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
Dec 20, 2015
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
Parts per hour Billing rate Click through rate
Profit maximizing product mix 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
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
TransformationData 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 qualitytransaction
data?
Analytical skillsand culture in
place?
Broad need inorganization?
Supportivesenior
executives?
Yes
Yes
Yes
Integrateanalytical
capabilitiesinto business
Implementnew systems
and dataarchitectures
Launch smallpilots andeducate
managers
Launchanalyticalinitiative insingle area
Launchanalytical
organizationalchangeprogram
No
No
No
No
Yes
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 >$12M encouraging customers to change from branches to ATMs
Outcome: Increased Profitability
Cumulative Profitability Dependence on Route Complexity
0
5
10
15
20
25
30
35
40
45
50
0 5 10 15 20 25 30 35 40
Number of Routes
Cum
. Pro
fit ($
Mill
ions
)
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
• SAP.com • Oracle.com • Google Analytics• Accenture.com• Spotfire.com• i2.com• Salesforce.com• cio.com• b-eye-network.com• juiceanalytics.com• WonderWare.com