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A Semiotic Information Quality Framework: Applications and Experiments

A Semiotic Information Quality Framework: Applications and Experiments. Mr Gregory Hill, Prof. Graeme Shanks and Dr Rosanne Price Clayton School of IT Monash University, Australia. Overview. Research Context Theoretical Basis Semiotic Framework Ontological Model Information Theory

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A Semiotic Information Quality Framework: Applications and Experiments

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  1. A Semiotic Information Quality Framework: Applications and Experiments Mr Gregory Hill, Prof. Graeme Shanks and Dr Rosanne Price Clayton School of IT Monash University, Australia

  2. Overview • Research Context • Theoretical Basis • Semiotic Framework • Ontological Model • Information Theory • Experiments • Impact of Data Quality Tagging • Impact of Data Quality Treatment

  3. Research Context • Semiotic Framework proposed • Shanks and Darke (1998) • Further theoretical and empirical development • Shanks and Price (2002-2005) (assessment) • Hill (2004) (measurement)

  4. Theoretical BasisSemiotics • Semiotics • Theory of signs and symbols • Philosophy, linguistics, information systems • Understand signs at different levels • Syntactic (form) • Semantic (meaning) • Pragmatic (use)

  5. Theoretical BasisSemiotics - cont’d • Syntactic Quality • Conformance to meta-data • Semantic Quality • Correspondence to external world • Pragmatic Quality • Stakeholder assessment • Ratings (scores) • Utility (prices)

  6. External World Representation X W State Transitions Theoretical BasisOntological Model • Proposed by Wand and Wang (1996) • Incompleteness • Ambiguity • Incorrectness (garbling) • Meaninglessness • Measurement?

  7. Theoretical BasisInformation Theory • Proposed by Shannon and Weaver (1949) • Quantifies amount of information • Information is “uncertainty removed” • Entropy: H(X) = – E[log p(x)] = - p(x) log p(x) • Mutual Information: I(X;Y) = H(X) - H(X|Y) • Used in information economics, psychology, genetics, game theory, cryptography, coding … but not information systems?

  8. Pragmatic Semantic Syntactic Theoretical BasisModel Comparison Empirical Subjective Assessment - Service-based Ontological Model Subjective Assessment - Product-based Integrity Rules Economic Subjective Measurement - Utility Theory Ontological Model Objective Measurement - Information Theory Integrity Rules Semiotic Theory

  9. Experiment IImpact of Data Quality Tagging • Data quality tags for human decision-making • Prior data quality tagging experiments • Chengular-Smith et al (1999) • Shanks and Tansley (2002) • Fisher et al (2003) • Form of data quality tags • Single criterion • Objective normalised score

  10. Experiment IImpact of Data Quality Tagging - cont’d • Context-dependent tags • Semantic level criteria • Organisational role and task • Administrative/geographic context • Form of tags • Subjective (Likert Scale ratings) • Objective (for comparison)

  11. Independent Dependent Measures Variables Variables Decision Selected Complacency Apartment Decision Strategy Decision Consensus Task Complexity Decision Decision Time Efficiency Data Quality Tagging Decision Confidence Confidence Rating Experiment IImpact of Data Quality Tagging - cont’d

  12. Experiment IIImpact of Data Quality Treatments • Treatment of “dirty data” in CRM processes • Simulation of “real-world” scenarios • Treatments (via garbling) • Outcomes (via pay-offs) • Discover antecedents of value-creation • Scenario (process, pay-offs, customer attributes) • Data quality treatment

  13. Information System External World Treatment Process Noise Process Customer Attributes Customer Attributes Customer Attributes Decision Process Outcome Outcome Outcome Pay-offs Experiment IIImpact of Data Quality Treatments - cont’d

  14. Experiment IIImpact of Data Quality Treatments - cont’d • Value model of CRM processes • Hill (2004) • SIFT metrics for planning and monitoring • Stake (pragmatic) • Influence (pragmatic) • Fidelity (semantic) • Tweak (semantic)

  15. Dependent Variables Independent Variables Construct Organisational Impact Measure Scenario Decision Process Treatment Treatment Treatment Influence Stake Fidelity Tweak Value Experiment IIImpact of Data Quality Treatments - cont’d

  16. Questions research@greg-hill.id.au

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