Information Quality

Information Quality Research Program

-Improving the value of Business Information and System Quality-

In the current economic situation information supply is more crucial than ever, but today the quality of information and data is worse than ever. In a recent survey, the data warehousing institute estimates that quality problems cost U.S. business more than $ 600 billion a year. Similar, Larry English states the business costs of non-quality data, including irrecoverable costs, rework of products and services, workarounds, and lost of missed revenue may be as high as 10 to 25 percent of revenue or total budget of an organisation. Thus, recently researchers and practitioners have realised the importance of information and data quality. But however, as the data warehousing institute’s survey and many consulting projects indicate, the problem still remains: How to ensure high-level data quality and information quality?

Research Agenda:

In order to achieve and manage high-level data and information quality in information systems, the research program long-term focus is to establish a comprehensive method for proactive data and information quality management, which can be adopted in practical settings.

As part of information management, information quality management establishes data and information quality principles on a strategic level. This forms the core of a data/information quality strategy. Quality planning realises these strategic objectives in operational quality goals. It transfers data quality strategy to operative data quality management, which consists of following key tasks:

· Quality planning gathers data consumer requirements and expectations and transfers them –according to strategic data quality objectives- into data delivery processes and information system specifications (e.g. IS architectures). Quality planning operationalise objectives through data quality criteria (e.g. accuracy, timeliness, completeness, …), which are selected, classified, prioritised and assigned with quality objectives (Þ Data/Information Quality Planning and Definition)

· Quality control monitors data delivery processes and assures they comply with specifications and data quality objectives. In order to identify quality deficits and implement adequate steps, data quality has to be measured and expressed quantitatively on an objective base (Þ Data/Information Quality Analyses, Assessment and Measurement).

· In order to prevent data defects, proactive data quality management provides techniques and tools for analysing causes and impacts of insufficient data quality. Based on this analyses improvement measures are developed and implemented. (Þ Continuous Data/Information Quality Improvement, Cost-Benefit analysis, Benchmarks).

· Proactive DQM is realised through a quality management system, which comprise an organizational structure, management processes and supporting tools and techniques. The organisational structure defines responsibilities for quality management processes (Þ Reference processes and organisation models)

Research topics include for example:

	Developing tools, metrics, standards, methods and reference models for assessing and managing data quality
	Conceptualizing and defining data and information quality
	Analyzing and assessing the quality of data/information in information systems
	Analyzing causes and impacts of and investments in data/information quality
	Cost/Benefit analysis of data quality

	in health information systems
	in financial institutions
	in logistics and multi-organisational information systems
	for knowledge management
	for web environments

For further information on data and information quality see for example Resources on Data and Information Quality

Research Partnership

Within the Research Program organisations work closely together with researchers forming domain-oriented research teams. Research teams address, based on a well-defined project plan, specific research subjects in Data and Information Quality. Advantages from such research co-operation include for example:

	Problem-oriented development of models, methods and tools with a particular focus on the problems of the corporate partner
	Relevance of research results in practice
	Involvement of Ph.D. students in practical projects
	Sharing and complementing expertise in data and information quality through the co-operation of researcher and practitioners
	Platform for open exchange of experiences and networking with other researchers and practitioners (e.g. workshops, conferences).
	Discussion and dissemination of research results within the research community

Student projects and INTRA-Work Placement:

Within our B.Sc. in Computer Applications study program students

	specify and design medium-to-large scale projects (third year and final year) and
	go on INTRA-work placements for six months (April-September)

This gives the students real-world experiences of the professional and practical business problems and offers great opportunities for companies. Both, students and practitioners benefit from these practical oriented projects. Organisations having any project ideas related to data and information quality and are interested in the INTRA-Work Placement program please contact Markus Helfert (markus.Helfert@computing.dcu.ie).

Funding support and contact:

Building practice-academic partnerships and carrying out application-oriented research projects are supported on a national and European level (For further information see for example Enterprise Ireland, Science Foundation Ireland or European Union Sixth Framework Programme). Practitioners interested in a possible research project please contact Markus Helfert (markus.Helfert@computing.dcu.ie).

Ph.D. Opportunities

Students shortly expect to obtain a first class degree in information management, information systems or computing science (with focus on business informatics or information systems) are very welcome to discuss post-grad (Ph.D. and M.Sc.) opportunities within the Research Program. For an informal discussion please contact Markus Helfert (markus.Helfert@computing.dcu.ie).

Contact

Dr. Markus Helfert

School of Computing

Dublin City University

Glasnevin

Dublin 9, Ireland

Phone: +353-1-700-8727

Fax: +353-1-700-5442

Email: markus.helfert@computing.dcu.ie