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Presentation of "Analytics at Work"TRANSCRIPT
Analytics at Work Smarter Decisions, Better Results
Tom Davenport
Babson College
SAS Institute Chile
21 October 2010
Thomas H. Davenport – Analytics at Work
The Worst of Times for Decisions?
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►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.
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.
Thomas H. Davenport – Analytics at Work
Analytical Culture
And Business
Processes
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Analytics at Work—The Big Picture
Data
Enterprise
Leadership
Targets
Analysts .
Better
Decisions!
Systematic Review
Analytical Capability Organizational Context Desired Result
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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”)
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
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
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
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
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Data
The prerequisite for everything analytical
Clean, common, integrated
Accessible in a warehouse
Measuring something new and important
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New Metrics / Data
Wine Chemistry Smile Frequency Optimized revenue
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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
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
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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
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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
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
Thomas H. Davenport – Analytics at Work
The 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
Thomas H. Davenport – Analytics at Work
The Context: Analytical Processes
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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?”
Thomas H. Davenport – Analytics at Work
Better Decisions Are the Goal of Analytics
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Reports Scorecards
Portals Drill-down
Decisions!
Thomas H. Davenport – Analytics at Work
Systematically Making Decisions Better
Identify Inventory
Intervene Institutionalize
Better Decisions
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Thomas H. Davenport – Analytics at Work
Most Common Decision Interventions
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Type of Intervention
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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
Thomas H. Davenport – Analytics at Work
Keep in Mind
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►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!