Giving Organisations

new Capabilities

to ask the Right

Business Questions

Stephen Simpson

CTO Office

stephen@ssimpson.net

@sharplyunclear

Value Captured

Sales Growth Profit Growth

Sales Growth to Existing

CustomersNew Customers

Product PerformanceTechnology

Leadership

Process

Improvement

Effective Project Execution

Balanced Innovation

Portfolio

Quality of Innovation

PipelinePartners’

Value-add

Employee

Commitment to

Innovation

Supportive Strategy,

Structure, & Systems Access to Talent

Outputs

Outcomes

Processes

Inputs

Making Data Work is Hard

Myron Ullman, CEO

Ron Johnson, CEO

The “All In” Approach

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.”

"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.”

The Experimental 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.

The “Wait and See” Approach

Can rarely evaluate all outcomes with sufficient precision

Usually don’t know relevant probabilities of outcomes

Possess limited memory

Satisficing

Results are often Modest

Business

StrategyDomain

Expertise

Data Mining

Company Systems & Data

Sourcing

Extraction

Interpretation

Visualisation

Implementation

Agile

Experimentation

We need to make

sure that we’re

asking people to

research the right

questions

We need to look in

many more places to

find data…

…and it will take a lot of

different skills and

approaches to bring it

together

We need to be careful to

curb our enthusiasm and

separate out the signal

from the noise

We need new techniques to

interpret and manipulate vast

numbers of data points on a

single surface

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

We need to perform analysis

quickly inside small projects,

with a specific business goal.

Some of these will fail.

And then we iterate to improve the

insight gained, or address the next

business question…

We need to choose the

right storage technologies,

integration services &

architecture

Business

Strategy

Obtaining new insights

CRISP-DM

Candidate Sources

Richards J. Heuer &

Randolph H. Pherson

Structured Analysis

Expert Judgment

Quantitative

Methods using

Expert-Generated

Data

Quantitative

Methods using

Empirical Data

Decomposition &

Visualisation

Idea Generation

Scenarios & Indicators

Hypothesis Generation

& Testing

Assessment of

Cause & Effect

Challenge Analysis

Conflict Management

Decision SupportStructured Analysis

Analytic Methods

13

a step by step process for analyzing the kind of

incomplete, ambiguous and sometimes deceptive

information that analysts must deal with.

Structured Analysis

16

Diagnostic + Contrarian + Imagination elements

Structured Analytic Techniques contain

The Techniques

1. Define the

project?2. Get started?

3. Examine & make

sense of the data?

Figure out what is

going on?

4. Assess the most

likely outcome of an

evolving situation?

5. Monitor a

situation to avoid

surprise?

9. Challenge your

own mental model?

6. Generate and test

hypotheses?

7. Assess the

possibility of

deception?

8. Foresee the

future?

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?

Decomposition & Visualisation

Idea Generation

Scenarios & Indicators

Hypothesis Generation

& Testing

Assessment of

Cause & Effect

Challenge Analysis

Conflict Management

Decision Support

1. Define the

project?

Choosing what you want to doDecomposition & Visualisation

Idea Generation

8. Foresee the

future?

Scenarios & Indicators

Hypothesis Generation

& Testing

Assessment of

Cause & Effect

Challenge Analysis

Decision Support

6. Generate and test

hypotheses?Hypothesis Generation

& Testing

Assessment of

Cause & Effect

12. Support a

manager in

deciding course of

action?

Hypothesis Generation

& Testing

Conflict Management

Decision Support

Template Structure

20

Overview

When to Use It

Value Added

The Method

Relationship to other Techniques

Origins of this Technique

21

Personalised Interactions

Long term

personal healthcare

Branded Currency

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.

1. Decomposition & Visualisation

Client micro-segmentation using multiple sources of data

Scenarios

Description

Understand your clients’ needs at the finest level of detail

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

• 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

Value Proposition

The best way to have a good idea is to have a lot of ideas

2. Idea Generation

Out-of-box thinking

Raw & refined ideas

Experimentation

Ambiguity/uncertainty

Research

Intuition

Surprise

Courage

Find the right things

Ask questions & explore

unknown innovation

Seize opportunities

Visualize future & consider

all options

Include incremental &

radical ideas

In-the-box thinking

Engineering/process

improvement

Precision

Well-calculated trade-offs

Buying/selling of ideas

Do things right

Answer questions & verify

solutions

Avoid major risks

Get product into the

marketplace

Bias for incremental

Creativity Value Creation

Cross Impact Matrix

For when “Everything is connected to everything else”

Business is in flux

System is stable

- Need to identify and monitor all

factors that might upset this

A significant event has occurred

- Need to understand implications

Context for discussion of interactions

Discover variables once thought to be simple

& independent are actually interrelated

Focus on

- Interactions that may have been overlooked

- Variables that might reinforce each other

A B C D E F

A. Personalised Interactions ++ ++

B. Existing mobile solutions --

C. Existing core banking solutions -- ++ -

D. Apps & Cloud Service interaction + - + +

E. Offers ++ - ++ ++

F. Analytics ++ ++

Cross Impact Matrix

3. Scenarios & Indicators

Scenarios are plausible &

provocative stories about how

the future might unfold

Observable Phenomena that can periodically be reviewed to help track events

Make humans recognize early signs

significant change

Spot emerging trends

- Warn unanticipated changes

- Avoid surprise

Forward looking, predictive

Objective baseline for tracking

Instil rigour into analytic process

Enhance credibility of what delivered

Exchange knowledge between experts

from different domains

Quality indicators are critical

- If narrowly defined or out of date

- Reinforce bias

- Discard new evidence

- Lull people inappropriately

- Dashboards…

Indicators Validator

- Quality and strength of indicator

- Whether appears in all scenarios

Indicators

Indicators

Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

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

Strong

Substantial

Moderate

Low concern

Neglible concern

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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

4. Hypothesis Generation & Testing

We are slow to accept the reality

of simple mistakes, accidents,

unintended consequences,

coincidences, or small causes

leading to large effects

5. Assessment of Cause & Effect

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

It is the mark of an educated mind to be able to entertain a

thought without accepting it.

6. Challenge 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

Pre-mortem analysis

Examples

- Internal inertia or uneven execution

- Competitors’ actions

- Law of unintended consequences

- Economic changes

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

…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.

8. Decision Support

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…

Iterating Quickly

Time is Key

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/

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

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In Summary