The document discusses how organizations can make smarter decisions through analytics. It outlines 5 levels of analytical capability that organizations can achieve, from analytically impaired to analytical competitors. It also identifies 5 key factors (data, enterprise, leadership, targets, analysts) that are important for building strong analytical capabilities. The goal of analytics is to systematically improve decisions through interventions like identifying key decisions, inventorying the current process, implementing changes, and institutionalizing the improvements. Regular measurement and adapting approaches are important for organizations to continue enhancing their decision-making.

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