - C O M M E R C E I N T H E ’ 9 0 S . ”
W E A LT H F R O N T C E O A D A M N A S H

3. S O U R C E : I D C : W O R L D W I D E B U S I N E S S A N A LY T I C S S O F T WA R E 2 0 1 2 – 2 0 1 6 F O R E C A S T
B I G D ATA G E T T I N G B I G G E R
Available for automation

4. I N 2 0 1 2 , F I T B I T R E C O R D E D
S A L E S T O TA L I N G $ 7 6
M I L L I O N . I N T H E M O S T
R E C E N T F U L L Y E A R , 2 0 1 4 ,
T O TA L S A L E S W E R E
W O R T H $ 7 4 5 M I L L I O N .
F I T B I T I N C . ( N Y S E : F I T )
S O U R C E : S M A R T E R A N A LY S T. C O M
N O N - S T R U C T U R E D B I G D A TA
E X A M P L E S : P R O D U C T
U S A G E , S U P P LY C H A I N ,
N A V I G A T I O N

5. R A D I O F R E Q . I D S O L U T I O N S E N A B L E
A I R L I N E T E C H N I C I A N S T O G E T
I N F O R M AT I O N B Y WA L K I N G T H E
C A B I N S C A N N I N G F O R R F I D PA R T S
R E D U C I N G I N V E N T O RY A N D
M A I N T E N A N C E L E A D T I M E B Y M O R E
T H A N 9 0 % - D O I N G I N 3 M I N U T E S
W H AT T O O K P R E V I O U S LY 3 H O U R S .
A E R O S PA C E & S U P P LY C H A I N
S O U R C E : M A I N TA G

6. I N 2 0 1 2 S P E C I A L I Z E D L I D A R
L A S E R S E N S O R S T H AT G O O G L E
U S E S O N I T S A U T O N O M O U S
V E H I C L E S C O S T M O R E T H A N
$ 7 0 , 0 0 0 . T H I S Y E A R , T H E
M A N U FA C T U R E R R E L E A S E D A
M I N I AT U R I Z E D V E R S I O N T H AT
C O S T S O N E - T E N T H T H E P R I C E .
D R I V E R L E S S A U T O M O B I L E S
S O U R C E : K L E I N E R P E R K I N S C A U F I E L D & B Y E R S , J A N U A RY 2 0 1 5

7. R O B O - A D V I C E P L AT F O R M S W I L L R E A C H $ 4 8 9
B I L L I O N I N A U M B Y 2 0 2 0 , A N E Y E - C AT C H I N G
2 , 5 0 0 % I N C R E A S E F R O M T H E $ 1 8 . 7 B T O D AY
S O U R C E S : C E R U L L I A S S O C I AT E S I N C . , “ R E TA I L D I R E C T F I R M S A N D D I G I TA L A D V I C E P R O V I D E R S 2 0 1 5 ” 1 1 / 4 / 2 0 1 5 ,
T W E E T, D O W N T O W N J O S H B R O W N @ R E F O R M E D B R O K E R , 1 1 / 5 / 2 0 1 5
“ T H E R E A R E 3 0 0 , 0 0 0 F I N A N C I A L A D V I S O R S I N T H E U S A N D
O N E T H I R D W I L L B E R E T I R I N G I N T H E N E X T T E N Y E A R S .
I H AV E M Y W O R K C U T O U T F O R M E . ” @ R E F O R M E D B R O K E R

8. 2 D I F F E R E N T S E G M E N T S .
W H O W I L L O F F E R B O T H ?
Stock
Active
Fractional
Work/Play
Idea Generation
Alpha Capture
2 A P P R O A C H E S T O
R O B O S E R V I C E S ,
:Scale
:Behavior
:Portfolio %
:Goal
:Key Activity
:Outcome
A C T I O N A B L E M A R K E T
I N T E L L I G E N C E
Portfolio
Passive
Majority
Accumulate
Index Tracking
Rebalance

10. 1. K N O W L E D G E , P R A C T I C E
2. A C C E S S
3. Q U A L I T Y O F D E C I S I O N S
4. B AT T L E O F E G O , P S Y C H O L O G Y
W H O H A S T H E T I M E O R D E S I R E ?
B A R R I E R S T O E N G A G I N G T H E
M A R K E T S :

11. " W H E N T H I N G S A R E B A D , T RY N O T T O PA N I C .
B U T I F T H E R E ' S N E E D T O PA N I C , PA N I C E A R LY. ”
@ P R E S T O N M O O R E O N T H E C Y C L E S O F E G O & E M O T I O N :
Social Cycle
Business Cycle

12. “ F I R S T WAV E O F B I G D ATA S H O W C A S E D T H E I N S I G H T
F R O M A N A LY S I S A N D P E R S P E C T I V E .
T H E S E C O N D WAV E O F B I G D ATA I S A U T O M AT I N G
A C T I O N S B A S E D O N S U C H P E R S P E C T I V E A N D D ATA . ”
D A V I D A F E R I A T, @ T R A D E I D E A S

13. PAT T E R N R E C O G N I T I O N
H O W T R A D E I D E A S D O E S I T:

14. A I D I R E C T E D E V E N T- B A S E D B A C K T E S T
H O W T R A D E I D E A S D O E S I T:
S O U R C E : T R A D E I D E A S B A C K O F F I C E , A I I N P U T R E P O R T

15. F I E L D I N G T H E B E S T T E A M D A I LY
H O W T R A D E I D E A S D O E S I T:

16. I N F O R M AT I O N A N D D ATA D E P E N D
O N T H E P E R S P E C T I V E F R O M W H I C H
W E L O O K AT T H E M

17. W H AT C A N T H I S D O ?
Q U E S T I O N :

18. R I S E O F T H E C O N C I E R G E B O T
S O U R C E : “ F O R G E T A P P S , N O W T H E B O T S TA K E O V E R ” S E P 2 9 , 2 0 1 5 B Y B E E R U D S H E T H

19. W E ’ R E R E C L A I M I N G F O R I N V E S T O R S
C O N T R O L O F T H E I R E N G A G E M E N T W I T H
T H E M A R K E T S .
A C T I O N A B L E M A R K E T
I N T E L L I G E N C E