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, TransactionsKnowledge / 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

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

Implementation Options Business needs to dictate extent of implementation and level of focus

Examples Source: 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

Other Applications of Data to Knowledge to Results Source:

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

Questions?