Information quality in the context of CRIS and CERIF. Maximilian Stempfhuber GESIS-IZ Social Science Information Centre Bonn, Germany CRIS 2008, June 5-7, Maribor. Agenda. Information quality (IQ) and CRIS: Why bother? IQ in the context of (euro)CRIS Code of Good Practice (CGP)
Maximilian StempfhuberGESIS-IZ Social Science Information CentreBonn, Germany
CRIS 2008, June 5-7, Maribor
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
Currently waterfall-like approach (one „big“ cycle)
Basic Idea: Separation of concerns(specification from implementation)
Provide a platform-independent model (PIM) for CRISs
transformations to generateplatform-specific models (PSM)
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.
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.
IQ or data quality denotes the degree of relevance of information in relation to a specific context and information need:
Alternative views to IQ
Question: Which views could contribute / support our approach to build and promote (the quality of) CRIS?
Information not based on facts, CERIFimpartial 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 / transformIQ research: A framework for IQ assessment
Information meets standards of accuracy, CERIFcompleteness, 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 taskIQ research: PSP/IQ as an example for an IQ model
Product and Service Performance model for Information Quality (PSP/IQ)Kahn et al. 2002
IQ and CRIS – what is missing?
IQ and CRIS – what is missing? (cont.)