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Data Quality for AIOps: Normalizing Logs, Metrics, and Topology Data

Poor data quality can undermine AIOps and create uncertainty across the organization. Here, we explain the effects of data quality and how to improve it at scale.

Explore AIOps fundamentals

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Accurate event correlation, anomaly detection, root cause analysis (RCA), and predictive intelligence all depend on clean, consistent, complete data. Backed by high-quality data, AIOps can become a trusted, dependable guide for understanding large-scale IT operations.

How data quality supports (or Starves) AIOps success

High-quality data improves the accuracy of AIOps insights. With it, teams detect anomalies sooner, fix issues faster, and make decisions with confidence. Poor data generates uncertainty, which limits the impact of AIOps initiatives and slows operations.
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Foundational data sources that power AIOps

AIOps relies on a variety of data sources; poor quality in any of them can undermine the insights that follow. That’s why it’s important to prepare and maintain good AIOps data quality from ingestion to analysis.

Preparing data for AIOps

Data quality for AIOps in action (Operational use cases)

Data quality infiltrates every corner of IT operations, influencing how well systems perform and how confidently teams can act. It’s up to leaders to make sure data is accurate, complete, and consistent. After all, high-quality data is the difference between predictable outcomes and costly chaos.

Halt alert overload before it escalates

To prevent alert fatigue, high-quality logs and events help AIOps filter irrelevant signals and merge duplicates. Poor data can overwhelm teams with repeated or false alarms, causing real incidents to go unnoticed and slowing response.

Locate root cases and correlate events

Accurate, standardized data lets AIOps link related events and quickly identify root causes. Inconsistent or missing data can cascade delays, where one failure causes confusion across various teams and applications.

Prevent resource shortages with accurate forecasts

AIOps can only predict resource needs as well as the metrics it’s fed. Inconsistent or incomplete performance data skews forecasts, leading to unnecessary infrastructure spend or capacity shortfalls that affect teams organization-wide.

Trust system health scores (without a doubt)

Dashboards depend on accurate, real-time logs, metrics, and topology to reflect system health. Poor data quality can make these scores unreliable, which erodes trust and causes teams to prioritize the wrong issues.

As we’ve seen across every use case, consistent data quality is what turns AIOps from a promising “initiative” into a dependable operational capability. 

 

BMC Helix AIOps leverages Helix Discovery, data normalization and reconciliation capabilities to ensure data ingested are accurate, complete, consistent and relevant. Teams can apply high-quality data across their AIOps as their systems, complexities, and data volumes grow.

Data quality failures that undermine AIOps

Unchecked data quality issues quietly derail your AIOps, distorting system behavior and skewing priorities in the wrong direction. And, when these failures occur at enterprise scale, the negative effects ripple across teams, services, and business outcomes.

Duplicates

Duplicate records inflate event volumes and distort system signals. AIOps spends time sorting repeats (instead of identifying real problems), delaying response and resolution.

Gaps

Missing data creates blind spots in system visibility. When signals disappear, AIOps can’t reliably detect patterns. This increases the risk of missed incidents and delays recovery.

Noise

Excessive, low-value data drowns out meaningful signals. Noise reduces confidence in alerts and forces teams to sift through distractions (rather than focus on actual issues).

Inconsistent formats

Inconsistent schemas and naming conventions prevent data from being analyzed together. This fragments understanding and weakens correlation across services and tools.

Stale records

Outdated data skews the current system state. AIOps may base conclusions on assets or relationships that no longer exist, leading to incorrect assessments and actions.

Addressing these common data quality issues requires more than just isolated corrections. It necessitates a platform designed to standardize, correlate, and operationalize data at scale. 
 

BMC Helix Discovery, a key component of the BMC Helix platform, provides the capabilities to do exactly that.

How can teams Improve observability data quality before AIOps?

Prevent incidents and keep IT running without disruption

Read our AIOps guide
Prevent incidents and keep IT running without disruption

In summary

What real impact does data have on your business? Strong data quality turns raw signals into reliable information that decision-makers can act upon with confidence.

IT teams detect anomalies faster, resolve incidents quickly, and protect revenue-critical services – all while avoiding downtime and misaligned priorities.

BMC Helix AIOps helps organizations identify the root causes and resolve incidents faster by leveraging operationally sound data quality. Enterprise leaders can trust they’re making the right decisions at every turn.