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For a managed service provider or other channel partner, the strongest AIOps and AI-security offering is rarely the one that ingests the most telemetry. It is the one whose signals are complete, accurate, enriched with context, and available in time to act on. That is the central argument of Donogh O’Reilly, senior vice president for Europe at NETSCOUT, in an IT Pro article published September 16, 2026. It is an industry executive’s perspective, not an independent test. The survey data that accompanies the argument shows association between strong data foundations and better AI outcomes, not proof that data quality on its own wins customers or margin. The sections below separate what the article claims, what the wider survey evidence supports, and what a partner can do next.
What data quality means for a telemetry business
Gartner defines data quality by how usable and applicable data is for an organization’s priority use cases, including AI and machine learning. The practical consequence is that there is no single quality threshold. A dataset that is good enough for capacity-trend reporting may be far too sparse for root-cause analysis of a security incident. Quality is judged against a specific job, not against an abstract standard, and that is the lens to use when reading any claim about “clean” or “high-quality” telemetry.
Why more telemetry can make operations worse
The article describes a chain that anyone running a monitoring stack will recognise. Sampled and siloed telemetry makes insights hard to correlate. Disconnected monitoring tools generate alert noise. The resulting burden leaves technicians reconciling information across consoles instead of resolving root causes. The author presents this as an operational pattern rather than a measured finding, so it is best read as a well-argued description of common experience rather than a quantified causal chain.
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Volume is therefore not the bottleneck. Each additional feed brings another schema, another timestamp convention, and another place where an incident can be only half visible. A partner that resells more collection without a way to join it is, in effect, selling more reconciliation work to the customer’s team.
#1 Best Overall
The baseline the article proposes
The article’s criteria for telemetry fall into four attributes and two capabilities. The table below states each one in practical terms and describes what a gap usually looks like in day-to-day operations. The symptoms column is an illustrative reading of the article’s argument, not a result the article measured.
| Criterion | What it means in practice | What a gap typically looks like |
|---|---|---|
| Completeness (attribute) | Every relevant domain and segment is covered, not just sampled flows or selected hosts | Investigations that stall at a boundary the data never crossed |
| Accuracy (attribute) | Values are correct and defined consistently across sources | Two tools reporting different figures for the same event |
| Contextual enrichment (attribute) | Signals carry asset, user, service, and location information | Alerts that cannot be prioritised without a manual lookup |
| Real-time availability (attribute) | Data arrives quickly enough to act on during an incident | Detection that is accurate but arrives after the impact |
| Continuous packet-level visibility (capability) | Ongoing visibility into traffic at packet detail rather than periodic snapshots | Events that fall between samples and are never seen |
| Cross-domain correlation (capability) | Network, application, and security signals can be joined into one view | Technicians correlating timestamps across separate consoles by hand |
What the survey evidence supports
Two recent surveys are often cited in this debate. They measure different things, and they should be read separately.
Rank #2
| Source and date | Population and fieldwork | Finding | How to read it |
|---|---|---|---|
| Gartner, April 16, 2026 | 353 data and analytics and AI leaders; fieldwork November–December 2025 | Organizations reporting 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 | A spending comparison between two groups. Data quality is one of several areas, and Gartner does not say it alone explains the difference. |
| IBM Institute for Business Value, 2025 (published in IBM Newsroom, November 13, 2025) | 1,700 senior data and analytics leaders across 27 geographies and 19 industries; fieldwork July–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 | Respondent-reported views rather than audited financial outcomes. |
Both reports carry strong language from their authors. Rita Sallam, Distinguished VP Analyst and Gartner Fellow, said in the April 2026 release that “D&A leaders play a central role in achieving their organization’s AI value ambition,” and added that “without trust in the data, outputs and decisions of AI models and agents, there is no value from AI.” Ed Lovely, Vice President and Chief Data Officer at IBM, said in the November 2025 release that “enterprise AI at scale is within reach, but success depends on organizations powering it with the right data.” These are expressions of the authors’ view. They are useful framing, but they are not measured effects.
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Gartner’s guidance on data quality gives a workable sequence for a partner or internal team that wants to act on these ideas:
- Map the data use cases and the data behind them, ranked by business value and risk.
- Agree with stakeholders on the quality each use case actually needs before measuring anything.
- Select a small number of dimensions and metrics for the priority use cases. Gartner recommends against applying every dimension in the same way everywhere.
- Profile the priority data to see its current condition, not its assumed condition.
- Monitor the short list of metrics continuously, with a named owner for each one.
Gartner’s nine common dimensions are 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 across datasets. For telemetry work, completeness, accuracy, and timeliness usually sit closest to the article’s criteria, but the choice should follow the use case.
Evaluating tools against the use case
Gartner’s guidance on enterprise data quality tools lists the capabilities a buyer should understand: profiling; parsing, standardizing, and cleansing; analytics and visualization; 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 states that no single capability on its own establishes trusted data.
Feature count is a poor proxy for quality. A better evaluation compares each product against the intended use case, the integrations that feed it, the governance rules that apply, and the team that will own it after deployment. For telemetry platforms, the article’s criteria suggest the following questions. This table is an editorial inference from those criteria, not a tested scorecard.
| Comparison axis | Question to put to a vendor or a proof of concept | Why it matters |
|---|---|---|
| Collection coverage and continuity | Which domains and segments are covered, and what happens during collector outages? | Gaps in coverage become blind spots in investigations |
| Accuracy and consistent definitions | How are the same metrics defined across sources? | Conflicting numbers slow decisions |
| Real-time availability | What is the end-to-end delay from capture to alert for a priority use case? | Late data limits its value in an active incident |
| Contextual enrichment | Which asset, user, and service attributes are attached automatically? | Context determines whether an alert can be prioritised |
| Cross-domain correlation | Can network, application, and security signals be joined in one workflow? | Reduces manual reconciliation during root-cause analysis |
| Fragmentation reduction | How many consoles does an analyst need to resolve a typical incident? | Fewer consoles usually means less noise and faster resolution |
Where the partner opportunity sits
The article identifies three categories. They differ in how directly the author ties them to data quality.
Best Value
Network visibility and packet-level telemetry
The article presents this as the strongest partner category, because it is the capability the author emphasises most: continuous, packet-level visibility that feeds correlation and root-cause analysis.
Enterprise data quality software
This is a broader, secondary category. Its relevance rests on Gartner’s guidance on tools, not on the article’s telemetry argument, so it suits partners whose customers already run data platforms that feed AI workloads.
Managed threat detection and response
The article identifies this as a service opportunity for MSPs. A managed service is only as good as the signal behind it, which is why the telemetry criteria above apply directly to the service’s quality.
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Quick Recap
Limits of the argument
- The author is an executive at NETSCOUT. Claims about visibility, service assurance, security outcomes, false positives, and business opportunity are the author’s view unless independently corroborated.
- The available reporting contains no controlled MSP case studies and no measured revenue results tied to data quality.
- Gartner and IBM figures come from different populations, years, and measures. Combining them into a single causal claim would overstate what either survey shows.
- Quality is use-case dependent. Raising the bar on every dataset costs money and effort that may not return value for lower-stakes uses.
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