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Analytics at Work
How to Make Smarter Decisions and
        Get Better Results




 Tom Davenport
 Babson College

 PBLS Hong Kong
 13 July 2010
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
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
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
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
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
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
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
New Metrics / Data




       Wine Chemistry             Optimized revenue         Smile Frequency



9 | 2010 © All Rights Reserved.                   Thomas H. Davenport – Analytics at Work
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
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
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
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
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
Better Decisions Are the Goal of Analytics



            Reports                                          Scorecards
                                   Decisions!
                                   D i i !




               Portals                                       Drill-down



15 | 2010 © All Rights Reserved.                Thomas H. Davenport – Analytics at Work
Systematically Making Decisions Better



                       Identify                  Inventory


                                    Better
                                   Decisions


                     Intervene                 Institutionalize


16 | 2010 © All Rights Reserved.                Thomas H. Davenport – Analytics at Work
Most Common Decision Interventions
                                             0,9


                                             0,8


                                             0,7


                                             0,6
                       Frequenc Mentioning




                                             0,5
                              cy




                                             0,4


                                             0,3


                                             0,2


                                             0,1


                                              0




                                                   Type of Intervention




17 | 2010 © All Rights Reserved.                                          Thomas H. Davenport – Analytics at Work
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
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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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
  • 9. New Metrics / Data Wine Chemistry Optimized revenue Smile Frequency 9 | 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
  • 15. Better Decisions Are the Goal of Analytics Reports Scorecards Decisions! D i i ! Portals Drill-down 15 | 2010 © All Rights Reserved. Thomas H. Davenport – Analytics at Work
  • 16. Systematically Making Decisions Better Identify Inventory Better Decisions Intervene Institutionalize 16 | 2010 © All Rights Reserved. Thomas H. Davenport – Analytics at Work
  • 17. Most Common Decision Interventions 0,9 0,8 0,7 0,6 Frequenc Mentioning 0,5 cy 0,4 0,3 0,2 0,1 0 Type of Intervention 17 | 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