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arxiv: 1907.08138 · v1 · pith:77PE3HBVnew · submitted 2019-07-18 · 💻 cs.DB · cs.LG

A Survey of Data Quality Measurement and Monitoring Tools

Lisa Ehrlinger , Elisa Rusz , Wolfram W\"o{\ss} This is my paper

Pith reviewed 2026-05-24 19:10 UTC · model grok-4.3

classification 💻 cs.DB cs.LG
keywords data qualitydata profilingdata quality metricsdata quality monitoringsoftware toolssurveydata preprocessing
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The pith

A survey of data quality tools finds that generally applicable metrics are rarely implemented despite wide research acceptance.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper performs a systematic search to identify hundreds of data quality tools and then evaluates 13 domain-independent ones on their support for profiling, metric-based measurement, and continuous monitoring. It establishes that many tools cover basic profiling but fall short on the measurement and monitoring functions that research has emphasized. The evaluation highlights opportunities to expand tool capabilities and enables discussion of why research concepts like broadly usable metrics see little practical adoption. This matters for anyone relying on data analytics, since unmeasured quality issues undermine downstream decisions. The work positions current tools as incomplete relative to established data quality ideas.

Core claim

After identifying 667 tools and evaluating 13 that meet criteria for domain independence and free evaluability, the survey shows common support for data profiling but limited implementation of generally applicable data quality metrics and continuous monitoring. This gap between research literature and tool functionality allows a critical discussion of concepts that are widely accepted in theory yet absent from observed practice.

What carries the argument

Systematic search followed by evaluation of 13 tools across the three areas of data profiling, metric-based data quality measurement, and continuous monitoring.

If this is right

  • Data quality tools can be extended to include more of the measurement functions described in research.
  • Continuous monitoring remains an underdeveloped capability across the evaluated tools.
  • Generally applicable metrics that work across domains are missing from most current implementations.
  • The survey results support targeted enhancements to close the gap between research concepts and tool features.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Tool builders could test whether adopting research-defined metric sets increases user adoption for analytics pipelines.
  • A follow-up inventory could track whether new tools since the survey have narrowed the implementation gap for general metrics.
  • Standardization efforts might focus on metrics that map directly to profiling outputs already common in tools.

Load-bearing premise

The 13 tools chosen after exclusion criteria represent the functional range of current domain-independent data quality tools and the search captured the relevant population without major bias.

What would settle it

Release or discovery of multiple additional domain-independent tools that each provide a suite of generally applicable metrics and continuous monitoring functions would contradict the observed rarity of those features.

Figures

Figures reproduced from arXiv: 1907.08138 by Elisa Rusz, Lisa Ehrlinger, Wolfram W\"o{\ss}.

Figure 1
Figure 1. Figure 1: Systematic Search different search functionalities, we selected the closest search-engine-specific settings to reflect our original search aim [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
read the original abstract

High-quality data is key to interpretable and trustworthy data analytics and the basis for meaningful data-driven decisions. In practical scenarios, data quality is typically associated with data preprocessing, profiling, and cleansing for subsequent tasks like data integration or data analytics. However, from a scientific perspective, a lot of research has been published about the measurement (i.e., the detection) of data quality issues and different generally applicable data quality dimensions and metrics have been discussed. In this work, we close the gap between research into data quality measurement and practical implementations by investigating the functional scope of current data quality tools. With a systematic search, we identified 667 software tools dedicated to "data quality", from which we evaluated 13 tools with respect to three functionality areas: (1) data profiling, (2) data quality measurement in terms of metrics, and (3) continuous data quality monitoring. We selected the evaluated tools with regard to pre-defined exclusion criteria to ensure that they are domain-independent, provide the investigated functions, and are evaluable freely or as trial. This survey aims at a comprehensive overview on state-of-the-art data quality tools and reveals potential for their functional enhancement. Additionally, the results allow a critical discussion on concepts, which are widely accepted in research, but hardly implemented in any tool observed, for example, generally applicable data quality metrics.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper reports a systematic search that identified 667 data quality tools, from which 13 domain-independent tools were selected and evaluated on three functionality areas: data profiling, metric-based data quality measurement, and continuous monitoring. It concludes that widely accepted research concepts such as generally applicable data quality metrics are hardly implemented in the observed tools and identifies potential for functional enhancement.

Significance. A methodologically transparent survey of this kind could usefully document the gap between data-quality research and deployed tools, providing a reference point for both practitioners and researchers seeking to implement more advanced metrics or monitoring.

major comments (2)
  1. [Abstract / Methods] The description of the systematic search (abstract and corresponding methods section) states that 667 candidates were obtained but supplies no search strings, list of queried sources or repositories, date range, or quantitative record of how the exclusion criteria were applied. Without these details the claim that the final 13 tools support the generalization that research concepts are “hardly implemented in any tool observed” cannot be assessed for selection bias or reproducibility.
  2. [Evaluation / Results] The evaluation of the 13 tools on the three functionality areas is presented without an explicit protocol (e.g., test data sets used, criteria for determining whether a metric is “generally applicable,” or how continuous monitoring was verified). This absence directly affects the reliability of the functional-scope comparison that underpins the headline observation.
minor comments (1)
  1. [Abstract] The abstract and introduction should briefly summarize the search and selection numbers so readers can immediately gauge the scope of the survey.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. The comments highlight important areas for improving methodological transparency, which we will address through revisions to the manuscript.

read point-by-point responses

  1. Referee: [Abstract / Methods] The description of the systematic search (abstract and corresponding methods section) states that 667 candidates were obtained but supplies no search strings, list of queried sources or repositories, date range, or quantitative record of how the exclusion criteria were applied. Without these details the claim that the final 13 tools support the generalization that research concepts are “hardly implemented in any tool observed” cannot be assessed for selection bias or reproducibility.

    Authors: We agree that the manuscript would be strengthened by including these methodological details. The systematic search was conducted using specific search strings across multiple sources (including general web searches, academic repositories, and software directories) within a defined time frame, followed by application of the pre-defined exclusion criteria mentioned in the paper. These elements were part of our process but were not reported in full. We will revise the Methods section to add the search strings, queried sources, date range, and a quantitative record (e.g., via a flow diagram) of how exclusions were applied from 667 candidates to the final 13 tools. This will support reproducibility and allow assessment of selection bias. revision: yes

  2. Referee: [Evaluation / Results] The evaluation of the 13 tools on the three functionality areas is presented without an explicit protocol (e.g., test data sets used, criteria for determining whether a metric is “generally applicable,” or how continuous monitoring was verified). This absence directly affects the reliability of the functional-scope comparison that underpins the headline observation.

    Authors: We acknowledge that an explicit evaluation protocol is needed to substantiate the comparisons. Our assessments relied on reviewing publicly available documentation, trial versions, and feature sets of the tools against criteria drawn from data quality literature for profiling capabilities, metric applicability, and monitoring features. However, the paper does not detail the exact verification steps or test data considerations. We will add a dedicated evaluation protocol subsection describing the criteria (including how “generally applicable” metrics were defined based on established dimensions), verification methods for each functionality area, and any use of test datasets or documentation checks. This revision will improve the reliability of the reported findings. revision: yes

Circularity Check

0 steps flagged

No circularity: descriptive survey reports external tool observations without self-referential derivations

full rationale

The paper performs a systematic search and evaluates 13 third-party tools against pre-defined criteria. Its claims (e.g., that generally applicable metrics are hardly implemented) are direct reports on observed software, not predictions or results derived from the paper's own fitted parameters, equations, or self-citations. The selection process and exclusion criteria are methodological choices, not load-bearing self-definitions or renamings of known results. No equations, ansatzes, or uniqueness theorems are invoked that reduce to the authors' prior work or inputs by construction. This matches the default case of a self-contained descriptive survey against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central observations rest on the assumption that the tool identification and selection process is comprehensive and unbiased; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption A systematic search can reliably identify the population of data quality tools and that pre-defined exclusion criteria produce an unbiased sample of 13 evaluable tools.
    Invoked to support the claim of a comprehensive overview of state-of-the-art tools.

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Reference graph

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