Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate.
With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action.
It All Begins with Data
Today, everything is digital, and that makes operations more complex and fragile. As companies add AI tools like customer service bots and coding assistants into the mix, they create even more dependencies and less room for error. That is a significant business risk, particularly when incidents are so costly: PagerDuty found that 68% of organizations lose more than $300,000 per hour during IT incidents, and a third lose at least $500,000.
While AI can contribute to the problem, it can also play a critical role in incident management when applied effectively. IT can enhance triage, run auto-diagnostics, and intelligently route alerts to the right engineers, saving time and reducing alert fatigue. For this to work, however, AI needs a real-time 360-degree view of the environment.
Establishing this level of visibility requires detailed incident origination data, including which systems and services were affected, which rules triggered alerts, and relevant timestamps. It also needs comprehensive resolution data, such as the type of automation used to remediate an issue, status updates, responder communications, and notes that explain why the issue occurred and how it was resolved.
Turning Data Into Value Requires Strong Governance
To be usable, incident datasets must be properly governed, and this governance depends on four key elements:
1. Data integrity
Duplicated records, inconsistent naming, and other issues undermine incident response processes. This leads to slower resolution times, limited ability to continuously improve, and engineers pulled away from their work to deal with false positives. At a time when DevOps talent is in high demand, this could have a direct impact on competitive advantage.
Organizations must work to keep data accurate and standardized, so AI-powered monitoring tools can do their job effectively. This reduces unnecessary noise in incident response workflows, ensures automated runbooks trigger the right actions at the right time, and enables incident post-mortems to generate useful insights.
2. Data security
Beyond the immediate hit to the bottom line, the long-term impact of data breaches on brand reputation and hard-won customer trust can be devastating. Regulators are also increasingly willing to scrutinize security posture and levy financial penalties.
Incident management processes must follow strict data security best practices to protect and encrypt data. These include least-privilege access policies to minimize risk exposure, logging and auditing for compliance, and encryption as a last line of defense. Alerts should also be configured to flag unusual access and usage patterns.
3. Compliance
The compliance landscape is growing more complex by the year. Alongside GDPR and similar data protection laws across US states, the EU AI Act introduces additional obligations for organizations developing, deploying, or selling certain AI systems. With the risk of significant fines, data governance teams must stay informed and continuously evolve their programs in response to changing mandates.
Organizations must meet data sovereignty and residency requirements, optimize data security, and ensure employees are appropriately trained. Supplier vetting is also critical: do data center providers and other partners meet recognized standards such as ISO 27001, SOC 2, and PCI DSS 4.0? Risk does not stop there as fourth-party breaches are an increasing concern.
From an AI perspective, governance teams also need clear visibility into how vendors use AI within their products, including whether these capabilities can be disabled if required. Contracts should define whether customer data is used to train models and under what terms, alongside assurances that human-in-the-loop checks are in place to prevent autonomous high-risk actions.
4. Storage
Storage is often overlooked in incident management, but it plays a critical role in the commercial viability of different approaches. Intelligent alert grouping and suppression reduce the volume of incident records and alerts stored in databases, while runbook automation supports more efficient data lifecycle management once an incident is resolved. This can include automatically deleting unused volumes and snapshots to further reduce costs.
Service graphs can also complement logging by helping teams understand dependencies and relationships across systems, enabling more targeted data collection and storage.
Governance in an Agentic Future
Get these four elements right, and organizations will have a solid foundation for effective incident management. But it's equally important to appreciate how the industry is evolving.
According to our data, three-quarters (75%) of companies have already deployed more than one AI agent, with 25% deploying five or more. These systems promise to automate incident investigation, diagnosis, and remediation to free up human talent to work on higher-value work.
This will only increase the importance of robust data governance. As humans are removed from parts of the decision-making process, data must be as accurate as possible. In a world shaped by autonomous AI, high-quality data is non-negotiable.