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Analytics at Work Smarter Decisions, Better Results Tom Davenport Babson College SAS Institute Chile 21 October 2010

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Page 1: SAS Davenport

Analytics at Work Smarter Decisions, Better Results

Tom Davenport

Babson College

SAS Institute Chile

21 October 2010

Page 2: SAS Davenport

Thomas H. Davenport – Analytics at Work

The Worst of Times for Decisions?

2 | 2010 © All Rights Reserved.

►Decision processes and outcomes are often bad! ► The body of knowledge on what works is often ignored

► Decisions take too long, get revisited, involve too many or few

► Many bad outcomes in the public and private sectors

►Little measurement/progress/accountability

►Weak ties between data/information/knowledge inputs and decisions

► If we’re not getting better at decision-making, much information work is called into question ► Data warehousing, analytics, reports, ERP, knowledge

management, etc.

Page 3: SAS Davenport

Thomas H. Davenport – Analytics at Work 3 | 2010 © All Rights Reserved.

The Best of Times for Decisions?

►Analytics and algorithms

► Intuition and the subconscious

► ―The wisdom of crowds‖

►Behavioral economics and ―nudges‖

►Neurobiology

►Decision automation

►…Etc.

Page 4: SAS Davenport

Thomas H. Davenport – Analytics at Work

Analytical Culture

And Business

Processes

4 | 2010 © All Rights Reserved.

Analytics at Work—The Big Picture

Data

Enterprise

Leadership

Targets

Analysts .

Better

Decisions!

Systematic Review

Analytical Capability Organizational Context Desired Result

Page 5: SAS Davenport

Thomas H. Davenport – Analytics at Work 5 5

What Are Analytics?

Optimization

Predictive Modeling/ Forecasting

Randomized Testing

Statistical analysis

Alerts

Query/drill down

Ad hoc reports

Standard Reports

“What’s the best that can happen?”

“What will happen next?”

“What happens if we try this?”

“Why is this happening?”

“What actions are needed?”

“What exactly is the problem?”

“How many, how often, where?”

“What happened?”

Descriptive

Analytics

(the “what”)

Degree

of Intelligence

Predictive and

Prescriptive

Analytics

(the “so what”)

Page 6: SAS Davenport

Thomas H. Davenport – Analytics at Work 6

Stage 5

Analytical

Competitors

Stage 4

Analytical Companies

Stage 3

Analytical Aspirations

Stage 2

Localized Analytics

Stage 1

Analytically Impaired

Levels of Analytical Capability

Page 7: SAS Davenport

Thomas H. Davenport – Analytics at Work 7

Analytical Competitors

Old Hands, Turnarounds, Born Analytical

Marriott — Revenue management

UPS — Operations and logistics, then customer

Progressive— risk, pricing

• Harrah’s — Loyalty and service

• Tesco — Loyalty and internet groceries

• MCI/Worldcom— Cost identification and reduction

• Capital One— “information-based strategy”

• Google — page rank, advertising, HR

• Netflix— customer preference algorithms

Page 8: SAS Davenport

Thomas H. Davenport – Analytics at Work 8 8

Analytical Competitors or Companies

Across Industries

Hospitality/ Entertainment • Harrah’s Entertainment

• Marriott International

• New England Patriots

• Boston Red Sox

• AC Milan

Financial Services • BancoItaú

• Banco de Chile

• Banco Santander

• Capital One

• CMR Falabella

Pharmaceuticals • Astra Zeneca

• Merck

• Vertex

Industrial Products • CEMEX

• John Deere & Company

Retail • Falabella

• La Polar

• Tesco

• Wal-Mart Transport • TurBus

• FedEx

• United Parcel Service

Telecommunications • EntelPCS

• Movistar/Telefónica

• Rogers Telecom

Consumer Products • E&J Gallo

• Mars

• Procter & Gamble

eCommerce • Amazon

• Ebay

• Expedia

Page 9: SAS Davenport

Thomas H. Davenport – Analytics at Work 9 | 2010 © All Rights Reserved.

The Analytical DELTA

Data . . . . . . . . breadth, integration, quality

Enterprise . . . . . . . .approach to managing analytics

Leadership . . . . . . . . . . . . passion and commitment

Targets . . . . . . . . . . . first deep, then broad

Analysts . . . . . professionals and amateurs

DELTA = change

Page 10: SAS Davenport

Thomas H. Davenport – Analytics at Work 10 | 2010 © All Rights Reserved.

Data

The prerequisite for everything analytical

Clean, common, integrated

Accessible in a warehouse

Measuring something new and important

Page 11: SAS Davenport

Thomas H. Davenport – Analytics at Work 11 | 2010 © All Rights Reserved.

New Metrics / Data

Wine Chemistry Smile Frequency Optimized revenue

Page 12: SAS Davenport

Thomas H. Davenport – Analytics at Work 12 | 2010 © All Rights Reserved.

Enterprise

If you’re competing on analytics, it doesn’t make

sense to manage them locally

No fiefdoms of data

Avoiding “spreadmarts”—analytical duct tape

Some level of centralized expertise for hard-core

analytics

Firms may also need to upgrade hardware and

infrastructure

Page 13: SAS Davenport

Thomas H. Davenport – Analytics at Work 13 | 2010 © All Rights Reserved.

Leadership

Gary Loveman at Harrah’s

“Do we think, or do we know?”

“Three ways to get fired”

Barry Beracha at Sara Lee

“In God we trust, all others bring data”

Jeff Bezos at Amazon

“We never throw away data”

“Our CEO is a real

data dog”

Sara Lee

executive

Page 14: SAS Davenport

Thomas H. Davenport – Analytics at Work 14 | 2010 © All Rights Reserved.

The Great Divide

Is your senior management team committed?

Full steam ahead!

• Hire the people

• Build the systems

• Create the processes

Prove the value!

• Run a pilot

• Measure the benefit

• Try to spread it

Page 15: SAS Davenport

Thomas H. Davenport – Analytics at Work 15 | 2010 © All Rights Reserved.

Targets

Pick a major strategic target, with a minor or two

TD Bank= Customer service and its impact

Harrah’s = Loyalty + Service

Google = Page rank/advertising + HR

Can also have two primary user group targets

Wal-Mart = Category managers + Suppliers

Owens & Minor = Supply chain managers + hospitals

Page 16: SAS Davenport

Thomas H. Davenport – Analytics at Work 16 | 2010 © All Rights Reserved.

Analysts

5-10%

Analytical Professionals—Own/Rent Can create new algorithms

Analytical Semi-Professionals—Own/Rent Can use visual and basic statistical tools, create simple models

Analytical Amateurs--Own Can use spreadsheets, use analytical transactions

15-20%

70-80%

* percentages will vary based upon industry and strategy

1%

Analytical Champions--Own Lead analytical initiatives

Page 17: SAS Davenport

Thomas H. Davenport – Analytics at Work

The Context: Analytical Culture

17 |

• Facts, evidence, analysis as the primary way of deciding

• Pervasive “test and learn” emphasis where there aren’t facts

• Free pass for pushbacks—”Where’s your data?”

• Still room for intuition based on experience

• A focus on action after analysis

• Never resting on your analytical laurels

Page 18: SAS Davenport

Thomas H. Davenport – Analytics at Work

The Context: Analytical Processes

18

Source: SAP AG 2006

Receives ASN

Releases ASN

Delivery Execution

Update Inventory Accounting

Update Inventory

Delivery Performance

“How effective is our fulfillment process?”

CLTV

“Does this order justify extra efforts?”

Inventory Forecast

“Will this be back in inventory?”

Defection Risk

“What is the customer status?”

Global ATP Check

Request Global ATP

Creation Sales Order

Fulfillment Request

Creation Purchase Order

Creation & Release Delivery

Request Returns per Customer

“What is the customer history?”

Page 19: SAS Davenport

Thomas H. Davenport – Analytics at Work

Better Decisions Are the Goal of Analytics

19 | 2010 © All Rights Reserved.

Reports Scorecards

Portals Drill-down

Decisions!

Page 20: SAS Davenport

Thomas H. Davenport – Analytics at Work

Systematically Making Decisions Better

Identify Inventory

Intervene Institutionalize

Better Decisions

20 | 2010 © All Rights Reserved.

Page 21: SAS Davenport

Thomas H. Davenport – Analytics at Work

Most Common Decision Interventions

21 | 2010 © All Rights Reserved.

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Type of Intervention

Page 22: SAS Davenport

Thomas H. Davenport – Analytics at Work 22

Multiple Interventions:

Better Pricing Decisions at Stanley

Pricing identified as one of four key decision domains by CIO

Pricing Center of Excellence established in 2003

Adopted several difference pricing methodologies

Implemented new pricing optimization software

Regular “Gross Margin Calls” for senior managers

Offshore capability gathers competitive pricing data

Some automated pricing systems, e.g., for promotions

Center spreads innovations across Stanley

Result: gross margin from 34% to over 40% in six years

Page 23: SAS Davenport

Thomas H. Davenport – Analytics at Work

Keep in Mind

23 | 2010 © All Rights Reserved.

►Five levels, five factors for building analytical capability

►Data and leadership are the most important prerequisites

►Make sure your targets are strategic

►Tie all your BI and analytics work to decisions

►This is not business as usual—there is a historic opportunity to transform your industry!