A product perspective on total data quality management
Open accessAbstract
To increase productivity, organizations must manage information as they manage products. The field of information quality (IQ) has experienced significant advances during its relatively brief history. Today, researchers and practitioners alike have moved beyond establishing information quality as an important field to resolving IQ problems ranging from IQ definition, measurement, analysis, and improvement to tools, methods, and processes. However, theoretically-grounded methodologies for Total Data Quality Management (TDQM) are still lacking. Based on cumulated research efforts, this article presents such a methodology for addressing these problems. The purpose of this TDQM methodology is to deliver high quality information products to information consumers. It aims to facilitate the implementation of an organization's overall data quality policy formally expressed by top management. Organizations of the 21st century must harness the full potential of their data in order to gain competitive advantage and attain strategic goals.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Deming, W. E. (1982) · Medical Entomology and Zoology · Cited by 7,297 · see citing works
Beyond Accuracy: What Data Quality Means to Data Consumers
Wang, R. Y., & Strong, D. M. (1996) · Journal of Management Information Systems · doi:10.1080/07421222.1996.11518099 · Cited by 4,672 · see citing works
Self-citationA framework for analysis of data quality research
Wang, R., Storey, V. C., & Firth, C. (1995) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.404034 · Cited by 686 · see citing works
Self-citationModeling Information Manufacturing Systems to Determine Information Product Quality
Ballou, D. P., Wang, R., Pazer, H. L., & Tayi, G. K. (1998) · Management Science · doi:10.1287/mnsc.44.4.462 · Cited by 555 · see citing works
Self-citationModeling Data and Process Quality in Multi-Input, Multi-Output Information Systems
Ballou, D. P., & Pazer, H. L. (1985) · Management Science · doi:10.1287/mnsc.31.2.150 · Cited by 542 · see citing works
Data quality requirements analysis and modeling
Wang, R., Kon, H., & Madnick, S. (2002) · Proceedings - International Conference on Data Engineering · doi:10.1109/icde.1993.344012 · Cited by 204 · see citing works
Self-citationMethodology for allocating resources for data quality enhancement
Ballou, D. P., & Tayi, G. K. (1989) · Communications of the ACM · doi:10.1145/62065.62068 · Cited by 82 · see citing works
DoD Guidelines on Data Quality Management.
Cykana, P., Paul, A., & Stern, M. (1996) · IQ · Cited by 35 · see citing works
Data Quality Assurance Activities in the Military Health Services System.
Corey, D. J., Cobler, L., Lonsdale, T., et al. (1996) · IQ · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- Cambridge Research Group. Information Quality Survey: Administrator's Guide. Cambridge Research Group, Cambridge, MA, 1997.
- Firth, C.P. and Wang, R.Y. Data Quality Systems: Evaluation and Imple- mentation. Cambridge Market Intelligence Ltd., London, 1996.
- Wang, R.Y. and Lee, Y.W. Integrity Analyzer: A Software Tool for Total Data Quality Management. Cambridge Research Group, Cambridge, MA, 1998.
- Richard Y. Wang (rwang@mit.edu), a leading authority in data quality research and practice, is Co-Director of the Total Data Quality Management Program at Massachusetts Institute of Technology. Research has been supported in part by MIT's Total Data Quality Management (TDQM) Research Program, Cambridge Research Group, and Naval Command, Control, and Ocean Surveillance Center's (NCCOSC) contract # NM66001-91-D-0103.
Cited by
Sorted by citations
Pipino, L. L., Lee, Y. W., & Wang, R. Y. (2002) · Communications of the ACM · doi:10.1145/505248.506010 · Cited by 1,724 · see citing works
Self-citationHazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014) · International Journal of Production Economics · doi:10.1016/j.ijpe.2014.04.018 · Cited by 863 · see citing works
Zaveri, A., Rula, A., Maurino, A., Pietrobon, R., Lehmann, J., & Auer, S. (2015) · Semantic Web · doi:10.3233/sw-150175 · Cited by 596 · see citing works
Data cleaning: detecting, diagnosing, and editing data abnormalities
Van den Broeck, J., Argeseanu Cunningham, S., Eeckels, R., & Herbst, K. (2005) · PLoS Medicine · doi:10.1371/journal.pmed.0020267 · Cited by 446 · see citing works
Liaw, S. T., Rahimi, A., Ray, P., Taggart, J., Dennis, S., de Lusignan, S., Jalaludin, B., Yeo, A. E. T., & Talaei-Khoei, A. (2013) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2012.10.001 · Cited by 170 · see citing works
Xie, J., Sun, L., & Zhao, Y. F. (2025) · Engineering · doi:10.1016/j.eng.2024.04.024 · Cited by 112 · see citing works
Proactive vs. reactive personalization: Can customization of privacy enhance user experience?
Zhang, B., & Sundar, S. S. (2019) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2019.03.002 · Cited by 109 · see citing works
Critical success factors for ERP system implementation: a user perspective
Reitsma, E., & Hilletofth, P. (2018) · European Business Review · doi:10.1108/ebr-04-2017-0075 · Cited by 80 · see citing works
Data Quality Management in the Internet of Things
Zhang, L., Jeong, D., & Lee, S. (2021) · Sensors · doi:10.3390/s21175834 · Cited by 77 · see citing works
Data quality and the Internet of Things
Liu, C., Nitschke, P., Williams, S. P., & Zowghi, D. (2020) · Computing · doi:10.1007/s00607-019-00746-z · Cited by 71 · see citing works
Data quality certification using ISO/IEC 25012: Industrial experiences
Gualo, F., Rodriguez, M., Verdugo, J., Caballero, I., & Piattini, M. (2021) · Journal of Systems and Software · doi:10.1016/j.jss.2021.110938 · Cited by 68 · see citing works
Evaluating a Model for Cost-Effective Data Quality Management in a Real-World CRM Setting
Even, A., Shankaranarayanan, G., & Berger, P. D. (2010) · Decision Support Systems · doi:10.1016/j.dss.2010.07.011 · Cited by 56 · see citing works
Information Quality Assessment for Data Fusion Systems
Becerra, M. A., Tobón, C., Castro-Ospina, A. E., & Peluffo-Ordóñez, D. H. (2021) · Data · doi:10.3390/data6060060 · Cited by 36 · see citing works
A Survey of Data Quality Measurement and Monitoring Tools
Ehrlinger, L., & Wöß, W. (2022) · Frontiers in Big Data · doi:10.3389/fdata.2022.850611 · Cited by 25 · see citing works
Data Quality Management in Big Data: Strategies, Tools, and Educational Implications
Nguyen, T., Nguyen, H.-T., & Nguyen-Hoang, T.-A. (2025) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2025.105067 · Cited by 20 · see citing works
Data Quality–Driven Improvement in Health Care: Systematic Literature Review
Lighterness, A., Adcock, M., Scanlon, L. A., & Price, G. (2024) · Journal of Medical Internet Research · doi:10.2196/57615 · Cited by 26 · see citing works
“Proposed by Wang [61] in the late 1990s, TDQM adapts traditional total quality management principles specifically to data management, highlighting the importance of data as a key asset or product.”
InfluentialVaknin, M., & Filipowska, A. (2017) · Lecture Notes in Business Information Processing · doi:10.1007/978-3-319-52464-1_15 · Cited by 4 · see citing works
Designing a Data Quality Management Framework for CRM Platform Delivery and Consultancy
Albrecht, R., Overbeek, S., & van de Weerd, I. (2023) · SN Computer Science · doi:10.1007/s42979-023-02196-z · Cited by 2 · see citing works
Schultheis, A., Bertrand, Y., Grüger, J., Malburg, L., Bergmann, R., & Serral Asensio, E. (2025) · IoT · doi:10.3390/iot6040063 · Cited by 1 · see citing works
“First is the Total Data Quality Management (TDQM) cycle, which comprises four main steps: defining, measuring, analyzing, and enhancing data [56,57].”
A FAIR Perspective on Data-Quality Frameworks
Nicholson, N., Negrao Carvalho, R., & Štotl, I. (2025) · doi:10.20944/preprints202507.0064.v1 · Cited by 10 · see citing works
- Authors
- Wang, R. Y.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Wang, Richard Y.
- ORCID
- Wang, Richard Y.
- Venue
- Communications of the ACM
- Published
- 1998-02
- Volume
- 41
- Issue
- 2
- Pages
- 58-65
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2000237244
- Cited by
- 911
- References
- 9
- Citations by year
- 2026: 10 · 2025: 20 · 2024: 27 · 2023: 41 · 2022: 27 · 2021: 41 · 2020: 28 · 2019: 63 · 2018: 39 · 2017: 38 · 2016: 35 · 2015: 46 · 2014: 45 · 2013: 51 · 2012: 42
- FWCI
- 21.48
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Communications of the ACM (publishedVersion)
- UCLA - Biblioteca de Ciencias y Tecnologia Felix Morales Bueno (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Social Sciences › Decision Sciences › Management Science and Operations Research › Knowledge Management and Technology
- Keywords
- information quality, information products, data quality measurement, data quality improvement, information consumers, competitive advantage
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
