An Accuracy Metric: Percentages, Randomness, and Probabilities
Abstract
Practitioners and researchers regularly refer to error rates or accuracy percentages of databases. The former is the number of cells in error divided by the total number of cells; the latter is the number of correct cells divided by the total number of cells. However, databases may have similar error rates (or accuracy percentages) but differ drastically in the complexity of their accuracy problems. A simple percent does not provide information as to whether the errors are systematic or randomly distributed throughout the database. We expand the accuracy metric to include a randomness measure and include a probability distribution value. The proposed randomness check is based on the Lempel-Ziv (LZ) complexity measure. Through two simulation studies we show that the LZ complexity measure can clearly differentiate as to whether the errors are random or systematic. This determination is a significant first step and is a major departure from the percentage-alone technique. Once it is determined that the errors are random, a probability distribution, Poisson, is used to help address various managerial questions.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Real-time data is vital in domains such as stock trading, while daily or weekly updates may suffice for inventory management.
“Real-time data is vital in domains such as stock trading, where rapid changes demand instant responses [37], while daily or weekly updates may suffice for inventory management, helping prevent overproduction or stockouts [38].”
From Data Quality in the Age of AI: A Review of Governance, Ethics, and the FAIR Principles (2025) · cites this paper as [37]
Cites
A formal theory of inductive inference. Part II
Solomonoff, R. J. (1964) · Information and Control · doi:10.1016/s0019-9958(64)90131-7 · Cited by 1,758 · see citing works
Anchoring Data Quality Dimensions in Ontological Foundations
Wand, Y., & Wang, R. Y. (1996) · Communications of the ACM · doi:10.1145/240455.240479 · Cited by 1,479 · see citing works
A formal theory of inductive inference. Part I
Solomonoff, R. J. (1964) · Information and Control · doi:10.1016/s0019-9958(64)90223-2 · Cited by 1,192 · see citing works
Flagged first, then by citations
Research works printed but not linked: 32
- 10.1109/TBME.2006.883696 · DOI
- 10.1145/291469.291471 · DOI
- 10.1080/07421222.2003.11045769 · DOI
- Chaitin G. J., 1975, Randomness and mathematical proof. Sci, Amer., 232, 47
- Clarke R. D. 1946. An application of the poisson distribution. J. Inst. Actuaries 72. Clarke R. D. 1946. An application of the poisson distribution. J. Inst. Actuaries 72 . · DOI
- Dietel E. J., 2003, Recordkeeping integrity: Assessing records’ content after Enron, Inform. Manag. J., 37, 43
- 10.1037/0033-295X.104.2.301 · DOI
- Gelman A. Carlin J. Stern H. and Rubin D. 1995. Bayesian Data Analysis. Chapman and Hall/CRC. Gelman A. Carlin J. Stern H. and Rubin D. 1995. Bayesian Data Analysis . Chapman and Hall/CRC. · DOI · Paper page
- 10.1093/bioinformatics/15.12.994 · DOI
- Huang K.-T. Lee Y. W. and Wang R. Y. 1999. Quality Information and Knowledge. Prentice Hall Englewood Cliffs NJ. Huang K.-T. Lee Y. W. and Wang R. Y. 1999. Quality Information and Knowledge . Prentice Hall Englewood Cliffs NJ.
- Kac M., 1983, What is random, Amer. Sci., 71, 405
- 10.1103/PhysRevA.36.842 · DOI
- Kolmogorov A. N., 1965, Three approaches to the quantitative definition of information, Probl. Inform. Transmis., 1, 1
- Lee Y. W. Pipino L. L. Funk J. D. and Wang R. Y. 2006. Journey to Data Quality. MIT Press Cambridge MA. Lee Y. W. Pipino L. L. Funk J. D. and Wang R. Y. 2006. Journey to Data Quality . MIT Press Cambridge MA. · DOI
- 10.1109/TIT.1976.1055501 · DOI
- Levin R. I. Rubin D. S. and Stinson J. P. 1986. Quantitative Approaches to Management. McGraw-Hill New York. Levin R. I. Rubin D. S. and Stinson J. P. 1986. Quantitative Approaches to Management . McGraw-Hill New York.
- Li M. and Vitanyi P. 1997. A Introduction to Kolmogorov Complexity and its Applications. Springer Verlag Berlin. Li M. and Vitanyi P. 1997. A Introduction to Kolmogorov Complexity and its Applications . Springer Verlag Berlin. · DOI
- The Merriam-Webster Dictionary, Edited by H. B. Woolf
- 10.1080/07421222.2005.11045823 · DOI · Paper page
- Olson J. E., 2003, Data Quality: The Accuracy Dimension. Morgan Kaufman
- Pierce E. M., 1997, Modeling database error rates, Data Qual., 3, 1
- 10.1093/intqhc/12.1.47 · DOI
- 10.1080/07421222.1999.11518259 · DOI
- Redman T., Measuring data accuracy
- 10.1145/269012.269025 · DOI
- 10.1002/j.1538-7305.1948.tb01338.x · DOI · Paper page
- Summers D. C. S. 2006. Quality. Pearson/Prentice Hall Upper Saddle River NJ. Summers D. C. S. 2006. Quality . Pearson/Prentice Hall Upper Saddle River NJ.
- Triola M. F., Elementary Statistics
- 10.1080/07421222.1996.11518099 · DOI · Paper page
- Winkel B. J., e and the Poisson Distribution
- 10.1109/TIT.1977.1055714 · DOI · Paper page
- 10.1109/TIT.1978.1055934 · DOI · Paper page
Cited by
Sorted by citations
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 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
“It can be quantified through error rates, field-and record-level measures [18,32], and newer metrics that distinguish between random and systematic errors [37].”
Suh, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3325892 · Cited by 8 · see citing works
- Authors
- Fisher, C. W., Lauria, E. J. M., & Matheus, C. C.
- Affiliations
- Marist University (US)
- Marist College
- State University of New York (US)
- Venue
- Journal of Data and Information Quality
- Published
- 2009-12
- Volume
- 1
- Issue
- 3
- Pages
- 1-21
- ISSN
- 1936-1955 (ISSN-L)
- 1936-1963
- OpenAlex
- W2090624037
- Cited by
- 40
- References
- 3
- Citations by year
- 2025: 8 · 2024: 3 · 2023: 3 · 2022: 3 · 2021: 1 · 2020: 1 · 2019: 1 · 2018: 4 · 2017: 3 · 2016: 1 · 2015: 3 · 2014: 2 · 2013: 2 · 2012: 2
- FWCI
- 2.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Data and Information Quality (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Quality and Management
- Physical Sciences › Mathematics › Theoretical Computer Science › Algorithms and Data Compression
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- database accuracy, accuracy measures, error rate, Lempel-Ziv complexity, Poisson distribution, data quality measurement
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
