1. CRICOS #00212K Information Quality Assessment Model: Shahan Mafuz Master of Business Informatics u121056@uni.canberra.edu.au Australian Government Information Systems Context

2. CRICOS #00212K Lets Cook A Delicious Dish

3. CRICOS #00212K Contents  Data, Information & Knowledge: The Definitions  Data Quality Vs Information Quality: Definition & Distinction  Theoretical Frame of Reference  Derived Hypothesis  The Context of Research  Strategy & Methodology of Research  Contributions  Assumptions & Limitations

4. CRICOS #00212K Data, Information & Knowledge: The Definitions Data: “set of values recorded in an information system, which are collected from the real world, generated from some pre-defined procedures, indicating the nature of stored values, or regarding usage of stored values themselves; or, a model for the purpose of organizing, constraining, representing those values in an information system for its consumers.” Information: “carried by non-empty, well-formed, meaningful, and truthful data, is a set of states of affairs, which are part of the real world and independent of its receivers.” Knowledge: “the combination of data and information, to which is added expert opinion, skills, and experience, to result in a valuable asset which can be used to aid decision making.”

5. CRICOS #00212K DQ Vs IQ: Definition & Distinction Data quality : “the gap between physical characteristics of signs and their specifications (smaller gap, higher quality).” Data Quality: “the intrinsic quality of data (a type of information bearer) itself.” Information Quality: “the degree to which the information is represented and to which the information can be perceived and accessed.” Information Quality: “information’s fitness for purpose.” Information Quality : “quality of outputs the information system produces, which can be in the form of reports or online screens”

6. CRICOS #00212K Theoretical Frame of Reference  Information quality impacts decision quality (Raghunathan 1999)  Data and Information are different; as such IQ differs from DQ (Hu & Feng 2005; Baškarada & Koronios 2013)  DQ Dimensions (Wang et al. 1996) are distilled into IQ metrics for many IQ assessmentsDimensions  Information System (IS) Quality impacts IQ and together with Information Service Quality impacts organization (Gorla et al. 2010).  Information can be viewed as a service in addition to product (Lee et al. 2007). Implying the importance of Information Service Quality.  High DQ may NOT be sufficient for better IQ (Melkas & Uotila 2008)  Metadata is a crucial part in defining and contextualising data

7. CRICOS #00212K Dimensions of DQ and derived criteria (Wang et al. 1996; Hu & Feng 2005)DQ

8. CRICOS #00212K Derived Hypothesis HYPOTHESIS 1: The relationship between Information Quality and data quality is one where IQ is a superset of data quality and dependent on it. HYPOTHESIS 2: Data Quality independently is NOT sufficient to achieve Information Quality, assuming other factors are constant. HYPOTHESIS 3: IQ ∝ Data Quality + Quality of (Metadata + Information Representation + Information Accessibility + Information System Service)

9. CRICOS #00212K The Context of Research : HEIMSHEIMS Data Quality Information Quality

10. CRICOS #00212K Research Methodology  Case Study  HEIMS Data Stores and associated Info. Systems  Mixed method approach to research & Data Analysis  Qualitative approach to deriving IQ indicators  Ethnography for information user experiences  Quantitative analysis of collected system data  Quantitative analysis of collected survey/interview data  Design Science approach to develop & Refine IQ assessment model

11. CRICOS #00212K Strategy & Sequence of Research  Extensive literature review  Formulation of an IQ assessment model with respect to HYPOTHESES. o Derive indicators from DQ and IQ dimensions o Convert indicators into workable variables o Create metrics/measures from variables  Create an instrument based on the IQ assessment model above  Select sample information system (HEIMS, HEIMS-Help, HEIMS Reports/dashboards, etc.)  Collect data about HEIMS by applying the IQ assessment model instrument  Analyze results from the above application  Compare results against ‘standalone’ DB-restricted data quality results  Provide recommendations from findings  Refine IQ assessment model for scalability and potential wider use

12. CRICOS #00212K Assumptions & Limitations Credibility of the information source for HEIMS Expertise of Information User Information System Quality

13. CRICOS #00212K Contributions  Support and further progress the notion that although IQ includes data quality, it is different from data quality  Forward the information centric as opposed to the data centric perspective of IQ  Add value to the AGIMO strategy and vision in information management  Provide recommendations to the Department of Education for HEIMS information quality  Support the thinking that data quality is information systems professional centered while IQ is information user centered

14. CRICOS #00212K Select Bibliography Madnick, S.E., Wang, R.Y., Lee, Y. & Zhu, H. 2009, "Overview and Framework for Data and Information Quality Research", Journal of Data and Information Quality (JDIQ), vol. 1, no. 1, pp. 1-22. Pitt, L.F., Watson, R.T. & Kavan, C.B. 1995, "Service Quality: A Measure of Information Systems Effectiveness", MIS Quarterly, vol. 19, no. 2, pp. 173-187. Wang, RY & Strong, DM 1996, 'Beyond accuracy: What data quality means to data consumers', Journal of Management Information Systems, vol. 12, no. 4, pp. 5-23, viewed 15 April 2014,.http://web.mit.edu/tdqm/www/tdqmpub/beyondaccuracy_files/beyondaccuracy.html> Uotila, T. & Melkas, H. 2007, "Quality of data, information and knowledge in regional foresight processes", Futures, vol. 39, no. 9, pp. 1117-1130. Slone, J.P. 2006, Information quality strategy: An empirical investigation of the relationship between information quality improvements and organizational outcomes, ProQuest, UMI Dissertations Publishing. Bandyopadhyay, R. 1977, "Information for Organizational Decisionmaking-a Literature Review", IEEE transactions on systems, man, and cybernetics, vol. 7, no. 1, pp. 1-15. Wang, R., Allen, T., Harris, W. & Madnick, S. 2003, An Information Product Approach For Total Information Awareness. Lee, S.H. & Haider, A. 2013, "Identifying relationships of information quality dimensions", PICMET,, pp. 1217. Alberts, D.S., Vassiliou, M. & Agre, J. 2012, "C2 information quality: An enterprise systems perspective", IEEE,, pp. 1.

15. CRICOS #00212K Slone, J.P. 2006, Information quality strategy: An empirical investigation of the relationship between information quality improvements and organizational outcomes, ProQuest, UMI Dissertations Publishing. Bandyopadhyay, R. 1977, "Information for Organizational Decisionmaking-a Literature Review", IEEE transactions on systems, man, and cybernetics, vol. 7, no. 1, pp. 1-15. Wang, R., Allen, T., Harris, W. & Madnick, S. 2003, An Information Product Approach For Total Information Awareness. Baskarada, S & Koronios, A 2013, Data, Information, Knowledge, Wisdom (DIKW): A Semiotic Theoretical and Empirical Exploration of the Hierarchy and its Quality Dimension, vol. 18, 2013. Gorla, N., Somers, T.M. & Wong, B. 2010, "Organizational impact of system quality, information quality, and service quality", Journal of Strategic Information Systems, vol. 19, no. 3, pp. 207-228. Raghunathan, S. 1999, "Impact of information quality and decision-maker quality on decision quality: a theoretical model and simulation analysis", Decision Support Systems, vol. 26, no. 4, pp. 275-286. Lee, Y.W., Strong, D.M., Kahn, B.K. & Wang, R.Y. 2002, "AIMQ: a methodology for information quality assessment", Information & Management, vol. 40, no. 2, pp. 133-146. Michnik, J. & Lo, M. 2009, "The assessment of the information quality with the aid of multiple criteria analysis", European Journal of Operational Research, vol. 195, no. 3, pp. 850-856. Hu, W & Feng, J 2005, Data and Information Quality: an Information-theoretic Perspective, viewed 10 April 2014,.http://cis.uws.ac.uk/research/journal/V9/V9N3/IQ.doc> Bing, C, Beizhan, W, Chengman, Z & Xueqin, H 2009, 'Research and Implementation of Information Quality Improvement', paper presented to Cooperation and Promotion of Information Resources in Science and Technology, 2009. COINFO '09. Fourth International Conference on, 21-23 Nov. 2009. Rogova, GL & Bosse, E 2010, 'Information quality in information fusion', paper presented to Information Fusion (FUSION), 2010 13th Conference on, 26-29 July 2010. Yang, J, 2010, '"The knowledgemanagementstrategyanditseffectonfirmperformance:A contingencyanalysis", International Journal of Production Econimics, 125, pp. 215-223 Wikipedia, 2013, "Information Quality", viewed 15 April 2003, Select Bibliography (Cont/d…)

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18. Potential Challenges ?End-to-End access to the HEIMS & Sub-Systems ?Changes to HEIMS during the course of research ?Access to AGIMO expertise ?Access to HEIMS information users

19. CRICOS #00212K The Context of Research : HEIMSHEIMS Metadata Education Providers Policy Makers Information Users Data Quality Information Quality