Using Analytics to Create Your Own Upturn

Copyright © 2010 Accenture All Rights Reserved. Accenture, its logo, and High Performance Delivered are trademarks of Accenture.

Paul Pierotti

22 March 2011

Dublin Chamber of Commerce;

Using Analytics to Create your own Upturn

Copyright © 2009 Accenture. All rights reserved. Accenture, its logo, and High Performance Delivered are trademarks of Accenture.

Analytics – A Definition

“The intelligent use of information to

improve investment decisions, operational

performance and customer outcomes”

2Copyright © 2011 Accenture All Rights Reserved.


Questions?

• We have a broad and deep understanding of our customer base today and

what we want it to be tomorrow

• This is communicated consistently across our organisation…

• …and drives our customer experience / decisions



Analytics Function Questions?

• Our Analytics function is a good place for Career Progression

• Our Analytics function has an appropriate scale

• I would be happy for our Analytics function to present to our Board

• Analytics is embedded in our business processes

• We make the necessary investment in Analytics

• There is a clear governance structure for managing our Analytics

• I would be confident that business users of Analytics have an appropriate

understanding of how it works



Accenture’s Dublin Analytic Innovation Centre


Key services to the market place /

clients

Client demonstration and

interaction suite

R & D capability driving new market assets / offerings

Pillar 2

Innovation Engine

Pillar 3

Client Showcase

Pillar 1

Client Services

Asset Development

ProjectDelivery

ManagedService Capability

Experience

Live-ServiceExperience

Client Insight Experience

Dublin

Analytics Lab

Data & BI Services

Analytical Services

Data, Model, Method & Tool

Research

Skills DevelopmentBureau Services

Market Asset Development

Business and Technology changes offer new opportunities

to innovate through Analytics

Business Context

• Global multi-speed

recovery

• New waves of growth

• Greater volatility

• Different questions

Innovation Opportunity

1. Measure what matters, while what

matters is changing

2. Next Practice not Best Practice

3. Innovate through Decision Process

Reengineering

Technology and Data

Context

• Technology mega-trends

• Data driven innovations

What has Changed? So What? Now What?


Business and Technology changes offer new opportunities

to innovate through Analytics

Business Context

• Global multi-speed

recovery

• New waves of growth

• Greater volatility

• Different questions


1. Measure what matters, while what

matters is changing


3. Innovate through Decision Process

Reengineering

Technology and Data

Context

• Technology mega-trends

• Data driven innovations



Organisations are asking different questions

What are the key drivers

of value and risk now?

We have access to more data than ever but lack actionable insight?

How should we be using scenario planning to inform portfolio value options?

How do we become more agile to sense and exploit opportunities?

What are the right external

triggers to dynamically re-

allocate resources?

How to we organise

our analytics

resources to

optimally serve the

business?

How to leverage non

traditional sources of

data to innovate to solve

problems differently?

How do I structure and

align accountability and

decision rights optimally

across the organisation?

Business Context


Co

mp

eti

tive

Ad

va

nta

ge

Sophistication of Intelligence

Optimisation

Predictive Modeling

Forecasting/extrapolation

Statistical analysis

Alerts

Query/drill down

Ad hoc reports

Standard Reports

“What’s the best that can happen?”

“What will happen next?”

“What if these trends continue?”

“Why is this happening?”

“What actions are needed?”

“What exactly is the problem?”

“How many, how often, where?”

“What happened?”

Predictive

Analytics

With greater volatility and complexity there is a need to

improve to compete

Analytics Definition Changing the Questions

Source: Competing on Analytics: The New Science of Winning (Davenport / Harris)

Descriptive

Analytics

Analytical Competitor

Now What?

So What?

What?

Business Context


The Data Explosion offers challenges and opportunities

• 80% of business is conducted on

unstructured information - Gartner

• 85% of all data stored is held in an

unstructured format - Butler Group

• Unstructured data doubles every

three months - Gartner Group

• 54% of knowledge workers lose

productivity from inadequate

information access – Economist

Intelligence Unit

• Despite all the money spent on

decision support systems, 40% of

major business decisions are not

based not on data and facts, but on

“gut instinct” – Accenture Research

. . . and its Changing Nature

Technology and Data Context

Data Volume Growth . . .

• 15 billion intelligent, connected

devices by the year 2015

• In 2013, the Internet will be nearly

four times larger than it is in 2009 -

Cisco



Starting from the types of data available can yield

interesting innovation opportunities

• Read what people are writing?

• Hear what people are saying?

• Monitor activities on media?

• See things from the sky (geospatial data)?

• Determine where people are (location services)?

• Monitor movement (people, vehicles)?

• Understand feelings/sentiment (customer, employee)?

What would you do if you could…



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There are also key technology megatrends to consider

Mobility

Trend Implications to Consider

Emerging

Technologies

Data Explosion • Information overload tends to impede decision making

• Signal vs. noise

• Rapidly changing data ecosystem => a data intelligence capability

• Real time data tied to physical location => speed and complexity of customer

interaction

• Integration with key business processes incorporating workflow and

collaboration to enable right-time resource allocation decisions

• Virtual access and processing of data at lower cost => more fully exploit

available data

• Use and lose (more data than storage capacity) – data and analytics appliances

• Ability to influence the individual real-time during the decision making process

• Rise of sentiment and social network related data will change the way

consumer info is obtained and used

• Increase in unstructured data (voice, video, geo spatial) is changing the way

companies will need to capture, process and exploit information

• Improvements in User Interfaces will fundamentally transform the interaction

with data

Cloud

Digital



High performers are investing in analytic capabilities to

create their own upturn

Market Performance2002 – 2009

Research Findings

Companies that invest heavily in advanced analytical

capabilities outperform the S&P 500 on average by 64%

Companies that invest heavily in developing analytical skills

and adopting an analytical mindset recover quicker

from economic downturns.

Analytical Shakers*

S&P 500 Index

Source: Accenture research40%

70%

100%

130%

160%

190%

220%

250%

2002 2003 2004 2005 2006 2007 2008 2009

Accenture research has compared companies across industries and found that companies that have invested in advanced analytics capabilities significantly

outperform the market.

Analytical Capability is an integrated framework employing people, process, and

technology to utilise actionable insights to drive different performance outcomes.


Business and Technology changes are opening up new

opportunities for innovating around analytic capabilities

Business Context

Global multi-speed recovery

New waves of growth

Greater volatility

Different questions


1. Measure what matters, while

what matters is changing


3. Innovate through Decision

Process Reengineering


Technology mega-trends

Data driven innovations



Measure what matters when what matters is changing

“Not everything that can be counted

counts, and not everything that

counts can be counted”

- Albert Einstein

. . . More things can be counted than ever before but are we measuring what

matters when what matters is changing?




0 1 2 3 4 5Common Practice Next Practice

Data

Me

tho

ds

Te

ch

Pe

op

le &

Org

an

isation

Le

ad

ers

hip

&

Go

ve

rna

nce

Case

Example

XXX

XX

XX

Stand alone analytic

capabilities

Dedicated resources focused on

high value analytics

X

XX

XX

XX

Primarily functionally focused Closed loop, multi-functional,

performance driven

Voluminous; inaccurate,

inconsistentRole-based, relevant, actionableX

XX XX X

X

Limited external content Exploring, sensing, & exploitingXX

XX

XXX

XXHistoric perspective to

forecasting

Dynamic resource allocationXX

XX

X

Functionally silo’d applicationsHolistic, integrated flexible

analytic ecosystemX XX

XXX

X

XXXX

Basic & variable skillsDeep & specialised skills

optimally sourced & structuredXXX

XX

XX

XX

Reactive analytic eventsSpeed to actionable insight

(“burden of proof”)X

XX

XXX

“Analysis paralysis” Integrated capabilities managed

as an enterprise assetXX

XXX

XXX

Simple, ad-hoc analysis Scenario planning as a dynamic

capabilityX

X XXX

Project based analytics in

functional pockets

Cross functional analytic

services portfolioXXX

X

Short term focus to

management processes

Management process & forums

for strategic & tactical decisionsXXX

XXX

Next Practice not Best Practice

One size internally focused Flexible with external influence

Managed in Silos Enterprise asset

Build, Test, Deploy, Own Spawn, Use, Benefit, Lose

Tool & capability managed Business outcome managed

X = Assessment from interviews



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http://www.pandg.com/en_US/index.jhtml

4. Value Realisation

• Measures the quality of the decision and the root cause of a course correction

3. Execution

• Determine interventions to execute and resources to allocate

2. Process

• Embedded analytical processes to generateand validate key insights

Innovate through Decision Process Reengineering

Marketing Supply

Chain

HR Manufact-

uring

Finance

Performance

Monitoring

Value

RealisationExecution

Insight

Validation

Insight

Generation

Questions on

Key Metrics

Technology Enablers

Core Analytics

Functional Analytics

Cross Functional Analytics

Analytics COE

Weekly

Forecast

Adjustment

Monthly

Business

Review

Quarterly

Health

Check

BI and

Packaged

Workbench

Root Cause

Analysis Tools

Statistical

Models

Optimisation

Tools

1. Strategy

• Focus on the key questions to manage business performance


1

1

234

2

3

4

Definition of Decision Process Reengineering

“... the fundamental rethinking and radical redesign of cognitive processes in organisations, that result in the selection of a course of action, to allocate resources or to choose to do nothing, to achieve dramatic improvements in business performance”


Leaders need to set an example


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Questions


Contact

Paul Pierotti

[email protected]

086 809 4075


mailto:[email protected]