Exceptional Data Quality Using Intelligent Matching and Retrieval
Open accessAbstract
Recent advances in enterprise web‐based software have created a need for sophisticated yet user‐friendly data‐quality solutions. A new category of data‐quality solutions that fill this need using intelligent matching and retrieval algorithms is discussed. Solutions are focused on customer and sales data and include realtime inexact search, batch processing, and data migration. Users are empowered to maintain higher quality data resulting in more efficient sales and marketing operations. Sales managers spend more time with customers and less time managing data.
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Up to 40% of email addresses change every two years.
“as seen in marketing, where up to 40% of email addresses change every two years [35].”
From Data Quality in the Age of AI: A Review of Governance, Ethics, and the FAIR Principles (2025) · cites this paper as [35]
The Results and Impact section reports faulty and duplicate CRM records, but does not give a rate at which email addresses change. Searches for the 40% figure and a two-year change interval found no matching passage in our copy. (LLM, conf=0.75)
Experimental — accuracy not yet measuredA German survey found that 66% of companies validate data quality with Excel or Access, and 63% do so manually without long-term planning.
“a German survey found that 66% of companies validate data quality with Excel or Access, and 63% do so manually without long-term planning [35].”
From Data Quality in the Age of AI: A Review of Governance, Ethics, and the FAIR Principles (2025) · cites this paper as [35]
The Existing Solutions section describes manual data cleansing using spreadsheets, but does not report a German survey or identify Excel or Access. Searches for the 66% and 63% figures found no passage supporting either statistic or the claim about long-term planning. (LLM, conf=0.77)
Experimental — accuracy not yet measured
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
No references could be resolved yet.
Research works printed but not linked: 12
- Bidlack, C. 2009. Enabling Data Quality with Lightweight Ontologies. In Proceedings of the Twenty-First Innovative Applications of Artificial Intelligence Conference, 2-8. Menlo Park, CA: AAAI Press.
- Chattaraj, A., and Parida, L. 2005. An Inex- act-Suffix-Tree-Based Algorithm for Detect- ing Extensible Patterns. Theoretical Comput- er Science 335(1): 3-14
- Dunn, H. L. 1946. Record Linkage. Ameri- can Journal of Public Health 36(12): 1412- 1416.
- Fellegi, I., and Sunter, A. 1969. A Theory for Record Linkage. Journal of the American Sta- tistical Association 64(328): 1183-1210.
- Larman, C., and Basili, V. 2003. Iterative and Incremental Development: A Brief His- tory. IEEE Computer 36(6): 47-56.
- Lee, Y. W.; Pipino, L. L.; Funk, J. D.; and Wang, R. Y. 2006. Journey to Data Quality. Cambridge, MA: MIT Press.
- Levenshtein, V. I. 1965. Binary Codes Capa- ble of Correcting Spurious Insertions and Deletions of Ones. Problems of Information Transmission 1(1): 8-17.
- Madnick, S. 2009. Overview and Frame- work for Data and Information Quality Research. ACM Journal of Data and Informa- tion Quality Research 1(1).
- Moustakides, G. V., and Verykios, V. S. 2009. Optimal Stopping: A Record-Linkage Articles SPRING 2010 73 Approach. ACM Journal of Data and Infor- mation Quality 1(2).
- Navarro, G. 2001. A Guided Tour to Approximate String Matching. ACM Com- puting Surveys 33(1): 31-88.
- Segaran, T. 2007. Programming Collective Intelligence. Sebastopol, CA: O'Reilly Media, Inc.
- Wang, R. Y.; Storey, V. C.; and Firth, C. P. 1995. A Framework for Analysis of Data Quality Research. IEEE Transactions on Knowledge and Data Engineering 7(4): 623- 640.
Cited by
Sorted by citations
Data Quality in the Age of AI: A Review of Governance, Ethics, and the FAIR Principles
Guillen-Aguinaga, M., Aguinaga-Ontoso, E., Guillen-Aguinaga, L., Guillen-Grima, F., & Aguinaga-Ontoso, I. (2025) · Data · doi:10.3390/data10120201 · Cited by 37 · see citing works
Data Quality in Customer Relationship Management (CRM): Literature Review
Petrović, M. (2020) · Strategic Management · doi:10.5937/straman2002040p · Cited by 19 · see citing works
- Authors
- Bidlack, C., & Wellman, M. P.
- Affiliations
- University of Michigan (US)
- ORCID
- Wellman, Michael P.
- Venue
- AI Magazine
- Publisher
- Wiley
- Published
- 2010-03
- Volume
- 31
- Issue
- 1
- Pages
- 65-73
- ISSN
- 0738-4602 (ISSN-L)
- 2371-9621
- OpenAlex
- W1519949090
- Cited by
- 5
- References
- 0
- Citations by year
- 2021: 1 · 2020: 1 · 2015: 1 · 2013: 2
- FWCI
- 0.87
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- AI Magazine (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Data Mining
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Keywords
- data quality, customer data, sales data, data migration, batch processing, real-time search, enterprise software
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
