On Generating High InfoQ with Bayesian Networks

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1 On Generating High InfoQ with Bayesian Networks Ron S. Kenett Research Professor, University of Turin Chairman and CEO, KPA Ltd.

2 Knowledge Information Quality Data Quality Analysis Quality Goals Examples from: Customer surveys Risk management of telecom systems Monitoring of bioreactors Managing healthcare of diabetic patients 2 Primary Data Secondary Data - Experimental - Experimental - Observational - Observational

3 Bayesian Networks Judea Pearl 2011 Turing Medalist Programmer s nightmare: 1. If the grass is wet, then it rained 2. if the sprinkler is on, the grass will get wet Output: If the sprinkler is on, then it rained 3 Causal calculus Counterfactuals Do calculus Transportability Missing data Causal mediation Graph mutilation External validity

4 Customer Surveys Behaviors Recommendation Loyalty Loyalty Index Attitudes & Perceptions Experiences & Interactions Overall Satisfaction Perceptions Equipment and System Sales Support Technical Support Training Supplies and Orders Software Add On Solutions Customer Website Purchasing Support Contracts and Pricing System Installation 4

5 Customer Surveys Goals Goal 1. Goal 2. Goal 3. Goal 4. Goal 5. Goal 6. Goal 7. Goal 8. Goal 9. Goal 10. Decide where to launch improvement initiatives Highlight drivers of overall satisfaction Detect positive or negative trends in customer satisfaction Identify best practices by comparing products or marketing channels Determine strengths and weaknesses Set up improvement goals Design a balanced scorecard with customer inputs Communicate the results using graphics Assess the reliability of the questionnaire Improve the questionnaire for future use 5

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7 Bayesian Network Analysis of Customer Surveys Kenett, R.S. and Salini S. (2009). New Frontiers: Bayesian networks give insight into surveydata analysis, Quality Progress, pp , August. 7

8 20% BOT12 8 8

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10 13% BOT

11 Information Quality (InfoQ) of Integrated Analysis Models f 1 f 2 f 3 f 4. N f Goals g 1 X X X X 4 g 2 X X 2 g 3 X X 2 g 4 X 1 N g Kenett R.S. and Salini S. (2011). Modern Analysis of Customer Surveys: comparison of models and integrated 11 analysis, with discussion, Applied Stochastic Models in Business and Industry, 27, pp

12 Goal BN CUB Rasch CC 1 Decide where to launch improvement initiatives 2 Highlight drivers of overall satisfaction 3 Detect positive or negative trends in customer satisfaction 4 Identify best practices by comparing products or marketing channels 5 Determine strengths and weaknesses 6 Set up improvement goals 7 Design a balanced scorecard with customer inputs 8 Communicate the results using graphics 9 Assess the reliability of the questionnaire 10 Improve the questionnaire for future use Kenett R.S. and Salini S. (2011). Modern Analysis of Customer Surveys: comparison of models and integrated 12 analysis, with discussion, Applied Stochastic Models in Business and Industry, 27, pp

13 Goal 1: Identify causes of risks that materialized Goal 2: Design risk mitigation strategies Goal 3: Provide a risk management dashboard 13

14 14 Bayesian Network of communication network data

15 ITOpR Vertical Apllication Knowledge Base Storing Area DB Merging Engine B.N. Engine Separated Information Provision TBSI Analyst * Request for Data Merging Support (A B) = relative frequency Merged Information Back Office Activities Confidence(A B)= conditional frequency B.N. Construction And Data Analysis Lift (A B) = Confidence (A B) / Support (B) HyperLift= more robust version of Lift Results Storage * Analyst Request via Form * Request of Information Answer formatting ON LINE Activities * Output to Analyst 15 Data structure and data integration

16 Probability of risks by types and severity 16 Communication and construct operationalization

and Manufacturing, Biopharmaceutical Report, ASA Publications, Vol. 18, No. 2, pp.

17 Peterson, J. and Kenett, R.S. (2011), Modelling Opportunities for Statisticians Supporting Quality by Design Efforts for Pharmaceutical Development and Manufacturing, Biopharmaceutical Report, ASA Publications, Vol. 18, No. 2, pp Monitoring of bioreactor 17 Kenett, R.S. (2012). Risk Analysis in Drug Manufacturing and Healthcare, in Statistical Methods in Healthcare, Faltin, F., Kenett, R.S. and Ruggeri, F. (editors in chief), John Wiley and Sons. Managing diabetic patients

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19 19 Control of bioreactor over time

20 Time Treatment of diabetic patient Decisions Diet Dose 20 Quality Risk Cost Utility

21 Risk 21 Dose

22 Knowledge 1. Data resolution 2. Data structure 3. Data integration 4. Temporal relevance 5. Chronology of data and goal 6. Generalizability 7. Construct operationalization 8. Communication Data Quality Information Quality Analysis Quality Goals 22

On Generating High InfoQ with Bayesian Networks

On Generating High InfoQ with Bayesian Networks Ron S. Kenett Research Professor, University of Turin Chairman and CEO, KPA Ltd. ron@kpa-group.com Based on joint work with Galit Shmueli, Silvia Salini