Thomas Davenport: Analytics at Work: How to Make Better Decisions and Get Better Results

1. Analytics at Work How to Make Smarter Decisions and Get Better Results Tom Davenport Babson College PBLS Hong Kong 13 July 2010

2. The Downside—Problems in Decisions Downside— ► D i i processes and outcomes are often Decision d t ft bad! ► The body of knowledge on what works is often ignored ► Decisions take too long, get revisited, involve too many or few ► Little measurement/progress/accountability p g y ► Weak ties between data/information/knowledge inputs and g p decisions ► If we’re not getting better at decision-making, g g g much of IT’s work is called into question ► Data warehousing, analytics, reports, ERP, knowledge management, etc. 2 | 2010 © All Rights Reserved. Thomas H. Davenport – Analytics at Work

3. The Upside—New Decision Frontiers ► Analytics and algorithms ► Intuition and the subconscious ► “The wisdom of crowds” ► Behavioral economics and “nudges” nudges ► Neurobiology ► Decision automation ► …Etc. 3 | 2010 © All Rights Reserved. Thomas H. Davenport – Analytics at Work

4. Analytics at Work—The Big Picture Work— Analytical Capability Organizational Context Desired Result Data Enterprise p Analytical Culture A l ti l C lt Better Leadership And Business Decisions! Targets T t Processes Analysts . Systematic Review 4 | 2010 © All Rights Reserved. Thomas H. Davenport – Analytics at Work

5. Levels of Analytical Capability Stage 5 Analytical Competitors Stage 4 g Analytical Companies Stage 3 Analytical Aspirations Stage 2 Localized Analytics Stage 1 g Analytically Impaired 5 Thomas H. Davenport – Analytics at Work

6. Analytical Competitors Old Hands, Turnarounds, Born Analytical Marriott — Revenue management UPS — Operations and logistics, then customer HSBC— risk, credit scoring, pricing Harrah s Harrah’s — Loyalty and service Tesco — Loyalty and internet groceries CreditCorp— D bt collection C ditC Debt ll ti Capital One “information based strategy” One— information-based strategy Google — page rank, advertising, HR ISM— analytical services 6 | 2010 © All Rights Reserved. Thomas H. Davenport – Analytics at Work

7. The Analytical DELTA Data . . . . . . . . breadth, integration, quality Enterprise . . . . . . . .approach to managing analytics p pp g g y Leadership . . . . . . . . . . . . passion and commitment Targets . . . . . . . . . . . first deep, then broad T t fi t d th b d Analysts . . . . . professionals and amateurs 7 | 2010 © All Rights Reserved. Thomas H. Davenport – Analytics at Work

8. Data The prerequisite for everything analytical Clean, common, integrated Accessible in a warehouse Measuring something new and important 8 | 2010 © All Rights Reserved. Thomas H. Davenport – Analytics at Work

10. Enterprise If you’re competing on analytics, it doesn’t make sense to manage them locally No fiefdoms of data Avoiding “spreadmarts”—analyticall d t t A idi “ d t ” l ti duct tape Some level of centralized expertise for hard-core analytics l i Firms may also need to upgrade hardware and infrastructure 10 | 2010 © All Rights Reserved. Thomas H. Davenport – Analytics at Work

11. 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 In trust, data” data dog” Sara Lee Jeff Bezos at Amazon executive ti “We never throw away data” 11 | 2010 © All Rights Reserved. Thomas H. Davenport – Analytics at Work

12. The Great Divide Full steam ahead! • Hire the people Is your senior • Build the systems management • C t the processes Create th team committed? Prove the value! • Run a pilot •MMeasure th b fit the benefit • Try to spread it 12 | 2010 © All Rights Reserved. Thomas H. Davenport – Analytics at Work

13. Targets Pick Pi k a major strategic target, with a minor or t j t t i t t ith i 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 13 | 2010 © All Rights Reserved. Thomas H. Davenport – Analytics at Work

14. Analysts Analytical Champions--Own 1% Lead L d analyticall iinitiatives l ti iti ti Analytical Professionals—Own/Rent 5-10% 5 10% Can C create new algorithms t l ith Analytical Semi-Professionals—Own/Rent y 15-20% Can use visual and basic statistical tools, create simple models Analytical Amateurs--Own Can use spreadsheets, use 70-80% 70 80% analytical transactions * percentages will vary based upon industry and strategy 14 | 2010 © All Rights Reserved. Thomas H. Davenport – Analytics at Work

18. Multiple Interventions: Better Pricing Decisions at Stanley Pricing identified as one of four key decision domains Pricing Center of Excellence established in 2003 Adopted several difference pricing methodologies Implemented new p p pricing optimization software g p 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 g g y Thomas H. Davenport – Analytics at Work

19. Keep in Mind ► Five levels, five factors for building analytical capability ► Data and leadership are the most important p p prerequisites q ► Make sure your targets are strategic ► Tie all your BI and analytics work to decisions ► Never rest! 19 | 2010 © All Rights Reserved. Thomas H. Davenport – Analytics at Work

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