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That is the central argument in Raj Joseph’s DQLabs article “The Noise in Modern Data Quality,” last updated April 23, 2026. Its six factors—scale, context, maturity, business impact, time and cost to value, and stewardship—are Joseph’s criteria, not a standardized scoring model or an independently established industry consensus.
What does “noise” mean in data quality?
Noise is activity that looks like quality management but does not help people decide whether data is fit for a purpose or what to do when it is not. A large volume of automated checks is not proof of trust: a rule can be technically correct yet business-irrelevant, and an alert can identify an unusual value that was intentional.
Joseph’s framing is useful because it treats quality as an organizational capability, not just a collection of tests. He writes in the DQLabs article, “The need for high-quality, trustworthy data in our world will never go away.” The harder question is whether a program helps answer: “what data is good for what purpose?”, “what data can be used where?”, “how can we improve?”, and “What data is sensitive?”
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Scale
A program should handle larger and more varied datasets and architectures without making every new source or check a bespoke engineering project. If coverage depends on one-off code that is difficult to maintain, the organization may add data faster than it can establish confidence in it.
Context
A value cannot always be judged in isolation. Joseph’s example is annual income: its meaning and acceptable use may differ between marketing, risk analysis and underwriting. As he puts it, “If we don’t understand the data from a context, it’s pretty much useless putting any solution.” A useful rule therefore states the intended use and business definition, not just a technical pattern.
Maturity
Organizations change. Growth, new systems and mergers and acquisitions can leave a company with different processes and data environments over time. A workable approach should accommodate that evolution rather than assume that the current architecture or operating model will remain fixed.
Business impact
A statistical outlier is a signal to investigate, not a verdict that the data is wrong. Joseph’s example is a deliberate price reduction intended to improve customer retention: it may look anomalous while accurately reflecting a business decision. The question is whether the data serves its purpose and whether the change has the consequences the business intends.
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Implementation effort has to be weighed against a changing data landscape, regulatory needs and customer expectations. A program that takes too long or costs too much to deliver useful results can lose support before it improves trust. The DQLabs article presents this as an evaluation factor; it does not provide a numerical benchmark or scoring threshold.
Stewardship
Technical teams can build pipelines and checks, while business users understand definitions, intended uses and consequences. Both need a role in improving quality and creating shared understanding. Without clear ownership, an alert may be detected but never interpreted or resolved by someone authorized to make the relevant decision.
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How data quality differs from data observability
Data observability monitors the behavior and health of data assets and pipelines. Common signals include freshness, row-volume anomalies, distribution changes, schema drift, pipeline failures and lineage. These signals help reveal that something changed or failed.
Data quality defines what “good” means for a business purpose and checks whether data meets that definition. Ataccama’s March 19, 2026 article on data quality and observability describes validation dimensions such as validity, completeness, uniqueness, accuracy and timeliness. It makes the distinction succinctly: “Pipeline health is not the same thing as business correctness.”
A table can arrive on time, have the expected number of rows and show no schema changes while still containing values that are wrong for their intended use. Conversely, a distribution shift may be valid because a business policy changed. Observability points to where and when behavior changed; business definitions and validation help determine whether that change is a defect.
How to turn an alert into useful action
Ataccama describes a practical operating loop as detect, triage and remediate. For it to work, an alert needs enough context for a person to assess the issue and take responsibility for it.
Detect and triage
A useful alert should explain what changed, how far it deviated, which downstream assets may be affected, who owns the data and whether a similar incident happened before. Lineage can help trace an upstream origin and downstream impact; ownership and business definitions help route the issue to people who can decide what it means.
Joseph’s warning about alert volume is direct: “The last thing anyone wants to do is get spammed — doesn’t matter if it’s email or slack or spending hours on root cause analysis to figure out it’s OK!” Anomaly detection can surface unexpected patterns, but unusual does not automatically mean harmful. Suppression and grouping are useful only if they reduce irrelevant alerts without concealing genuine incidents.
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Remediate and prevent recurrence
Once a person determines that a change is a real problem, the response may be to correct a recurring pattern, tighten an appropriate governed rule or move a check upstream where prevention is possible. Ataccama presents these as operating practices; they are not a performance guarantee for any particular product or program.
Anomalo’s platform page describes capabilities including unsupervised machine-learning checks, false-positive suppression, alert routing, root-cause analysis and lineage. Those are vendor-described features, not independent comparative measurements. When evaluating any such capability, ask how it behaves with your definitions, data and alert ownership—not only whether it appears on a feature list.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate a data-quality approach
Compare both the technology and the operating model. A program can have broad monitoring and still lack business validation, or have detailed rules that are too difficult to maintain across an evolving environment. Use these questions to make the comparison concrete:
- Coverage: Does it combine business-specific validation with monitoring for freshness, schema, volume and distribution changes?
- Meaning and criticality: Can teams express organizational definitions, intended use and the importance of each data asset?
- Impact: Can people trace lineage and understand which downstream assets may be affected?
- Alerts and ownership: Does an alert include actionable context, route to an accountable owner and distinguish investigation-worthy changes from expected behavior?
- Users: Can both business stewards and technical users participate in defining, reviewing and improving quality?
- Fit and change: Does it fit the organization’s architecture and integrations, and can it accommodate changing systems and processes?
- Effort: What implementation and ongoing work are required, and how soon can the approach produce useful value?
These are comparison axes synthesized from Joseph’s six factors and Ataccama’s distinction between quality and observability. They do not rank products or imply that a particular vendor has been tested against another.
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