Data to Knowledge to Results
Review and Analysis of Paper by
Davenport et al
Team: Something Different
Myron Burr
Kevin McComas
Easwar Srinivasan
Bill Winett
Data vs Information
Data :
Measures, Transactions
Knowledge / Information
Profit maximizing product mix
Parts per hour
Billing rate
Click through rate
Profit maximizing bundling of
solutions
Individualized, targeted web
pages
What are the Issues?
• Background:
– Firms are spending billions on IT applications ( ERP, POS scanners,
web and e-commerce systems, and CRM)
– Generated billions of transaction records
• Observation:
– Very little data is converted to knowledge (less than 10% in studied
firms)
• Problem Statements:
– Lost opportunities for improved results
– Unrealized business value from these investments
Proposed Approach to Resolution
 Davenport et al, researched over 100
companies
 Developed a model for building analytic
capability
 Demonstrated how to realize results from
this capability
Framework
Strategy
• What are our core business processes?
• What key decisions need analytic
insights?
• What information matters?
• Clear strategy leads to good
measurements and
therefore good data
gathering
Context
 Process needs a foundation
 Required ingredients for success
 Grounded in
 Firm’s strategy (and the information
needed to execute this strategy)
 Skills and experience of staff
 Organization and culture
 Data-oriented / Fact-based
 Technology and Data
Skills and Experience
 Key Roles
 DB Administrator: loads, organizes and
checks data
 Business Analyst / Data Modeler
 Decision Maker / Outcome Manager
 Skills: Depth depends on above role





Technology Skills
Statistical Modeling and Analytic Skills
Knowledge of the Data
Knowledge of the Business
Communication and Partnering
Without skilled staff, IT applications are a waste of $$$.
Organization and Culture
 62% of managers: organization
and culture biggest barriers to
getting significant return on IT
investment
 Related to skills and experience
 Value Data-oriented / Fact-based
analysis and decision making
 Organization of analytics staff
 Centralized or decentralized depends
on:
 Sophistication of the analysis
 Amount of local knowledge needed
 Cultural orientation of the firm
Technology and Data
 Specific hardware and software,
networking and infrastructure
 Transaction versus analytic
approach
 Integration of analytic technologies
 Requires human insight; can’t
automate
 60 to 80% of cost in cleaning up and
integrating data
Transformation
Data to Knowledge
 Analytic and Decision Making
Process
 Depends on experience and relationships
of analysts and decision makers
 Working closely with decision makers to
understand the questions:
 Standard, highly-structured: Inventory?
Sales?
 Semi-structured: Optimum inventory
level? Production versus forecasting?
 Unstructured: customer segment
migration?
 An evolving and iterative process
 Use “decision audits” to evaluate
effectiveness of process
Outcomes
 Desired financial outcomes (greater
profitability, revenues, or market
share) may require changes in:
 Behaviors: e.g., cost control
 Processes and Programs: e.g.,
development of new marketing initiative
 Extensive communication may be
required
 Implementation of decisions will
determine result.
Application Methodology
• Flowchart
High quality
transaction
data?
No
Implement
new systems
and data
architectures
No
Launch small
pilots and
educate
managers
No
Launch
analytical
initiative in
single area
No
Launch
analytical
organizational
change
program
Yes
Supportive
senior
executives?
Yes
Broad need in
organization?
Yes
Analytical skills
and culture in
place?
Yes
Integrate
analytical
capabilities
into business
Implementation Options
• Business needs to
dictate extent of
implementation and level
of focus
Examples
Source:
http://www.cs.csi.cuny.edu/~imberman/DataMining/KD
D%20beginnings.pdf
More Results
• Earthgrains eliminated 20% of products,
increased profits by 70%
• Owens & Minor won $100M contract by
showing customer how to save money
• Wachovia Bank improved performance by
modeling branch locations
• Harrah’s Entertainment plans to use
customer data to increase cross-selling
• Fleet Bank saved >$12M encouraging
customers to change from branches to
ATMs
Outcome:
Increased Profitability
Cumulative Profitability Dependence on Route Complexity
50
Cum. Profit ($Millions)
45
40
35
30
25
20
15
10
5
0
0
5
10
15
20
Number of Routes
25
30
35
40
Other Applications of Data to
Knowledge to Results
Source: http://www.cs.csi.cuny.edu/~imberman/DataMining/KDD%20beginnings.pdf
Take-Aways
To get the most from your IT investment:
• Hardware, software, networking and
infrastructure only the starting point
• You need to commit significant skilled
human resources
• Develop sophisticated analytic processes
• Instill culture that values data and creating
information
• Make decisions on info and then execute
Additional Resources
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SAP.com
Oracle.com
Google Analytics
Accenture.com
Spotfire.com
i2.com
Salesforce.com
cio.com
b-eye-network.com
juiceanalytics.com
WonderWare.com
Questions?