analytics at work - how to make better decisions to get better results
TRANSCRIPT
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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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?
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• 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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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?
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►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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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
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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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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
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DataData
The prerequisite for everything analytical
Clean, common, integrated
Accessible in a warehouse
Measuring something new and important
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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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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
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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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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
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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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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
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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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Thomas H. Davenport – Analytics at Work
The Context: Analytical ProcessesThe Context: Analytical Processes
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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
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Processes Structures
Capabilities Transactions
Decisions!
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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
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Thomas H. Davenport – Analytics at Work
Most Common Decision InterventionsMost Common Decision Interventions
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Thomas H. Davenport – Analytics at Work
Closing the Loop: Decision ReviewClosing the Loop: Decision Review
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►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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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
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Thomas H. Davenport – Analytics at Work
Questions & Answers
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