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Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
Competing on analytics by Thomas H. Davenport & Jeanne G. Harris
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Competing on analytics by Thomas H. Davenport & Jeanne G. Harris

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  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Copyright © 2007, SAS Institute Inc. All rights reserved.
  • Transcript

    • 1. Competing on AnalyticsThe New Science of Winning Tom Davenport University of Houston ISRC November 15, 2007
    • 2. The Planets Are Aligned for Analytics  Powerful IT  Data critical mass  Skills sufficiency  Business need2 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 3. What Are Analytics? Analytics Decision Optimization What’s the best that can happen? Predictive Analytics Competitive Advantage What will happen next? Forecasting What if these trends continue? Statistical models Why is this happening? Alerts What actions are needed? Query/drill down Where exactly is the problem? Ad hoc reports How many, how often, where? Standard reports What happened? Reporting Degree of Intelligence3 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 4. What Should Organizations Do with Analytics?  Using analytics is good  Finding the best customers, and charging them the right price  Minimizing inventory in supply chains  Allocating costs accurately and understanding how financial performance is driven  Competing on analytics is better  Making analytics and fact-based decisions a key element of strategy and competition4 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 5. What Is Analytical Competition About? Dispassionate analysis Passionate advocacy Data and statistics Intuition Computers People Discipline and rigor Creativity and insight5 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 6. Analytical Competitors Old Hands Polishing Their Edge  Marriott — Revenue management  Wal-Mart — Supply chain analytics  RBC — Cost and customer profitability  P&G — Supply chain  Progressive — Pricing risk6 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 7. Analytical Competitors Major Turnaround in Strategy or Culture  Harrah’s — Loyalty and service  Tesco — Loyalty and Internet groceries  MCI — Network pricing  Rogers / Nextel / Verizon Wireless / Cablecom — Customer relationship processes  A’s / Red Sox / Patriots / Rockets — Players for price7 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 8. Analytical Competitors Number-Crunchers from Birth  Capital One — “Information-based strategy”  Amazon — Supply chain, advertising, page changes  Yahoo — Pages as controlled experiments  Netflix — Movie preference algorithms8 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 9. Analytical Competitors Cut Across Industries Consumer Products Industrial Products • Kraft • Deere • Mars • Cemex • E&J Gallo Retail Financial Services • J.C. Penney • Bank of America • Best Buy • Barclay’s Transport / Travel and • Humana Entertainment • FedEx Government • Schneider • New York Police Dept. • Hilton • VA Hospitals • Army Recruiting9 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 10. Analytics in Professional Sports  Identify undervalued attributes  Develop new performance metrics  Know when a player is ready to move up  Use your own selection criteria  Assess the ability to work as part of a team  Understand risk better than your competitors  Determine who gets hurt and who gets tired  Who inspires others to play better?  Who drags down the team?10 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 11. The Analytical Delta CE AN P IE RM CE O S RF PE PROGRESS11 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 12. The Analytical Performance Delta E STAGE 5: Analytical Competitors NC MA 11/32 firms OR STAGE 4: Analytical Companies RF More analytical =PE 6/32 higher performance STAGE 3: Analytical Aspirations 7/32 STAGE 2: Localized Analytics 6/32 STAGE 1: Analytically Impaired 2/3212 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 13. The Analytical Performance Delta (cont.) 15% of top performers versus 3% of low performers indicated that analytical capabilities are a key element of 47% 2002 their strategy. 2006 37% 33% 27% 19% 12% 10% 8% 9% 0% No analytical Minimal analytical Some analytical Above average Analytic capability capability capability capability analytical capability is a key element of strategySource: Accenture Survey of 205/392 companies13 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 14. High Performers Use Analytics Top performers have a greater analytical orientation than low performers. High Low Performers Performers 65 % have significant decision-support/analytical capabilities 23% 36 value analytical insights to a very large extent 8 77 have above average analytical capability within industry 33 77 have BI/Data Warehouse modules installed 62 73 make decisions based on data and analysis 51 40 use analytics across their entire organization 2314 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 15. How Analytical Competitors Make Money  Optimize a distinctive capability or external relationship  Customer relationships, supply chain, HR, R&D, etc.  Harrah’s, Marriott, Amazon, etc.  Understand and take action on the business better  MCI, Sara Lee Bakeries, RBC  Offer analytics to customers as the core offering  Apex Management Group in insurance risk management  Franklin Portfolio Associates in equity portfolio development  Offer analytics to customers to augment existing product or service  SmartSwing in golf clubs  Nielsen/IRI in retail/consumer products15 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 16. The Analytical Landscape Is Always Changing  Airlines—letting a business model become obsolete  Baseball teams—on-base percentage becomes over-valued  Capital One—other banks catch up, and they enter a new business16 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 17. The Analytical DELTA — Pieces PIE C ES Data . . . . . . . . breadth, integration, quality Enterprise . . . . . . . .approach to managing analytics Leadership . . . . . . . . . . . . passion and commitment Targets . . . . . . . . . . . first deep, then broad Analysts . . . . . professionals and amateurs17 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 18. Data  The prerequisite for everything analytical  Clean, common, integrated  Accessible in a warehouse  Measuring something new and important18 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 19. New Metrics / Data Wine Chemistry Driving Data Run Production19 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 20. Enterprise  If you’re competing on analytics, it doesn’t make sense to manage them locally  No fiefdoms of data  Avoiding the analytical equivalent of duct tape  Some level of centralized expertise for hard-core analytics  Firms may also need to upgrade hardware and infrastructure20 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 21. Enterprise-Wide Customer View Types of Processes in Which Data Used Data Sales Marketing Logistics Service Internal Transaction Web Metrics External Geo-Demo External Attitudinal21 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 22. Leadership  Gary Loveman at Harrah’s  “Do we think, or do we know?”  “Three ways to get fired”  Barry Beracha at Sara Lee“Our CEO is a real  “In God we trust, all others bring data”data dog” Sara Lee  Jeff Bezos at Amazon executive  “We never throw away data” 22 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 23. The Great Divide Full steam ahead! • Hire the people Is your senior • Build the systems management • Create the processes team committed? Prove the value! • Run a pilot • Measure the benefit • Try to spread it23 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 24. Targets With limited analytical resources, pick a major strategic target, with a minor or two Harrah’s = Loyalty + Service Patriots = Player selection + TFE Barclay’s = Asset analysis + Credit cards UPS = Operations + Customer data Can also have two primary user group targets Wal-Mart = Category managers + Suppliers Owens & Minor = Logistics + Hospitals Progressive = Actuaries + Customers24 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 25. Analysts Analytical Professionals 5-10% — Can create algorithms Analytical Semi-Professionals 15-20% — Can use visual tools, create simple models Analytical Amateurs — Can use spreadsheets 70-80%25 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 26. Taking Action  Analytics need to be embedded into the machinery of organizational action  Operational decision-making  Business processes  Manager and employee behavior  Customer expectations26 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 27. The Analytical DELTA — Progress Moving to: Stage 3 Stage 5 Stage 1 Stage 2 Analytical Stage 4 Analytical Success Factor Analytically Impaired Localized Analytics Aspirations Analytical Companies Competitors Data Inconsistent, poor quality, poorly Data useable, but in functional or process Organization beginning to create Integrated, accurate, common data in central Relentless search for new data and metrics organized silos centralized data warehouse repository Enterprise n/a Islands of data, technology, and Early stages of an enterprise-wide Key data, technology and analysts are All key analytical resources centrally expertise approach central-ized or managed PROGRESS networked Leadership No awareness or interest Only at the function or process level Leaders beginning to recognize importance Leadership support for analytical competence Strong leadership passion for analytical of analytics competition Targets n/a Multiple disconnected targets that may not be Analytical efforts coalescing behind a Analytical activity centered on a few key Analytics support the firm’s distinctive strategically important small set of targets domains capability and strategy Analysts Few skills, and these attached to specific Isolated pockets of analysts with no Influx of analysts in key target areas Highly capable analysts in central or networked World-class professional analysts functions communication organization and attention to analytical amateurs27 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 28. Next Steps for Analytics  Continual pursuit of new data types  Real-time action  Content mining, intangibles analytics  Engineering multi-modal decision-making  Model management / analytical resource management / knowledge management28 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics
    • 29. It Doesn’t Happen Overnight — Start Now!  Takes a while to put data and infrastructure foundation in place, and even longer to develop human capabilities, a fact-based culture, and “success stories”  Barclay’s five-year plan for “Information-Based Customer Management”  UPS — “We’ve been collecting data for six or seven years, but it’s only become usable in the last two or three, with enough time and experience to validate conclusions based on data.”29 | 2007 © All Rights Reserved. Thomas H. Davenport – Competing on Analytics

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