MMPRO: A Methodology Based on ISO/IEC 15939 to Draw Up Data Quality Measurement Processes

Nowadays, data plays a key role in organizations, and management of its quality is becoming an essential activity. As part of such required management, organizations need to draw up processes for measuring the data quality (DQ) levels of their organizational units, taking into account the particularities of different scenarios, available resources, and characteristics of the data used in them. Given that there are not many works in the literature related to this objective, this paper proposes a methodology -abbreviated MMPROto develop processes for measuring DQ. MMPRO is based on ISO/IEC 15939. Despite being a standard of quality software, we believe it can be successfully applied in this context because of the similarities between software and data. The proposed methodology consists of four activities: (1) Establish and sustain the DQ measurement commitment, (2) Plan the DQ Measurement Process, (3) Perform the DQ Measurement Process, and (4) Evaluate the DQ Measurement Process. These four activities are divided into tasks. For each task, input and output products are listed, as well as a set of useful techniques and tools, many of them borrowed from the Software Engineering field.

[1]  Zbigniew J. Gackowski.  A formal definition of operation quality of factors: a focus on data and information , 2007, Int. J. Inf. Qual..

[2]  Richard Y. Wang,et al.  Toward quality data: An attribute-based approach , 2014, Decis. Support Syst..

[3]  Richard Y. Wang,et al.  Information Quality (Advances in Management Information Systems) , 2005 .

[4]  Mattias Gustavsson.  Information Quality Measurement , 2006 .

[5]  Martin J. Eppler,et al.  Measuring Information Quality in the Web Context: A Survey of State-of-the-Art Instruments and an Application Methodology , 2002, ICIQ.

[6]  Larry P. English.  Improving Data Warehouse and Business Information Quality: Methods for Reducing Costs and Increasing Profits , 1999 .

[7]  Richard Y. Wang,et al.  IP-MAP: Representing the Manufacture of an Information Product , 2000, IQ.

[8]  Carlo Batini,et al.  Data Quality: Concepts, Methodologies and Techniques (Data-Centric Systems and Applications) , 2006 .

[9]  Mario Piattini,et al.  A proposal for a set of attributes relevant for Web portal data quality , 2008, Software Quality Journal.

[10]  Helena Galhardas,et al.  A Taxonomy of Data Quality Problems , 2005 .

[11]  Mario Piattini,et al.  A Data Quality Measurement Information Model Based On ISO/IEC 15939 , 2007, ICIQ.

[12]  Diane M. Strong,et al.  Data quality in context , 1997, CACM.

[13]  Carlo Batini,et al.  An Analytical Framework to Analyze Dependencies Among Data Quality Dimensions , 2006, ICIQ.

[14]  Yu Cai,et al.  Managing data quality in inter-organisational data networks , 2007, Int. J. Inf. Qual..

[15]  Carlo Batini,et al.  A Framework And A Methodology For Data Quality Assessment And Monitoring , 2007, ICIQ.

[16]  Martin J. Eppler,et al.  A Classification and Analysis of Data Quality Costs , 2004 .

[17]  Richard Y. Wang,et al.  A product perspective on total data quality management , 1998, CACM.

[18]  Alun D. Preece,et al.  An ontology‐based approach to handling information quality in e‐Science , 2008, Concurr. Comput. Pract. Exp..

[19]  M. Oivo,et al.  Application of software measurement at Schlumberger RPS: towards enhancing CQM , 1995 .

[20]  Felix Naumann,et al.  Assessment Methods for Information Quality Criteria , 2000, IQ.

[21]  W. Edwards Deming,et al.  Out of the Crisis , 1982 .

[22]  Mikhaila Burgess,et al.  Quality Measures and The Information Consumer , 2006, ICIQ.

[23]  Thomas C. Redman,et al.  Data Quality: The Field Guide , 2001 .

[24]  Mario Piattini,et al.  DQRDFS - Towards a Semantic Web Enhanced with Data Quality , 2008, WEBIST.

[25]  Mouzhi Ge,et al.  A Review of Information Quality Research - Develop a Research Agenda , 2007, ICIQ.

[26]  Xavier Franch,et al.  Using Quality Models in Software Package Selection , 2003, IEEE Softw..

[27]  Adir Even,et al.  Utility-driven assessment of data quality , 2007, DATB.

[28]  Diane M. Strong,et al.  10 Potholes in the Road to Information Quality , 1997, Computer.

[29]  Thomas Redman,et al.  Data quality for the information age , 1996 .