Information quality in the context of CRIS and CERIF

1. Information quality in the context of CRIS and CERIF Maximilian StempfhuberGESIS-IZ Social Science Information CentreBonn, Germany CRIS 2008, June 5-7, Maribor

2. Agenda • Information quality (IQ) and CRIS: Why bother? • IQ in the context of (euro)CRIS • Code of Good Practice (CGP) • IQ coverage at CRIS 2002 – CRIS 2008 • IQ research: An overview • IQ and CRIS: Towards better integration • Conclusions

3. Information quality (IQ) and CRIS: Why bother? • CRIS vs. library catalogues, repositories, websites etc.Any difference concerning authority, completeness, correctness? • Are CRISs meant to be of quality? Which? • Does the quality of CRIS contents influence its use? • Are all CRISs the same (concerning quality)? • Networked CRISs: Just add up individual quality? • Would CRISs and the CRIS community benefit of a more explicit, comparable model of quality?

4. IQ in the context of (euro)CRIS – Code of Good Practice The CGP view to (information) quality: “fit for purpose” “To ensure the continued use of a CRIS, it is necessary to provide additional value or benefits to both users and contributors to the system. This may be achieved by adhering to a quality plan which defines the accuracy, timeliness, data completeness, presentation of data to the end user, and the functionality offered by the search software.” CGP V3.0, page 14

5. IQ in the context of (euro)CRIS – Code of Good Practice Currently waterfall-like approach (one „big“ cycle)

6. IQ in the context of (euro)CRIS – Code of Good Practice

7. IQ in the context of (euro)CRIS – Code of Good Practice / CERIF Discussion • Waterfall model might not be adequate for complex scenarios in or for which CRIS are designed • “Fit for purpose” only one (specific) view to quality • General problem: Abstract models (CPG) are hard to translate into actual systems in a deterministic way • General problem: Detailed specifications (CERIF) do not guarantee systems meeting users’ demands

8. Alternative models: Spiral model

9. Alternative models: Model Driven Architecture (MDA) Basic Idea: Separation of concerns(specification from implementation) Provide a platform-independent model (PIM) for CRISs and transformations to generateplatform-specific models (PSM)

10. IQ in the context of (euro)CRIS – CRIS 2002 to 2008 topics Summing up 16 of 69 papers from CRIS proceedings: • Information quality: To improve on aspects like correctness, authoritative registers, controlled vocabularies, persistent identifiers, automatic checking of values and structure are used, and through intellectual processes carried out by experts the data is enriched to make it more useful or trustworthy. Semantic Web technologies are suggested to improve completeness of data (also across individual CRISs). • Data integration: This becomes an issue as soon as data is exchanged or individual CRIS are networked. Methods employed are the certification of information systems, checking of data structures and values against formal requirements, mapping between vocabularies, and automatic and intellectual de-duplication.

11. IQ in the context of (euro)CRIS – CRIS 2002 to 2008 topics Summing up 16 of 69 papers from CRIS proceedings (cont.): • Quality as a process: Checking data towards quality criteria as soon as it is created, using existing data to verify new data, and enabling feedback loops from users of data to incrementally improve overall data quality. • Personalization: Better matching CRIS features (e.g. amount and level of detail of data, presentation of information, availability of features) to the specific demand of individual users or well defined user groups. • For a community of practice, there is not much concerning IQ that can be shared (practices, tools etc.) • Individual results are not generalized; hard (how?) to apply • IQ has not the same coverage as data structures, data exchange etc.

12. What is (information) quality? • Degree to which a set of inherent characteristics fulfills requirements (ISO 9000) • Conformance to requirements (Philip B. Crosby) • "Fitness for use". Fitness is defined by the customer. (Joseph M. Juran) • The quality has two dimensions: "must-be quality" and "attractive quality“ (Noriaki Kano)

13. What is (information) quality? (cont.) IQ or data quality denotes the degree of relevance of information in relation to a specific context and information need: • Requirements may be user specific or very general • Total of all requirements towards information or information products ([information] process oriented view) • Information that is fit for use by information consumers (user oriented view)

14. IQ research: An overview Alternative views to IQ • fit for purpose • exceptional view (quality as something special) • perfection (quality as a consistent or flawless outcome) • value for money (quality in terms of return on investment) • transformation (quality in terms of change from one state to another) Harvey 1995 Question: Which views could contribute / support our approach to build and promote (the quality of) CRIS?

15. Information not based on facts,impartial view, hard to understand Spelling errors, incorrect values, outdated data Violation of domain constraints, company or government regulations Inaccessible or insecure information,difficult to aggregate / transform IQ research: A framework for IQ assessment Accurracy, Timeliness,Completeness Ge&Helfert 2007

16. Information meets standards of accuracy, completeness, and free-from-error Information product must be useful and relevant to the user’s needs Indicates a process by which information consumers regularly receive information in a timely manner Information consumers can easily obtain and manipulate information that adds value to their task IQ research: PSP/IQ as an example for an IQ model Product and Service Performance model for Information Quality (PSP/IQ)Kahn et al. 2002

17. IQ and CRIS: Towards better integration IQ and CRIS – what is missing? • A model (see PSP/IQ) expressing a common approach to IQ, shared by the (euro)CRIS community • Sets of well defined IQ dimensions, matched to the model and user / CRIS providers’ needs (there are over 180 already defined) • Common IQ metrics, connected to IQ dimensions and applicable to “real” CRIS • A shared understanding on how IQ dimensions influence each other

18. IQ and CRIS: Towards better integration IQ and CRIS – what is missing? (cont.) • A set of transformation how to transfer the CRIS IQ model to an individual CRIS (see MDA) • Standardized ways for assessing IQ (measuring and creating IQ metrics) • Tested methods for improving IQ • Formal ways for expressing IQ dimensions at the record / attribute level and on the CRIS level • Agreed on ways how to use information abot IQ

19. IQ and CRIS: What can we gain? • Better and comprehensible service to users • Procedures for improving a CRIS during its lifetime • Promotion of CRIS to external users for critical tasks (research evaluation, strategic planning etc.) • IQ as an incentive for researchers (providing information), sponsors and users of CRIS • Higher IQ for networked CRISs

20. Conclusions • IQ research offers formal models of assessing and improving IQ • IQ in the context of (euro)CRIS currently has not the same role as data structures and semantics (CERIF) • Formalizing IQ in the context of CRIS is a precondition of making CRIS a reliable source of information • The quality of networked CRISs at the ERA level is depending on assessing, preserving and improving IQ

21. Thank You! Dr. Maximilian Stempfhuber GESIS-IZ Social Science Information CentreLennéstr. 30, 53113 Bonn, Germanymax.stempfhuber@gesis.org