Data Governance Visionaries: Managing Alert Fatigue Across the Enterprise

“Data Governance Visionaries” series shines a spotlight on enterprise leaders and their best practices.

Data Governance Visionaries is a multi-month series featuring the best of what leaders and practitioners are learning in the field. Previously, we covered the four revolutions in data governance and how to hire a data governance leader. This week, we’re addressing how the proliferation of monitoring tools can lead to alert fatigue. 

Increasingly, data teams face the same challenges as on-call DevOps teams: they are inundated by excessive notifications, potentially missing critical issues, unable to become proactive as they work through an ever-growing backlog of noisy alerts. 

This guide outlines a four-step approach to effectively managing alerts and keeping teams focused on what truly matters: using data as a competitive advantage to drive business value. We end with a recommended alert audit checklist and additional ways that Anomalo can help data teams get ahead of incidents with intelligent alerting, root cause analysis, and scaling.

How Data Teams Can Manage Alert Fatigue in 4 Steps

Step 1: Know Where Your Alerts Are Going

Alerts only add value when they reach the right people. Ensuring that alerts are properly routed minimizes noise and prevents teams from being overwhelmed by irrelevant notifications.

• Customize alert destinations: Use Anomalo’s alert routing features to ensure that engineering teams don’t receive finance-related alerts and vice versa.
• Check default notification settings: Your organization’s default alert destination is applied unless changed. Review these settings to ensure they align with your needs.
• Leverage multi-channel tools: Platforms like Slack and Teams allow for more granular routing, improving alert relevance and reducing unnecessary notifications.
• Audit destinations periodically: Spot-check alert configurations to verify that critical checks are reaching the right stakeholders.

By taking control of alert destinations, data practitioners can ensure that they receive only the alerts that matter to them, reducing unnecessary noise and churn.

Step 2: Understand Alert Priority

Not all alerts require immediate attention. Prioritizing alerts effectively ensures that teams focus their efforts on issues that truly impact the business. Teams should use priority levels wisely:

• Low Priority: No alerts are sent, but failures are logged for reference.
• Normal Priority: Alerts slow down after a specified number of consecutive failures.
• High Priority: Alerts fire every time the check fails, no matter how many times it has happened.

We recommend using high priority only for critical tables and checks, as overusing high priority can create unnecessary noise. We also recommend enabling success alerts only where needed. For example, organizations can configure Anomalo to send success notifications after a failure, but this should be used selectively to avoid unnecessary volume. 

A well-balanced priority structure ensures that failures are noticed without overwhelming teams with redundant notifications.

Step 3: Figure Out Your Maximum Alert Volume

Alerts are only helpful if they can be processed. To prevent overload, determine how many alerts your team can realistically acknowledge and act upon. 

Here are common ways unnecessary alerts slip in:

• SLAs are set too early for lagged data (here, Anomalo can predict suggested SLAs)
• Validation rules always fail (here, Anomalo will pause alerts if assigned normal priority, but they’ll still be flagged as red inside our UI)
• Key metrics look at too many segments or measures (here, Anomalo can recommend using a single key metric to suppress noise)
• Checks run too often (here, Anomalo can recommend running certain checks at a lower cadence)

To optimize check configurations, teams can set realistic SLAs to avoid premature missing data alerts, convert frequently failing validation rules into key metrics, reduce segment complexity to limit unnecessary notifications, and adjust check frequency to match operational needs.

By setting reasonable expectations and refining configurations, organizations can cut down on unnecessary alerts while preserving data integrity.

Step 4: Be Realistic About Time Investments for Triage

Reducing alerts is only half the battle. Teams must also have a structured approach to processing the alerts they receive.

• Develop a triage strategy: Prioritize certain types of alerts, such as those related to pipeline issues or business-critical data.
• Refine notification destinations and custom alert management: Ensure that alerts are actionable, rather than overwhelming.
• Assess alert efficiency periodically: Teams should continuously refine their triage approach to improve response times and reduce alert fatigue.

An effective triage system ensures that alerts drive action rather than becoming a source of frustration. 

Introducing the Anomalo Alert Audit Checklist

We’ve gone through four major steps in setting up a quality alert structure, but there’s a world of optimization out there once you have your foundational system in place. As you explore further, it’s worth keeping some big ideas in mind.

Alerts should work for you, not against you. Any alert can be technically useful (alerting you to a data quality issue), but the best alerts are useful enough to be worth the triage time and contribution to alert volume. Inefficiencies in alert management can take time to surface, so proactive management is good practice.

The following alert audit checklist is a great place to come back to every once in a while. Asking these questions about your setup can expose inefficiencies earlier and help make your alert management more intentional.

• Does my default notification destination make sense?
• Is everyone who needs to receive an alert successfully receiving that alert?
• Is anyone receiving alerts they don’t need to receive?
• Are my checks set to the lowest workable priority level?
• Do I have SLAs set up correctly in Anomalo for tables with lagged data?
• Do I have any validation rules that should be key metrics instead?
• Are my segment metrics set up for success, with narrow scopes and anomaly validation?
• Do my hourly checks need to run hourly?
• Does my team have a strategy for prioritizing alerts?

How Anomalo Manages Alert Fatigue for Enterprise Data Teams

Managing alert fatigue is an ongoing process that requires proactive monitoring and refinement. The best alerts are those that deliver valuable insights without overwhelming teams. By fine-tuning alert destinations, prioritizing notifications, setting realistic alert volumes, and optimizing triage workflows, data governance leaders can ensure that alerts work for them, not against them. Anomalo offers a comprehensive solution to this challenge, ensuring that your data quality alerts are both actionable and efficient.

1. Intelligent Alert Routing

Anomalo’s customizable alert destinations ensure that notifications reach the appropriate teams, minimizing noise and enhancing relevance. For instance, engineering teams receive alerts pertinent to their functions, while finance teams are notified of issues affecting their data. This targeted approach reduces unnecessary distractions and ensures timely responses to critical data quality issues.

2. Prioritization of Alerts

Not all data quality issues carry the same weight. Anomalo allows users to set priority levels for different checks:

• Low Priority: Failures are logged without sending alerts, suitable for non-critical data.
• Normal Priority: Alerts are sent initially but reduce in frequency after consecutive failures, preventing alert fatigue.
• High Priority: Every failure triggers an alert, ensuring immediate attention to vital data issues.

This tiered system ensures that teams focus on the most impactful data quality concerns.

3. Automated Anomaly Detection

Anomalo employs unsupervised machine learning to monitor data continuously, identifying anomalies without the need for manual rule-setting. This proactive approach detects issues such as missing data, unexpected values, or deviations from historical patterns, allowing teams to address problems before they escalate. Here, Anomalo utilizes layers of unsupervised machine learning checks to ensure that alerts are infrequent and meaningful, while maintaining coverage on every column of a data table.

4. Root Cause Analysis and Resolution

Upon detecting an anomaly, Anomalo provides automated root cause analysis (RCA) on failing checks, highlighting the likely source of the problem. This feature accelerates troubleshooting by offering:

• Samples and Visualizations: Clear representations of the data issue for quick understanding.
• Ticketing Integrations: Seamless creation of tickets in JIRA to manage issue resolution workflows.

These tools streamline the resolution process, reducing the time and effort required to maintain data quality.

5. Scalability and Integration

Designed for enterprise environments, Anomalo scales effortlessly to monitor thousands of tables and billions of records. Its efficient scheduling and bulk configuration capabilities ensure cost-effective monitoring. Moreover, Anomalo integrates seamlessly with data catalogs, ETL tools, and data warehouses within your existing data infrastructure.

What’s next for Data Governance Visionaries?

Alert fatigue can undermine the effectiveness of data quality initiatives. Anomalo addresses this challenge by delivering intelligent, prioritized, and actionable alerts, supported by advanced analytics and seamless integrations. 

In the coming weeks, stay tuned as we continue to explore topics like building data stewardship programs, automating data quality checks, and measuring the ROI of governance initiatives. Ready to transform your data governance strategy? Let’s talk! 

Learn more about how Anomalo’s machine learning approach to data quality can help you identify data issues before they become problems.