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For a managed service provider or channel partner, the advantage in AIOps and AI-enabled security comes from a signal that is reliable, contextual and timely, not from collecting the largest possible volume of telemetry. In practice, data quality means the data is fit for the specific decision it drives. That argument is set out in “Slicing through the static,” published in IT Pro on September 16, 2026 by Donogh O’Reilly, senior vice president, Europe, at NETSCOUT. The sections below separate the article’s reasoning from the independent evidence around it, and turn that reasoning into steps a partner can act on.
What “data quality” means when the use case is the test
Gartner defines data quality by how usable and applicable data is for an organization’s priority use cases, including AI and machine learning. That definition has a practical consequence: a telemetry feed that is good enough for monthly capacity reporting may be unfit for automated incident correlation. Quality is measured against a job, so there is no single threshold that every dataset must meet.
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The telemetry problem the article describes
The article traces a chain of failures that many MSP operations will recognise. Each link is the author’s account of how the problem develops:
- Sampled and siloed telemetry. When data is sampled or held in separate tools, insights are hard to correlate across the estate.
- Disconnected monitoring tools. Each tool raises its own alerts, so the same event can generate noise in several places.
- Reconciliation work. Technicians spend their time matching records across consoles instead of working the problem.
- Delayed root-cause analysis. By the time the data has been reconciled, the incident has already consumed the time that matters most.
The article’s proposed response is fit-for-purpose telemetry, continuous visibility, context enrichment and correlation across domains. The article presents these as operational claims. It does not report quantified causal findings for any of them.
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The baseline attributes to check against
The article’s telemetry argument rests on a short list of attributes. They work well as a checklist for any feed a partner relies on:
- Completeness: Are the sources and time windows needed for the incident actually present? Gaps here produce conclusions that look confident but rest on partial evidence.
- Accuracy: Do the recorded values reflect what happened on the network? Accuracy is the attribute most often assumed rather than tested.
- Contextual enrichment: Does each signal carry the asset, customer, service and location details an engineer needs to act on it?
- Real-time availability: Is the data available while the incident is still developing, not after the post-incident review?
- Continuous packet-level visibility: Does the platform see traffic continuously, rather than in samples that can miss short or intermittent events?
- Cross-domain correlation: Can network, security and application signals be linked to one another?
These are the author’s proposed baseline. Partners should treat them as hypotheses to test in their own environment, not as settled requirements.
Where a partner should start: a practical quality program
Gartner’s guidance on data quality programs suggests a sequence that suits a partner with limited time and staff:
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstall- Map use cases by value and risk. List the decisions your team makes from data, such as incident triage, customer reporting or threat escalation, and rank them by business value and by the cost of getting them wrong.
- Agree the required quality with stakeholders. Engineers, account managers and, where relevant, customers should agree what “good enough” means for each use case.
- Select a short list of dimensions and metrics. Gartner recommends choosing a small number of high-priority measures rather than applying every dimension equally everywhere.
- Profile the priority data. Before tuning alerts, measure how complete, accurate and current the data for the top use cases actually is.
- Monitor the chosen metrics continuously. Quality drifts as tools, integrations and customer estates change, so the measures need an owner and a review cycle.
The nine common dimensions, in operational terms
Gartner lists nine common dimensions of data quality: accessibility, accuracy, completeness, consistency, precision, relevancy, timeliness, uniqueness and validity. Gartner notes that not all of them need to be applied at once, or in the same way everywhere. The table translates each one into a question a partner can put to a telemetry or ticketing feed. The questions are a practical reading of the definitions, not a published Gartner test.
| Dimension | Question to ask of a feed or alert stream |
|---|---|
| Accessibility | Can the engineer who acts on an alert reach the underlying data without a manual export? |
| Accuracy | Do the recorded values match what the device or flow actually did? Spot-check against a packet capture for a sample of events. |
| Completeness | Which sources or time windows are missing during a typical incident? |
| Consistency | Does the same host, customer or service carry the same identifier in every tool? |
| Precision | Is the granularity fine enough to separate one host from a whole subnet? |
| Relevancy | Does each field help the use case, or does it add noise? |
| Timeliness | How old is the data when the alert fires? |
| Uniqueness | Does one incident produce one record, or several duplicate alerts from different tools? |
| Validity | Do values conform to the expected format and range? |
Choosing tools by use case, not feature count
Gartner’s tool guidance lists capabilities that enterprise data quality software may provide: profiling; parsing, standardising and cleansing; analytics and visualisation; matching, linking and merging; multidomain support; business-driven workflow and issue resolution; rule management and validation; metadata and lineage; monitoring and detection; and automation and augmentation. Gartner’s point is that no one capability on that list establishes trusted data by itself.
A partner evaluating a platform should therefore compare candidates against four things: the specific use case, the integrations with the tools already in the estate, the governance model for who sets and changes rules, and who will own the platform operationally after deployment. A long feature list that the team never configures adds cost without adding quality.
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What the surveys show, and what they do not
Two sets of survey findings are often cited alongside the article’s argument. They measure different things, come from different populations and should be read separately.
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|---|---|---|---|
| Gartner, April 16, 2026 | 353 data and analytics and AI leaders; fieldwork November to December 2025 | Organizations with successful AI initiatives invest up to four times more, as a percentage of revenue, in foundational areas including data quality, governance, AI-ready people and change management, compared with organizations reporting poor AI outcomes. | It does not show that data quality alone explains the gap. The figure compares two groups of organizations and does not isolate any single area of investment. |
| IBM Institute for Business Value, 2025 | 1,700 senior data and analytics leaders across 27 geographies and 19 industries; fieldwork July to September 2025 | 84% of surveyed chief data officers said their unique data products had already provided significant competitive advantages. 78% cited leveraging proprietary data as a top strategic objective to differentiate their organization. | These are respondents’ own assessments, not audited financial results, and they do not measure the effect of any particular data quality programme. |
Gartner’s April 2026 release puts the point directly: “Without trust in the data, outputs and decisions of AI models and agents, there is no value from AI,” said Rita Sallam, Distinguished VP Analyst and Gartner Fellow. The surveys support the general direction of the article’s argument, that stronger foundations go with better reported AI outcomes. They do not prove that data quality produces competitive advantage on its own.
Where the partner opportunity sits
The article points to several categories. They differ in how directly the evidence supports them:
- Network visibility and packet-level telemetry platforms. This is the capability the article emphasises most, so it is the most direct partner category in its argument.
- Enterprise data quality software. A broader, secondary category, supported by Gartner’s tool guidance rather than by the article itself.
- Managed threat detection and response. The article identifies this as a service opportunity for MSPs that depends on reliable, correlated signals.
The evidence does not establish partner margins, programme economics or revenue outcomes for any of these categories. Any named vendor or partner programme should be checked directly against that provider’s current terms before a decision.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Limits of the current evidence
- The article is written by an executive at NETSCOUT. Its claims about visibility, service assurance, security, false positives and business opportunity are the author’s views unless independently corroborated.
- No controlled MSP case studies were identified that measure the effect of data quality on incident outcomes.
- No independent comparison of named products’ performance was identified.
- No measured revenue results from channel partners were identified.
- The Gartner and IBM figures are survey findings with different populations and definitions. They should not be combined into a single causal claim.
Diagnosing alert noise: a starting branch
When a partner suspects a data quality problem behind alert noise, the symptom usually points to the dimension to check first. These are practical starting points rather than a tested procedure:
- The same incident opens several tickets. Check uniqueness and whether identifiers are consistent across the tools involved.
- Alerts arrive long after the event. Check timeliness and the collection cadence, including whether sampling is delaying what the platform sees.
- Engineers cannot tell which host or customer is affected. Check consistency, precision and contextual enrichment.
- Alerts look plausible but do not match packet evidence. Check accuracy by comparing a sample of alerts with a packet capture for the same period.
Work through one branch at a time, and fix the source of the signal before tuning alert thresholds, which otherwise only hides the underlying problem.
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Gartner’s April 2026 survey also quotes Rita Sallam on the role of data and analytics leaders, a point worth weighing alongside the operational steps above. Partners that agree quality thresholds with the people who act on alerts, rather than with tool vendors alone, are better placed to judge whether a platform is delivering a usable signal.
The strongest case for data quality as a channel advantage is operational rather than statistical: a partner that can trust its signal can resolve incidents faster, with fewer people reconciling records. That claim is consistent with the article and the surveys, but the evidence reviewed here does not yet measure it directly.
Ed Lovely, Vice President and Chief Data Officer at IBM, put the operational side of the same idea this way in IBM’s November 13, 2025 announcement: “Enterprise AI at scale is within reach, but success depends on organizations powering it with the right data.”
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