The document discusses how businesses can design their structure and processes to leverage big data, social media, and mobile technologies. It recommends taking a "decision first" approach where business processes are designed around automating operational decisions using rules and analytics. This allows processes to have straight-through processing instead of being interrupted by human decisions at each step. The document also discusses how businesses can build "smart" systems using capabilities like business rules management, analytics, predictive modeling, and business process management to gain insights from large amounts of customer data and improve decision-making.

1. BUSINESS
DESIGN
For
Big-­‐Data
Driven,
Social
&
Mobile
Consumer
A
Decision
Management
Approach
Gagan
Saxena
(@Gagan_S)
October
16,
2012

2. Agenda
1. Business
coping
with
Challenges
of
Big-­‐Data
and
the
Social,
Mobile
Consumer
2. Not
a
Technology
Issue
3. Business
Design
–
Structure,
Processes
and
Roles
4. “Decision
First”
Approach
5. Three
Steps
to
Success
2

3. Business
Squeezed
in
the
Middle
Consumer
Evoluon
Technology
Choices
3

4. Big
Data
Challenge
• Velocity
– More
rapid
arrival
• Volume
– Of
much
more
data
• Variety
– In
many
more
formats
– …
including
unstructured
and
semi-­‐structured
4

5. ACE
Enterprises:
The
Story
So
Far….
• Established
Business
in
Retail
Distribuon
• Facing
challenges
from
nimbler
Competors
• Struggling
to
keep
more
demanding
Customers
• Need
to
do
something.
Quickly.
• Department
Heads
go
out
to
develop
Responses.
5

6. ACE
Enterprises
Responds
to
Challenges
1. Marke(ng
ramps
up
Social
Media
team
and
‘buys’
two
new
Markeng
Management
tools
2. Sales
recommends
a
Loyalty-­‐Based
Discounng
3. Product
Development
develops
Cross-­‐Sell/
Up-­‐Sell
packages
to
maximize
Margins
4. Opera(ons
develops
Rules
for
more
efficient
Shipping
and
for
roung
Customer
Care
calls
5. Accoun(ng
tries
yet
another
Performance
Management
Dashboard
6. Technology
signs
up
for
more
BI,
BPM,
BRMS
and
Infrastructure
to
go
with
it
6

7. ACE
Enterprises
is
Stuck
• Too
many
Projects
have
been
Started
• New
‘ad-­‐hoc’
Roles
&
Procedures
have
been
Created
• Time
and
Money
is
running
out
• Daily
ops
are
suffering
since
Projects
have
priority
• ACE
is
no
bejer
off.
Things
are
worse.
7

8. A
Fresh
Look
Redesign
Consumer
Experience
And
Create
a
“SMART”
System.
8

9. What
is
a
‘Smart’
System?
Business
Rules
Management
Analycs
Big
Data
Predicve
Modeling
Complex
Event
Processing
Natural
Language
Processing
Business
Process
Management
How
do
we
bring
these
capabilies
into
the
Organizaon?
9

10. 10

11. 11

12. 12

13. “Informaon
Technology”
coined
in
1958
(US-­‐BEA).
Double
every
18
months
per
Moore’s
Law.
In
2006,
we
moved
into
the
Second
Half
of
the
Chessboard.
“Exponenal
Growth
has
confounded
our
intuion
&
expectaons.”
13

14. Soluon?
“The
soluon
is
organizaonal
innovaon:
co-­‐invenng
new
organizaonal
structures,
processes,
and
business
models
that
leverage
ever-­‐advancing
technology
and
human
skills.”
Where’s
the
Manual?
14

15. The
Classics
on
Business
Process
Design.
Assume
‘Classic’
(Legacy)
Technology.
15

16. Management
by
Objecves
(1954)
"Objecves
are
needed
in
every
area
where
performance
and
results
directly
and
vitally
affect
the
survival
and
prosperity
of
the
business."
Create
Customers!
16

17. ”Linking
Strategy
to
Operaons”
17

18. Designing
New
Validated
Business
Models
Learning
18

19. New
Approach
to
Business
Process
Design.
Decisions,
First.
19

20. Business
is
a
Series
of
Decisions
Strategic
Decisions
• Few
in
number,
large
impact
• Should
we
acquire
this
company
or
exit
this
market?
Tac(cal
Decisions
• Management
and
control,
moderate
impact
• Should
we
re-­‐organize
this
supply
chain,
change
risk
mgt
approach?
Opera(onal
Decisions
• Day-­‐to-­‐day
decisions
that
affect
one
transacon
or
customer
• Best
offer
for
this
customer?
How
risky
is
this
loan?
Is
this
claim
fraudulent?
20

21. Change
in
Processing
Paradigm
Interrupted
Processing
A
B
C
Process
Human
Process
Human
Process
Decision
Decision
Straight
Through
Processing
Automated
Decisions
A
C
Manage
Rules
&
B
Handle
Excepons
21

22. Business
Rules
...statements
of
the
acons
you
should
take
when
certain
business
condions
are
true.
22

23. Business
Rule
Formats
23

24. The
Analycs
Spectrum
Business
Intelligence
Data
Mining
Predicve
Analycs
X
X
XX
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
How
do
I
use
data
to
Who
are
my
best/ How
are
those
learn
about
my
worst
customers?
customers
likely
to
customers?
What
has
How
do
I
turn
my
data
behave
in
the
future?
been
happening
in
my
into
rules
for
bejer
How
do
they
react
to
business?
decisions?
the
myriad
ways
I
can
“touch”
them?
Knowledge
-­‐
Descrip1on
Ac1on
-­‐
Prescrip1on
24

25. Analycs
DRIVE
Acon
Operational
Systems THE CRUCIAL LINK
For being Agile & Adaptive
Decision
Analytic
Systems Business
relies
on
Experts
to
manage
this
Link
CANNOT
SCALE!
25

26. From
Decisions
to
Data
Data
Analyc
Insight
Decisions
NOT
the
other
way
around!
26

27. ACE
Enterprises
are
Ready
for
the
3
Steps!
Discover
Build
Improve
27

28. 1.
Discover
Decisions
Hint:
Operaonal
Decisions
are
Everywhere
28

29. Idenfy
&
Understand
Decisions
✓ Name
✓ Descripon
✓ A
queson
✓ A
defined
set
of
allowable
answers
✓ Key
facts
like
volume,
complexity,
latency
29

30. Decompose
Decisions
What
is
required
to
make
Decisions?
q Guidelines,
Policy
Documents
q Human
Experse
q Regulaons
q Exisng
System
Logic
q Analyc
insight
q Data
Describing
the
Case
q External
Reference
Data
q Results
of
other
decisions
30

31. Analyze
Knowledge
&
Decision
Dependencies
31

32. 2.
Build
Decision
Services
Once
Decisions
have
been
defined,
Build
the
Technical
Components
that
encapsulate
Decisions
Straight
Through
Processing
Automated
Decisions
A
C
Manage
Rules
&
B
Handle
Excepons
32

33. 3.
Improve
Decisions
• Learning
from
the
Results
of
Decision
Execuon
– Good
Decisions?
– Bad
Decisions?
• Running
Experiments
– Use
experimental
configuraon
on
small
parts
of
the
business
and
compare
(A/B
Tesng)
• Advanced
Analycs
– Find
Pajerns
in
Data
– Extrapolate
Data
33

34. ACE
Enterprises
&
Decision
Management
Strategy
New
Roles
&
Workflows
Aligned
with
Decision
Model
Automated
Decisions
Rules
Experts
Opmize
Technology
Rules
Raonalized
Technology
Architecture
Automated
Run
Base
&
Predicve
Processes
Analycs
34

35. QUESTIONS?
35