Information & System Quality

1. Information & System Quality Considering and assuring quality dimensions in architecture design "Drowning in data, yet starved of information"(Ruth Stanat, 1990, in 'The Intelligent Corporation’ ) Ir. Nitesh Bharosa | n.bharosa@tudelft.nl 11-02-2010

2. Who am I? • Nitesh Bharosa • PHD candidate at the ICT Section (finishing in January 2011) • M.Sc. in Systems Engineering, Policy Analysis and Management Thesis: Enterprise Architecture at Siemens • Research interest • information & system quality • orchestration & coordination • enterprise-architecture, SOA, SAAS, • public safety and disaster management • Courses: • SPM3410 Web information Systems and Management • SPM4341 Design of Innovative ICT-infrastructures and services, • guest lectures e-business and management of technology

3. Today’s goals • Understand the concepts of information and system quality in multi-actor environments • Be able to distinguish multiple information quality dimensions • Be able to distinguish multiple systems quality dimensions • Understand principles for assuring information and system quality • Introduction to “Master of Disaster Game”

4. Further reading • Strong, Lee & Wang. (1997). Data quality in context. Communications of the ACM. • Nelson et al (2002). Antecedents of information and system quality. Journal of Management Information Systems. • Bharosa, N., et al (2009). Identifying and confirming information and system quality requirements for multi-agency disaster management. In the ISCRAM 2009 proceedings.

5. Agenda • Background and relevance • Concepts and definitions • Hurdles for IQ and SQ in practice • Complex multi actor case: Disaster management • How do we assure information and system quality in the architecture? • Summary and conclusions

6. When was the last time you were encountered with wrong information?

7. Information Systems Success theory* Information Quality System Quality *Delone & Mclean (1992). Information Systems Success: the quest for the dependent variable. Information Systems Research, 3(1), pp.60-95

8. Relevance of poor IQ for the typical enterprise* • Operational Impacts: • Lowered customer satisfaction • Increased cost: 8–12% of revenue in the few, carefully studied cases • For service organizations, 40–60% of expense • Lowered employee satisfaction • Typical Impacts: • Poorer decision making: Poorer decisions that take longer to make • More difficult to implement data warehouses • More difficult to reengineer • Increased organizational mistrust • Strategic Impacts: • More difficult to set strategy • More difficult to execute strategy • Contribute to issues of data ownership • Compromise ability to align organizations *based on Redman (2002)

9. What is information quality?

10. The concept of quality in Information systems • Quality is not a new concept in information systems management and research • What is ‘new’ is the explosion in the quantity of information and the increasing reliance of most segments of society on that information • Challenges: defining and improving quality for a specific context • Information systems researchers have attempted to define data quality, information quality software quality, system quality, documentation quality, service quality, web quality and global information systems quality

11. Some definitions for IQ • Quality information is information that meets specifications or requirements (Khan & Strong, 1999) • IQ is the characteristic of information to meet the functional, technical, cognitive, and aesthetic requirements of information producers, administrators, consumers, and experts (Eppler, 2003) • Information of high IQ is fit for use by information consumers (Huang, Lee, Wang, 1999, p. 43) • IQ as set of dimensions describing the quality of the information produced by the information system (Delone & Mclean, 1992). • Quality of information can be defined as a difference between the required information (determined by a goal) and the obtained information (Gerkes, 1997)

12. IQ Frameworks * 1 *Lesca & Lesca (1995)

13. IQ Frameworks * 2 *Redman (1996) Data Quality for the information age

14. IQ Frameworks* 3a *Strong, D. M., Lee, Y. W., & Wang, R. Y. 1997. Data Quality in Context. Communications of the ACM, 40(5): pp.103-110.

15. IQ frameworks* 3b *Strong, D. M., Lee, Y. W., & Wang, R. Y. 1997. Data Quality in Context. Communications of the ACM, 40(5): pp.103-110.

16. Discussion:Is there a difference between data quality and information quality?An what about knowledge and wisdom?

17. Transitions from data to wisdom Intelligence Complexity of quality management Based on level of understanding & experience Knowledge Internalization over time (human processing, can be tacit) Information Processing (use of information systems) (raw) Data Volume

19. Data, Information, Knowledge and Wisdom* • Data is an discrete, unitary, and indivisible element which conveys a single value. Data serves as the basis for computation and reasoning to be executed • Information is an aggregate of one or more data elements with certain established relationships, and it has the ability to convey a single, meaningful message • Knowledge is a large-scale selective combination or union of related pieces of information accumulated over a prolonged period of time, and it can be viewed as a discipline area • Wisdom is the new knowledge subset created when the deductive ability acquired by a person after attaining a sufficient level of understanding of a knowledge area is executed *Adapted from Liang (1994)

20. Data to information processing* Al-Hakim (2007) Information Quality Function Deployment

21. Subjective and context dependent nature of information • “Perfect” IQ, is difficult, if not impossible, to achieve • but neither is it necessary! • If users of the data feel that its quality, which can be described by such attributes as accuracy, completeness and timeliness, is sufficient for their needs, then, from their perspective, at least, the quality of the information available to them is fine • Hence we need a clear understanding of user processes and their information needs in specific context

22. What is system quality?

23. System Quality • Defined as: the quality of the information system (as producing system) and not of the information (as product) (Delone & McLean, 1992) • Also not a ‘new’ concept in information systems • However, this concept has received less formal and coherent treatment than information quality • Trend: information systems are becoming more than just single software applications • SQ is also an antecedent for information system success

24. Examples of poor system quality 1

25. Examples of poor system quality 2

26. Complex multi-actor systems • Examples include supply chains, value networks traffic systems and crisis management networks • In such systems, intra- and inter organizational information flows need to be coordinated in order to achieve goals: high interdependency • Information systems play in critical role in the coordination process • Multiple echelons of coordination: strategic, tactical and operational • Actors operate in a complex, dynamic and unpredictable task environment

27. IQ & SQ issues during disaster response • Chernobyl (1986) • Herculus (1999) • Enschede (2000) • New York (2001) • Singapore (2003) • Tsunami (2004) • Schiphol (2006) • Delft (2008) • … Disaster Management

28. Complexity: heterogeneous actors and systems during 9/11 response *source: Comfort, L. (2002), ‘‘Rethinking Security: Organizational Fragility in Extreme Events,’’ Public Administration Review 62, Special Issue (September), 98–107

29. Information flows in the Netherlands Strategic Echelon Tactical Echelon Emergency Control room Operational Echelon

30. Practice 1: distributed teams Manual situation report generation

31. Practice 2: several information types, formats, sources and technologies

32. Examples of poor IQ during disasters

33. Examples of poor SQ during disasters

34. Main Challenge: Assuring IQ and SQ in MAS Information Quality + ? System Quality +

35. Some generic steps in the assurance process • Understand the stakeholder goals and information needs • Model the process and information flows • Define clear IQ and SQ measurement instruments • Analyze hurdles for IQ and SQ (symptoms) on the various architectural layers (i.e., via observations and interviews) • Synthesize principles for assuring IQ and SQ • Implement and evaluate principles (i.e., prototyping, gaming simulation) • Train awareness: information as a product • Capture feedback and start over again (continuous process)

36. 1. Stakeholder Analysis • Consumers/clients • Process architects • Database architects • Data suppliers • Application architects • Communication trainers • Programmers • Managers (CIO, CTO etc) • Auditors etc

37. 2. Process and information flow modeling

38. 3a. IQ and SQ measurement • Context dependent • Multidimensional constructs • Subjective: dependent on the user judgment • So, how do we measure IQ and SQ? • Need for multiple instruments • Questionnaires (paper or online) • Observations • Interviews • Focus groups • Gaming

39. 3b. IQ measurement items

40. 3c. SQ measurement items

41. 4. Hurdles in MAS

42. 5a. Strategies to avoid poor IQ and SQ • Sender or source based strategies • e.g., rules and policies, data cleansing • Receiver or destination based strategies • e.g., filters, aggregation algorithms • Mediation or network based strategies • e.g., stewardship and “Information Orchestration”

43. 5b. Conventional source based techniques for IQ improvement • data cleansing & normalization (Hernadez & Stolfo, 1998), • data tracking & statistical process control (Redman, 1996), • data source calculus & algebra (Lee, Bressen, & Madnick, 1998) • data stewardship (English, 1999) • dimensional gap analysis (Kahn, Strong, & Wang, 2002) • Usually there are four steps involved • Profiling and identification of DQ problems • Reviewing and characterize of expectations (business rules) • Instrument development and Measurement • Solution proposition and implementation

44. 5c. Conventional techniques for SQ improvement • More/better hardware • More/better software • Reduce number of nodes in the information flow • Redundancy (reliability and robustness) • Less forms and procedures in the information exchange process

45. 5d. Limitations of conventional assurance approaches • More databases and technologies include higher cost and do not solve IQ and SQ problems in coherence • Assume a “static” data layer • Do not address task environment dynamics and uncertainty • Reactive, do not include strategies for sensing and adapting • Need for proactive mechanisms to deal with dynamic information needs

46. 5e. An information orchestration approach Offensive Advance structuring strategy Dynamic adjustment strategy Preemptive principles (e.g., IQ auditing) Exploitative principles (e.g., proactive sensing) During a disaster Before a disaster Information Orchestration Protective principles (e.g., dependency diversification) Corrective principles (e.g., IQ rating) Defensive 46

47. 5f. Advance structuring strategy and principles • Examples of preemptive principles • Treat information as product not by-product • Organize IQ audits on a regular basis • Assign IQ roles and responsibilities across organizational units • Examples of protective principles • Maximize the number of sources for each information object • Define several information access and manipulation levels • Strive for loosely coupled application components

48. 5g. Dynamic adjustment strategy and principles • Examples of exploitative principles • Anticipate information needs prior to the occurrence of events • Exploit multi-channel and technology convergence • Scan the environment for complementary information • Examples of corrective principles • Maximize the number of feedback opportunities across the network • Develop policies for ascertaining information needs, acquiring and managing information throughout its life cycle • Encourage a sharing culture (data to information transformation by collective interpretation, discussion & expert analysis)

49. 6a. Prototyping

50. 6b. Gaming simulation