analytics at work - how to make better decisions to get better results

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16296 Presents: Analytics at Work: How To Make Better Decisions and Get Better Results Tuesday, February 2, 2010 12:00 PM – 1:00 PM Eastern 11:00 AM – 12:00 PM Central 10:00 AM – 11:00 AM Mountain 9:00 AM – 10:00 AM Pacific Featuring: Tom Davenport Program hosted by: Angelia Herrin

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Page 1: Analytics at Work - How to Make Better Decisions to Get Better Results

16296

Presents:

Analytics at Work: How To Make Better Decisions

and Get Better Results

Tuesday, February 2, 2010 12:00 PM – 1:00 PM Eastern 11:00 AM – 12:00 PM Central 10:00 AM – 11:00 AM Mountain 9:00 AM – 10:00 AM Pacific

Featuring:

Tom Davenport

Program hosted by:

Angelia Herrin

Page 2: Analytics at Work - How to Make Better Decisions to Get Better Results

Sign-In Roster Instructions for Site Coordinator

You are welcome to fill out an electronic site roster with the names and email addresses of everyone at your location.

Please visit the URL below to enter the names and email

addresses:

attend.krm.com/16296

We will be accepting names for 3 days after the audio seminar.

Thank you for your participation.

Page 3: Analytics at Work - How to Make Better Decisions to Get Better Results

Copyright © 2010, SAS Institute Inc. All rights reserved.

Analytics at WorkAnalytics at WorkSmarter Decisions, Better ResultsSmarter Decisions, Better Results

Tom DavenportTom DavenportBabson CollegeBabson College

Harvard Business PublishingHarvard Business PublishingFebruary 2, 2010February 2, 2010

Thomas H. Davenport – Analytics at Work

From Where Do These Ideas Come?From Where Do These Ideas Come?

2 | 2010 © All Rights Reserved.

• Competing on Analytics: The New Science of Winning• Based on a Harvard Business Review article

in 2006, and an initial study of 32 companies

• Strong focus on companies that had made analytics a key competitive advantage

• Led to study of many more companies, several surveys, and several industry-specific analyses

• Analytics at Work: Smarter Decisions, Better Results• Addresses how any company can become

more analytical and fact-based

• Orientation to the linkage between analytics and decisions

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Page 4: Analytics at Work - How to Make Better Decisions to Get Better Results

Copyright © 2010, SAS Institute Inc. All rights reserved.

Thomas H. Davenport – Analytics at Work

WhatWhat’’s Wrong with Decisions Today?s Wrong with Decisions Today?

3 | 2010 © All Rights Reserved.

►Decision outcomes are often bad!

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

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

► Over-reliance on intuition, under-reliance on data and analytics

►Little measurement/progress/accountability

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

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

management, etc.

Thomas H. Davenport – Analytics at Work4 | 2010 © All Rights Reserved.

The Decisions Dishonor Roll

►Private sector► The decision to go deep and leveraged into real

estate loans at Lehman Brothers

► The decision to offer widespread subprime mortgage loans with little documentation at Countrywide, Golden West, etc.

► The decision to expand in farm equipment at Tenneco

► The decision not to sell Yahoo to Microsoft

►Public sector► The decision to invade Iraq

► The decision to stay in Vietnam and escalate the war

► The decision to invade Cuba at the Bay of Pigs

► The decisions to launch Challenger, and not to rescue Columbia

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Page 5: Analytics at Work - How to Make Better Decisions to Get Better Results

Copyright © 2010, SAS Institute Inc. All rights reserved.

Thomas H. Davenport – Analytics at Work5 | 2010 © All Rights Reserved.

The Upside—New Decision Frontiers

►Analytics and algorithms

► Intuition and the subconscious

► “The wisdom of crowds”

►Behavioral economics and “nudges”

►Neurobiology

►Decision automation

►…Etc.

Thomas H. Davenport – Analytics at Work

Analytical CultureAnd Business

Processes

6 | 2010 © All Rights Reserved.

Analytics at WorkAnalytics at Work——The Big PictureThe Big Picture

Data

Enterprise

Leadership

Targets

Analysts .

BetterDecisions!

Systematic Review

Analytical Capability Organizational Context Desired Result

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Page 6: Analytics at Work - How to Make Better Decisions to Get Better Results

Copyright © 2010, SAS Institute Inc. All rights reserved.

Thomas H. Davenport – Analytics at Work7

Stage 5Analytical

Competitors

Stage 4Analytical Companies

Stage 3Analytical Aspirations

Stage 2Localized Analytics

Stage 1Analytically Impaired

Levels of Analytical Capability

Thomas H. Davenport – Analytics at Work8 | 2010 © All Rights Reserved.

DataData

The prerequisite for everything analytical

Clean, common, integrated

Accessible in a warehouse

Measuring something new and important

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Page 7: Analytics at Work - How to Make Better Decisions to Get Better Results

Copyright © 2010, SAS Institute Inc. All rights reserved.

Thomas H. Davenport – Analytics at Work9 | 2010 © All Rights Reserved.

New Metrics / DataNew Metrics / Data

Wine Chemistry Smile FrequencyOptimized revenue

Thomas H. Davenport – Analytics at Work

Data Through the StagesData Through the Stages

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Stage 4 Stage 5Stage 3 Stage 4Stage 2 Stage 3Stage 1 Stage 2

Analytically Impaired to Localized Analytics

•Gain mastery over local data of importance,

including building functional data marts.

Localized Analytics to Analytical Aspirations

•Build enterprise consensus around some analytical

targets and their data needs.

•Build some domain data warehouses (e.g., customer) and corresponding analytical

expertise.

•Motivate and reward cross-functional data contributions

and management.

Analytical Aspirations to Analytical Companies

•Build enterprise data warehouses and integrate

external data.

•Engage senior executives in EDW plans and

management.

•Monitor emerging data sources.

Analytical Companies to Analytical Competitors

•Educate and engage senior executives in competitive

potential of analytical data.

•Exploit unique data.

•Establish strong data governance, especially

stewardship.

•Form a BICC if you don’t have one yet.

Stage 1Analytically Impaired

Stage 2Localized Analytics

Stage 3Analytical Aspirations

Stage 4Analytical

Companies

Stage 5Analytical

Competitors

Stage 1Analytically Impaired

Stage 2Localized Analytics

Stage 3Analytical Aspirations

Stage 4Analytical

Companies

Stage 5Analytical

Competitors

Stage 1Analytically Impaired

Stage 2Localized Analytics

Stage 3Analytical Aspirations

Stage 4Analytical

Companies

Stage 5Analytical

Competitors

Stage 1Analytically Impaired

Stage 2Localized Analytics

Stage 3Analytical Aspirations

Stage 4Analytical

Companies

Stage 5Analytical

Competitors

Stage 1Analytically Impaired

Stage 2Localized Analytics

Stage 3Analytical Aspirations

Stage 4Analytical

Companies

Stage 5Analytical

Competitors

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Page 8: Analytics at Work - How to Make Better Decisions to Get Better Results

Copyright © 2010, SAS Institute Inc. All rights reserved.

Thomas H. Davenport – Analytics at Work11 | 2010 © All Rights Reserved.

EnterpriseEnterprise

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

Thomas H. Davenport – Analytics at Work12 | 2010 © All Rights Reserved.

LeadershipLeadership

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

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Page 9: Analytics at Work - How to Make Better Decisions to Get Better Results

Copyright © 2010, SAS Institute Inc. All rights reserved.

Thomas H. Davenport – Analytics at Work13 | 2010 © All Rights Reserved.

TargetsTargets

Pick a major strategic target, with a minor or twoHarrah’s = Loyalty + Service

Patriots= Player selection + TFE

Google = Page rank/advertising + HR

Can also have two primary user group targets

Wal-Mart = Category managers + Suppliers

Owens & Minor = Supply chain managers + hospitals

Thomas H. Davenport – Analytics at Work14 | 2010 © All Rights Reserved.

AnalystsAnalysts

5-10%Analytical ProfessionalsCan create new algorithms

Analytical Semi-ProfessionalsCan use visual and basic statistical tools, create simple models

Analytical AmateursCan use spreadsheets, use analytical transactions

15-20%

70-80%

* percentages will vary based upon industry and strategy

1%

Analytical ChampionsLead analytical initiatives

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Page 10: Analytics at Work - How to Make Better Decisions to Get Better Results

Copyright © 2010, SAS Institute Inc. All rights reserved.

Thomas H. Davenport – Analytics at Work

SuccessFactor

Stage 1Analytically Impaired

Stage 2Localized Analytics

Stage 3Analytical Aspirations

Stage 4Analytical Companies

Stage 5Analytical Competitors

Data Inconsistent, poor quality and organization; difficult to do substantial analysis; no groups with strong data orientation.

Much data useable, but in functional or process silos; senior executives don’t discuss data management.

Identifying key data domains and creating central data repositories.

Integrated, accurate, common data in central warehouse; data still mainly an IT matter; little unique data.

Relentless search for new data and metrics; organization separate from IT oversees information; data viewed as strategic asset.

Enterprise No enterprise perspective on data or analytics.Poorly integrated systems.

Islands of data, technology, and expertise deliver local value.

Process or business unit focus for analytics. Infrastructure for analytics beginning to coalesce.

Key data, technology and analysts are managed from an enterprise perspective.

Key analytical resources focused on enterprise priorities and differentiation.

Leadership Little awareness of or interest in analytics.

Local leaders emerge, but have little connection.

Senior leaders recognizing importance of analytics and developing analytical capabilities.

Senior leaders developing analytical plans and building analytical capabilities.

Strong leaders behaving analytically and showing passion for analytical competition.

Targets No targeting of opportunities.

Multiple disconnected targets, typically not of strategic importance.

Analytical efforts coalescing behind a small set of important targets.

Analytics centered on a few key business domains with explicit and ambitious outcomes.

Analytics integral to the company’s distinctive capability and strategy.

Analysts Few skills, and those attached to specific functions.

Unconnected pockets of analysts; unmanaged mix of skills.

Analysts recognized as key talent and focused on important business areas.

Highly capable analysts explicitly recruited, developed, deployed, and engaged.

World-class professional analysts; cultivation of analytical amateurs across the enterprise.

DELTA Stage Model

Thomas H. Davenport – Analytics at Work

The Context: Analytical CultureThe Context: Analytical Culture

16 | 2010 © All Rights Reserved.

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

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Page 11: Analytics at Work - How to Make Better Decisions to Get Better Results

Copyright © 2010, SAS Institute Inc. All rights reserved.

Thomas H. Davenport – Analytics at Work

The Context: Analytical ProcessesThe Context: Analytical Processes

17 | 2010 © All Rights Reserved.

Source: SAP AG 2006

Receives ASN

Releases ASN

DeliveryExecution

UpdateInventory

Accounting

UpdateInventory

Delivery PerformanceDelivery Performance“How effective is our fulfillment

process?”

CLTVCLTV“Does this order justify extra

efforts?”

Inventory ForecastInventory Forecast“Will this be back in inventory?”

Defection RiskDefection Risk“What is the customer status?”

Global ATPCheck

RequestGlobal ATP

CreationSales Order

Fulfillment Request

CreationPurchase Order

Creation &Release Delivery

RequestReturns per CustomerReturns per Customer“What is the customer history?”

Thomas H. Davenport – Analytics at Work

Better Decisions Are the Goal of AnalyticsBetter Decisions Are the Goal of Analytics

18 | 2010 © All Rights Reserved.

Processes Structures

Capabilities Transactions

Decisions!

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Page 12: Analytics at Work - How to Make Better Decisions to Get Better Results

Copyright © 2010, SAS Institute Inc. All rights reserved.

Thomas H. Davenport – Analytics at Work19 | 2010 © All Rights Reserved.

A Study of DecisionsA Study of Decisions

Decisions!

►57 attempts to improve specific decisions

►90% of companies could name one

►Most decisions were frequent and operational► Pricing (of consumer goods, industrial goods, government

contracts, maintenance contracts, etc.);

► Targeting of consumers for marketing initiatives (by retailers, insurers, credit card firms);

► Merchandising decisions by retailers (what brands to buy in what quantity for what stores, shelf space allocation);

► Location decisions (for bank branches, where to service industrial equipment)

►Results in “Make Better Decisions,” Harvard Business Review, Nov. 2010

Thomas H. Davenport – Analytics at Work

Systematically Making Decisions BetterSystematically Making Decisions Better

20 | 2010 © All Rights Reserved.

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Page 13: Analytics at Work - How to Make Better Decisions to Get Better Results

Copyright © 2010, SAS Institute Inc. All rights reserved.

Thomas H. Davenport – Analytics at Work

Most Common Decision InterventionsMost Common Decision Interventions

21 | 2010 © All Rights Reserved.

Thomas H. Davenport – Analytics at Work

Closing the Loop: Decision ReviewClosing the Loop: Decision Review

22 | 2010 © All Rights Reserved.

►Tom Brady as “a student of error”

►Providence Regional Medical Center in Everett, Washington► “The hospital set up an independent panel to investigate

medical mistakes, disclose its findings to the patient, and voluntarily offer a financial award if warranted. As a result, Providence has only two malpractice suits pending, compared with an average of 12 to 14 at other hospitals of similar size.”(Business Week, Jan. 7, 2010)

►Chevron► Mandatory “lookbacks” at decision quality on all decisions over

$100M (along with pre-decision review”

► “A culture of honesty and self-examination”

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Page 14: Analytics at Work - How to Make Better Decisions to Get Better Results

Copyright © 2010, SAS Institute Inc. All rights reserved.

Thomas H. Davenport – Analytics at Work

The International Institute for AnalyticsThe International Institute for Analytics

The first peer-based research organization in the analytics world

2010 research topics to include:

Analytics in health care (clinical intelligence)

Analytics in human resources

Taking advantage of proprietary data

The new roles of analysts

All new Individual Members who register in February receive a complimentary autographed copy of Analytics at Work

Go to http://iianalytics.com to join

23 | 2010 © All Rights Reserved.

Thomas H. Davenport – Analytics at Work

Questions & Answers

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