2. Making Data Work is Hard
Value Captured
Outputs
Outcomes
Sales Growth
Profit Growth
Sales Growth to Existing
Customers
Product Performance
Technology
Leadership
New Customers
Process
Improvement
Effective Project Execution
Processes
Inputs
Balanced Innovation
Portfolio
Supportive Strategy,
Structure, & Systems
Partners’
Value-add
Employee
Commitment to
Innovation
Quality of Innovation
Pipeline
Access to Talent
3. The “All In” Approach
Ron Johnson, CEO
Myron Ullman, CEO
4. You start out thinking you have a sales problem but might find
it is not really sales but marketing or customer retention...
…you could spent a lot of time on analysis that doesn’t lead to
solving the right problem.”
5. The Experimental Approach
"We did a Hadoop trial last year, it didn't go very far
because we weren't getting the intelligence out of it
that we thought we would. So we are looking at some
other initiatives with different vendors this year.
"We tried to put three different data sets together, and
then tried to see if we could find some causality
between the data sets that would gives us intelligence
that would allow us to manage our operations better…
"Whether that was how we set the trial up or the
software I don't know, so we are going to try
some different things.”
6. The “Wait and See” Approach
Incumbents are rarely disrupted by new technologies they can't
catch up to, but instead by new business models they can't match.
Institutions will try to preserve the problem to which they are
the solution.
7. Satisficing
Can rarely evaluate all outcomes with sufficient precision
Usually don’t know relevant probabilities of outcomes
Possess limited memory
9. Obtaining new insights
Business
Strategy
We need to make
sure that we’re
asking people to
research the right
questions
Domain
Expertise
Company Systems
& Data
And then we iterate to improve the
insight gained, or address the next
business question…
Data Mining
Agile
Experimentation
We need to choose the
right storage technologies,
integration services &
architecture
Sourcing
We need to look in
many more places to
find data…
Extraction
…and it will take a lot of
different skills and
approaches to bring it
together
We need to perform analysis
quickly inside small projects,
with a specific business goal.
Some of these will fail.
We need to be careful to
curb our enthusiasm and
separate out the signal
from the noise
Interpretation
Implementation
We need simple, easy to
use production tools to act
upon the new insights.
Authority needs to be
delegated to where the
information is captured
Visualisation
We need new techniques to
interpret and manipulate vast
numbers of data points on a
single surface
12. Analytic Methods
Decomposition &
Visualisation
Idea Generation
Expert Judgment
Scenarios & Indicators
Quantitative
Methods using
Expert-Generated
Data
Quantitative
Methods using
Empirical Data
Hypothesis Generation
& Testing
Assessment of
Cause & Effect
Challenge Analysis
Conflict Management
Structured Analysis
Decision Support
14. Structured Analysis
a step by step process for analyzing the kind of
incomplete, ambiguous and sometimes deceptive
information that analysts must deal with.
17. Choosing what you want to do
1. Define the
project?
2. Get started?
Decomposition & Visualisation
Decomposition & Visualisation
3. Examine & make
sense of the data?
Figure out what is
going on?
Idea Generation
Scenarios & Indicators
4. Assess the most
likely outcome of an
evolving situation?
5. Monitor a
situation to avoid
surprise?
6. Generate and test
hypotheses?
Hypothesis Generation
& Testing
Assessment of
Cause & Effect
7. Assess the
possibility of
deception?
8. Foresee the
future?
9. Challenge your
own mental model?
Challenge Analysis
Conflict Management
10. See events from
the perspective of
other players?
11. Managing
conflicting mental
models or
opinions?
12. Support a
manager in
deciding course of
action?
Decision Support
21. 1. Decomposition & Visualisation
When forced to work within a strict framework the imagination
is taxed to its utmost – and will produce its richest ideas.
Given freedom the work is likely to sprawl.
22.
23. Value Proposition
Understand your clients’ needs at the finest level of detail
Client micro-segmentation using multiple sources of data
Description
FOR marketing operations
WHO want to understand the growth potential for each identified customer subdivision
THE understand your clients’ needs at the finest level of detail solution
PROVIDES understanding of the root causes for your current share of each identified slice
THAT lets you act on the information quickly with targeted retail product placement & location selling
UNLIKE your existing solution
WHICH is coarse-grained and retrospective
Scenarios
•
•
•
•
•
Retail product placement & location selling
Counteracting effectiveness of competitors
Understanding local reputation via ”voice of the customer”
Real-time decision making such as mobile-based coupon positioning to particular segments
Partner organisations’ service effectiveness
26. Creativity
Value Creation
Out-of-box thinking
In-the-box thinking
Raw & refined ideas
Experimentation
Engineering/process
improvement
Ambiguity/uncertainty
Precision
Research
Well-calculated trade-offs
Intuition
Buying/selling of ideas
Surprise
Do things right
Courage
Answer questions & verify
solutions
Find the right things
Ask questions & explore
unknown innovation
Seize opportunities
Visualize future & consider
all options
Include incremental &
radical ideas
Avoid major risks
Get product into the
marketplace
Bias for incremental
27. Cross Impact Matrix
For when “Everything is connected to everything else”
Business is in flux
Context for discussion of interactions
System is stable
Discover variables once thought to be simple
- Need to identify and monitor all
factors that might upset this
A significant event has occurred
- Need to understand implications
& independent are actually interrelated
Focus on
- Interactions that may have been overlooked
- Variables that might reinforce each other
28. Cross Impact Matrix
A
B
C
C. Existing core banking solutions
D. Apps & Cloud Service interaction
E
F
++ ++
A. Personalised Interactions
B. Existing mobile solutions
D
--- ++
+ -
E. Offers
++
F. Analytics
++
-
+
+
++
++
++
29. 3. Scenarios & Indicators
Scenarios are plausible &
provocative stories about how
the future might unfold
30.
31. Indicators
Observable Phenomena that can periodically be reviewed to help track events
Make humans recognize early signs
significant change
Spot emerging trends
Quality indicators are critical
- If narrowly defined or out of date
- Reinforce bias
- Warn unanticipated changes
- Discard new evidence
- Avoid surprise
- Lull people inappropriately
Forward looking, predictive
Objective baseline for tracking
- Dashboards…
Indicators Validator
Instil rigour into analytic process
- Quality and strength of indicator
Enhance credibility of what delivered
- Whether appears in all scenarios
Exchange knowledge between experts
from different domains
32. Indicators
2013
Q4
Q3
Mobile Offers
Reaching right segment
People engaged
Volunteering information
Infrastructure
Holding initiative back
Cloud
Security, regulatory, compliance
Service
Take-up standard services
3rd party composing new apps
Industry Trends
Personalised CRM
Branded Currency
Device as Bank
Ecosystem
Retailers using your backbone
Competitive launches
Q1
□
●
▫
□
▫
▫
□
○
○
○
Q2
●
▪
□
▫
▫
Q1
●
▪
□
●
▫
Q4
2015
Q3
□
●
□
●
□
□
Q2
2014
Q3
Neglible concern
Low concern
Moderate
Substantial
Strong
▫
▪
□
○
●
Q4
33. 4. Hypothesis Generation & Testing
A possible explanation of the past or a judgment about
the future is a hypothesis that needs to be tested by
collecting and presenting evidence
34.
35. 5. Assessment of Cause & Effect
We are slow to accept the reality
of simple mistakes, accidents,
unintended consequences,
coincidences, or small causes
leading to large effects
36.
37. Personalised Interactions will increase: Key Assumptions check
Legal and privacy – Caveated.
Components available across entire chain – Caveated.
Customers want seamless, personally relevant services – Solid
Devices will progress sufficiently – Solid
Analytics techniques are sufficiently refined, accurate and timely – Caveated
Back-end systems will support workload – Solid
Systems will be cost effective – Caveated. What’s the ROI of something you don’t know?
Employees trained and authority delegated to act – Unsupported
38. 6. Challenge Analysis
It is the mark of an educated mind to be able to entertain a
thought without accepting it.
39.
40. Pre-mortem analysis
Imagine the future where your plan has been implemented, but has failed
Advantages:
Take people out of perspective of
defending their plan & shielding
themselves from its flaws
Increase level of candour
Can be used to show decision makers
that are typically over-confident that
their decisions and plans will work
Questions re-framed, to elicit different
responses to original ones
Legitimises dissent – asked to make a
positive contribution by identifying
weaknesses in previous analysis
Examples
- Internal inertia or uneven execution
- Competitors’ actions
- Law of unintended consequences
- Economic changes
41.
42. 7. Conflict Management
Disagreements sparked by differences in perspective, competencies,
& access to information… actually generate much of the value that can
come from collaboration across organisational boundaries.
http://hbr.org/2005/03/want-collaboration-accept-and-actively-manage-conflict
43.
44. 8. Decision Support
…without overstepping the limits of their role…; just structures all the
relevant information in a format that makes it easier for the decision
maker to make a choice.
45.
46. A word on Dashboards
It is also unfortunate to see how many business intelligence
and enterprise data warehousing projects get waylaid by the
singular pursuit of pretty dashboards…
49. Self-Inflicted Complexity
When we sacrifice dealing with detail complexity to
focus on dynamic complexity, the solutions don’t
produce the outcomes that we really want.
http://blogs.hbr.org/2013/09/our-self-inflicted-complexity/
50. In Summary
Does provide new capabilities to ask right questions
- Offers path to clearer business goals
- Discourages “wait and see” approaches
Encourages cross-organisational linkages
Validates or challenges experts’ “hunches”
More limited use in monitoring subsequent change
12