9. fri 1045 1130 griffin - starting the sustainable analytic journey

Deloitte’s Analytics
Symposium 2010
Starting the sustainable
analytic journey
Jane Griffin

October 2010

Agenda

• Analytics vision
• Key analytics principles
• Keys to successful analytic initiatives

1 Starting the sustainable analytic journey Copyright © 2010 Deloitte Development LLC. All rights reserved.

Analytics vision

The process of delivering analytics business results is one of continuous
improvement. Starting anywhere in the analytics cycle, an organization
can immediately begin to address its specific needs.


Sustaining analytics

Deep analytics pushes insights to frontline decision makers by embedding
capabilities and outputs deep in the processes of everyday work. But that process
has to be wrapped with serious change management for it to stick.
Analyze Manage Measure Manage
Set Strategy Operationalize
Strategy Results Operations Data

Set and Predict the Architect and Manage Provide Identifying
communicate outcome of the integrate performance “alerts,” sources of key
enterprise strategy and predictive with metrics identify the data, govern,
strategy integrate into models into and processes problem and integrate,
operations process, via analytical recommend cleanse, and
technology, and applications corrective organize then
workforce action deliver it

Design
Direct Sense
Direct Architect Measure Operate Govern
Direct Direct
Direct Direct Direct Direct
Direct Direct Direct Direct

Communicate Predict Integrate
Direct Monitor Report Monitor
Direct Direct Direct Direct Direct
Direct Direct Direct Direct Direct Direct

Integrate Implement Correct Respond Correct
Direct Direct
Direct Direct
Direct Direct Direct
Direct Direct Direct


Link analytics to high-impact areas

A full-scale shift toward analytics is underway today in almost every domain.
Things have gotten so complicated, that shooting from the hip has lost its allure.
For some that is a welcome change. For others it is something to resist. It all
depends on perspective. Every domain must be optimized to meet demands.
Product Management
• Product Pricing, Product Profitability, Portfolio
Optimization
Market/ Product Customer Management
Sales Mgmt Mgmt
• Lifetime Value, Customer Segmentation, Loyalty,
Customer Experience, Profitability, Churn
Service/Operations Management
Supplier/ Customer • Customer Experience, Capacity Planning/Demand
Advanced
Partner Mgmt Forecasting, Capital Expenditure, Performance,
Analytics
Mgmt
Leakage/Shortfall, Workforce Effectiveness
Enterprise Management
• Fraud, Revenue Assurance, Asset Utilization, Security,
Enterprise Service/
Collections, Advanced Forecasting
Mgmt Ops Mgmt Supplier/Partner Management
• Vendor Efficiency, Contract Compliance, Vendor
Optimization
Market/Sales Management
• Channel Optimization, Campaign Performance, Up
selling, Cross-selling


Weave into organization fibers

No matter where you enter the cycle of
business analytics — analytics insight,
performance optimization, information
management — the challenges of
execution and adoption are similar.
Organization that have mastered
applying analytics to support enterprise
decision-making tend to focus on seven
key principles as they strive to
accelerate workforce adoption and
capture more value.


Weave into organization fibers (cont’d)

The challenge today is to push
analytics capabilities even deeper into
the organization. from business
executives to the front lines in a
coordinated fashion.
This requires both a new level of focus
and dexterity. To be effective,
organizations must employ the right
discipline, methods, personnel,
technologies, and data.


Key analytics principles

Start where Assess your current capabilities, get a clear picture of the gaps. Focus
you are on low-hanging fruit and develop a long-term analytics road map

Crunchy Know which questions matter most to your industry, strategy,
questions and priorities

Detect and respond to signals better and faster is the key to achieving
Signal strength
competitive advantage

Accelerating Automate delivery of the information — to management, operational,
insights and analytical processes

User engagement Output must deliver insights people need — in whatever forms they
and visualization need – to make fact-based decisions

Fact-driven Embed analytics capabilities into decision-making processes matching
culture your approach with your style

Right fit analytics Match statistical and analytics techniques to the job at hand.


Assess and set goals

An honest self-assessment is the first step in understanding your current
capabilities — and the gaps you’ll need to close to get more value from
your analytics investments. Think in terms of both technical capabilities
and organizational depth.
Nonexistent Developing Defined Advanced Leading
• Hindsight driven • Functional level • Competitive • Analytics key • Analytics-driven
Advanced
Analytics

• No market insight • Across few domains advantage analytics • Analytics incentives strategy
• No analytic • Deliver token results • Executive support • Operationalized • Global analytics
applications • Analytics isolated • Analysts disparate • Leveraging analytical • Analytics integrated
• Analytics isolated tools • Analytics mainstream
• Excel-based • • • • PM is analytics-driven
Management Intelligence Management
Business Performance

Market driven KPIs defined Integrated PM
• Budget analysis • Finance has PM skills • Linked KPIs • Value-lined KPIs • PM COE disseminate
• Process-level • Aspiration for more • Sophisticated skills • Aligned incentives skills
decisions • Scorecards tools • Standardizing PM • Integrated PM • Predictive
modeling/PM
• BI limited to reporting • BI skills in IT • BI strategy defined • BI governance • Strategy driven
• BI skills basics • Data unreliable • BI-driven strategy • BI/BA skills in IT • BI/BA resources
• BI isolated • Dedicated BI team • BI architecture • Standardized BI tools
• DW established • Owners of BI • Full level of BI tools • Information-driven

• No governance • Limited DM skills • DM governance • Full data governance • High-quality
• Little data quality • Developing DM • EIM architecture • DM team integrated data/MDM
Data

• No MDM processes • Data quality/MDM • MDM implemented • Skilled DM resources
• No IT BI/DM skills • Recognize need • Dedicated DM team • Mature DM tools • IBI/DM governance
• Standard DM


Assess and set goals

An honest self-assessment is the first step in understanding your current
capabilities — and the gaps you’ll need to close to get more value from
your analytics investments. Think in terms of both technical capabilities
and organizational depth.
Nonexistent Developing Defined Advanced Leading
• Hindsight driven • Functional level • Competitive • Analytics key • Analytics-driven
Advanced
Analytics

• No market insight • Across few domains advantage analytics • Analytics incentives strategy
• No analytic • Deliver token results • Executive support • Operationalized • Global analytics
applications • Analytics Isolated • Analysts disparate • Leveraging analytical • Analytics integrated
• Analytics isolated tools • Analytics mainstream
• Excel-based • • • • PM is analytics-driven
Management Intelligence Management
Business Performance

Market driven KPIs defined Integrated PM
• Budget analysis • Finance has PM skills • Linked KPIs • Value lined KPIs • PM COE disseminate
• Process-level • Aspiration for more • Sophisticated skills • Aligned incentives skills
decisions • Scorecards tools • Standardizing PM • Integrated PM • Predictive
modeling/PM
• BI limited to reporting • BI skills in IT • BI strategy defined • BI governance • Strategy driven
• BI skills basics • Data unreliable • BI driven strategy • BI/BA skills in IT • BI/BA resources
• BI isolated • Dedicated BI team • BI architecture • Standardized BI tools
• DW established • Owners of BI • Full level of BI tools • Information-driven

• No governance • Limited DM skills • DM governance • Full data governance • High quality
• Little data quality • Developing DM • EIM architecture • DM team integrated data/MDM
Data

• No MDM processes • Data quality/MDM • MDM implemented • Skilled DM resources
• No IT BI/DM skills • Recognize need • Dedicated DM team • Mature DM tools • IBI/DM governance
• Standard DM


Deliver hindsight, insight, and foresight

Shareholder value is improved when value levers are effectively managed
with indicators, that provide lagging, leading, and predictive information,
based on reliable internal and external structured and unstructured data.
Shareholder Value

Revenue Growth Risk Management Asset Efficiency Operating Margin
Dimensions
Value

Mitigate
Increase Strengthen Corporate/ Improve Improve Improve
Grow Investor Improve Improve
Production Pricing and Planning and Working Supply Chain
Regulatory Trust Asset Use F&D Costs
Growth Royalty Analysis Capital Insight
Risks
Lever
Value

Improve optimization of Reduction in downtime
Field improvements Reduce off-contract
transportation scheduling associated with
and reliability spend and leakage
and drilling plans equipment failures
Indicators

Predictive Indicators

Leading Indicators

Lagging Indicators

Location Data Well Data Material Data Vendor Data
Data

Product Data Asset Data Pricing Data Financial Data

Hierarchies and
Sub-Surface Sensor Data Human Resource Data Land Lease Data
Categorization Data


Deliver hindsight, insight, and foresight (contd.)

Knowing which questions to ask — where the most relevant and valuable insights
are likely to be found — is the foundation of any analytics initiative. It requires
collective intelligence that spans industry, function, and technology.

Shareholder Value
Using your strategy and business processes you determine:
• What drives value?
Revenue Growth Risk Management
Dimensions

Asset Efficiency
• What questions do I need to ask? Operating Margin
• How do we measure up?
Value

Increase
Strengthen Mitigate • What investments drive the highest return?
Improve Improve Improve
Pricing Corporate/
Production
and Regulatory
• What information doImprove to answer these important questions?
Planning and
Grow Investor
Trust
I need
Asset Use
Working Supply Chain
Improve
F&D Costs
Growth Analysis Capital Insight
Royalty Risks
Lever
Value

Improve optimization of Reduction in downtime
Field improvements Reduce off-contract
transportation scheduling associated with
and reliability spend and leakage
and drilling plans Critical information model must be organized,
aggregated,
equipment failures

standardized, and accessible to answer critical business
Indicators

Predictive Indicators
questions, provide foresight, insight, and measure success
Leading Indicators

Lagging Indicators
Identify authoritative information source for priority data needs
Location Data Well Data Material Data Vendor Data
Apply business process analytical and asset intelligence to
Data

Product Data
monitor decisions, strategies, and transition optimization
Asset Data Pricing Data Financial Data
and risk
Hierarchies and
Sub-Surface Sensor Data Human Resource Data Land Lease Data
Categorization Data


Signal strength

Signal strength in applied analytics calls for moving beyond traditional
structured data and standard tools to advance sensors, exploratory data
analysis, and knowledge discovery.

Wisdom of crowds

Equipment E-mail
Facilities Videos
Energy
Signal
analysis
????
Action

Excel Economic
Operational Geological
Systems Climate

Social Web analytics

Accelerating insights through automation

Embracing applied analytics requires automation across all dimensions of your
analytics operations. In short, automate delivery to process and people and need
to automate responses — so that action is taken with certainty and at the
lowest cost.

• Conducted analysis on historical transactional data
Transactional to identify improvement opportunities
pricing assessment • Developed a business case detailing opportunities
(TPA) and implementation plans

• Revised the price optimization models to allow for
more accurate and timely pricing
Price • Incorporated data from the TPA to allow for more
optimization granular price setting

• Revised the retail store performance criteria to
focus on individual store profitability
Corporate pricing • Renegotiated contracts with large wholesalers and
strategy dealers where margins were poor


Accelerating insights through automation (cont.)

• Implemented an enterprise-wide pricing software
August Performance Project Performance
Route Baseline Target Actual,
Actual Chart Volume Clients Chart Volume Clients
to-date

201 13% 12% 12% 0% -3% 12% -1% -4%

202 11% 14% 10% -1% -4% 12% -1% -5%

Pricing technology tool to support the new processes 203

204
30%

17%
16%

10%
29%

12%
-4%

-1%
-2%

-3%
28%

12%
-5%

-1%
-2%

-5%

and data • Aligned and integrated with the concurrent global 205

206
13%

27%
12%

14%
12%

26%
+1%

0%
-1%

-2%
14%

27%
0%

0%
-3%

-2%

5% 10% 15% 20% 25% 30% 5% 10% 15% 20% 25% 30%

management SAP implementation Total

Legend:
12% 21% -2% -3% 22% -3% -4%

Target Actual Baseline

Payout Tiers: Supervisor Example

• Developed compensation plans for the sales 15%

Bonus as a % of Compensation
11% 10%

Organizational organization that are aligned with profitability 7%

alignment and • Revised commission structure to be based on mix 3%

compensation of volume and margin -1%
0% 50% 100% 150% 200% 250%
CC. Attainment
Payout Curve Actual Payment

Example: Field Team 1

• Utilized a ―high touch‖ implementation to train the
… Wk1 Wk2 Wk3 Wk4 Wk5 Wk6 Wk7 Wk8 Wk9 Wk10 Wk11 Wk12 Wk13 Wk14 Wk15 Wk16 Wk17 Wk18 Wk19 Wk20

DC 1

DC 2

DC 3

field sales team in pricing and profitability DC 4

DC 5

Corporate HQ

Field execution • Developed the appropriate cadence of review Socialization at Corporate Level

Regional kick-off and prep work

Kick-off and prep work
Work at Corporate HQ

Follow-up Work

Pilot closure

activities to allow for measurable progress
Initial Workshop Virtual Follow-up


User engagement and visualization

Well-designed data visualizations and interfaces let the business user act on the
results, or drill down for more information. Involve the user in the design: They will
tell you when the amount of information they’re getting in each display is too
much, too little, or just right.
3500
Clustering and anomaly detection
3000 identifies health care providers that
consistently claim higher charges
Number of Patients

2500
Suspect Provider
2000

1500

1000 Specialty Providers

500

0
$- $10,000.00 $20,000.00 $30,000.00 $40,000.00 $50,000.00 $60,000.00
Cost/Claim
Each point represents one in-patient provider’s claims in a fiscal year


Building a fact-based culture

Consider using an approach, such as Deloitte’s As One methodology for
assessing your analytics environment’s ability to drive execution
throughout the organization. The methodology helps evaluate support for
an organization’s analytics priorities and develop targeted interventions.
Leaders who Proactive Ask tough Educate and Stand by Reward Measure what
serve obsolescence questions prepare your facts adoption matters
Make sure you Drop tools that Insist on Get out ahead One version Use carrots, Get good at
walk the talk don’t work evidence of change of truth not just sticks crunchy
A CFO who The smart way When reviewing No matter what One adoption Once you have Expand your
says, ―These to make sure decisions made your approach barrier is a fear a stable model data model for
are the inputs I outdated ideas by others, ask to change that analytics in place, begin reporting KPIs
use to make are eliminated to see analytics management, aren’t reliable to link desired to measure how
decisions‖ will is to forbid their that went into training related facts. Develop a behaviors to many decisions
go a long way continued use. the decision- to expectations, system of incentives and your people are
to ensure making resources, and certification to compensation making, as well
adoption of process. benefits will be define selected and their
those inputs by needed. Plan reports and effectiveness.
others. Ask about fact- for them findings as
based inputs ―official.‖
as well as
assumptions.

As used in this document, ―Deloitte‖ means Deloitte Consulting LLP. Please see www.deloitte.com/us/about for a detailed
description of the legal structure of Deloitte LLP and its subsidiaries.


Right-fit analytics

The proliferation of tools has already outrun the pool of available talent to use the
tools powerfully in today’s business. That leads to two big challenges: setting the
overall analytics strategy and then getting the work done. An experienced and
disciplined team of partners is essential to navigate the complexity.

Business Challenges Constraints Solutions

Efficiency Budget
CRISP-DM
Staffing Linux
S/CMM
SAS SQL Server
Infrastructure Clustering
Cost
Experian SPSS UNIX
Licensing SVM Windows
Rapid Solaris
CART
Risk Tolerance Miner Oracle
Risk Regression
Urgency

Security

End Users

Keys to successful analytic initiatives

Develop a road map linked to questions, KPIs, value and
Value-driven resolves related issues, and challenges and opportunities
rationale associated with achieving those objectives

Integrate the initiatives and projects required to address the
Integrated
data- related issues, challenges, and opportunities into a
structure and
measurable value
common structure for managing progress and measuring
value across the enterprise
A single shared end-state architecture and data governance
Coherent framework underpins delivery of improved access to high-
enterprise-wide
quality information across the enterprise
end-state

The road map should be designed to evolve and will require
Living and regular updates and maintenance throughout the multiyear
evolving program delivery program


Summary: Starting the sustainable analytic journey

• Analytics is a cycle of improving business
process performance, not a project
• Focus on high-impact areas, meaningful
measures, available signals
• Move beyond the hindsight of traditional
business intelligence with advanced
analytics that enable deep insight and
distant foresight
• Encourage a fact-based culture that values
the opportunities evident in information over
conjecture or speculation


Contact information

Jane Griffin
Principal
Deloitte Consulting LLP
janegriffin@deloitte.com


"This presentation contains general information only and is based on the experiences and research of Deloitte practitioners. Deloitte is not, by means of
this presentation, rendering business, financial, investment, or other professional advice or services. This presentation is not a substitute for such
professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision
or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte, its affiliates, and related entities shall
not be responsible for any loss sustained by any person who relies on this presentation.

About Deloitte
Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee, and its network of member firms, each
of which is a legally separate and independent entity. Please see www.deloitte.com/about for a detailed description of the legal structure of Deloitte
Touche Tohmatsu Limited and its member firms. Please see www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP
and its subsidiaries.

"This presentation contains general information only and is based on the experiences and research of Deloitte practitioners. Deloitte is not, by means of
this presentation, rendering business, financial, investment, or other professional advice or services. This presentation is not a substitute for such
professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision
or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte, its affiliates, and related entities shall
not be responsible for any loss sustained by any person who relies on this presentation.

Copyright © 2010 Deloitte Development LLC. All rights reserved.
Member of Deloitte Touche Tohmatsu Limited

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