Data Team Incident Management

Why Incident Management Matters

• Consistency and Reliability in Data Operations - As the GitLab Data team manages critical analytics infrastructure and data pipelines that support business decision-making across the organization, having standardized incident management processes ensures consistent response times and resolution quality. Without clear guidelines, incidents may be handled differently by various team members, leading to inconsistent outcomes and potentially prolonged downtime that impacts data availability for stakeholders.
• Improved Response Times and Accountability - Standardized incident management creates clear escalation paths, defined roles and responsibilities, and established communication protocols. This structure eliminates confusion during high-pressure situations, ensuring that the right people are notified quickly and that response efforts are coordinated effectively. When everyone knows their role and the expected procedures, incidents can be resolved faster with less organizational friction.
• Knowledge Preservation and Continuous Improvement - Formal incident management processes include documentation requirements that capture what went wrong, how it was resolved, and what can be improved. This creates an institutional knowledge base that helps prevent similar incidents in the future and enables new team members to learn from past experiences. The structured approach also facilitates post-incident reviews that drive systematic improvements to data infrastructure and processes.
• Stakeholder Communication and Trust - Clear incident management standards ensure that affected stakeholders receive timely, accurate updates about data availability issues. This transparency builds trust with business users who depend on data for their operations and helps manage expectations during outages. Consistent communication also demonstrates the Data team’s professionalism and commitment to service reliability.
• Compliance and Risk Management - For a data team handling sensitive business information, having documented incident response procedures helps meet compliance requirements and reduces organizational risk. Standardized processes ensure that security considerations are consistently addressed during incidents and that appropriate stakeholders are notified when data integrity or confidentiality may be compromised.

Outcome of Incident Management Mechanism

Faster Problem Resolution

• Reduced downtime for critical data pipelines, ETL/ELT processes, and analytics dashboards
• Clear escalation paths when data quality issues, pipeline failures, or performance degradation occur
• Documented troubleshooting procedures for common issues like failed data loads, schema changes, or API rate limits

Better Data Quality & Reliability

• Proactive monitoring catches data anomalies, missing data, or drift before downstream users are affected
• Root cause analysis helps identify systemic issues in data infrastructure (e.g., recurring transformation failures, source system instabilities)
• Service level objectives (SLOs) for data freshness, completeness, and accuracy become measurable and enforceable

Improved Collaboration

• Clear ownership of data assets and pipelines - knowing exactly who to contact when specific data sources or models break
• Cross-functional coordination between data teams and stakeholders during outages (e.g., notifying analysts when marts are unavailable)
• Shared knowledge base of past incidents helps new team members understand common failure patterns

I. Incident Definition, Severity and Creation

1. Incident Definition

An incident is any anomalous condition that results in—or may lead to—service degradation, data quality issues, or system outages that require immediate human intervention to prevent disruptions or restore operational status.

Incidents may manifest through:

• Availability Issues: Data, dashboards, or analytics tools becoming inaccessible or unavailable to users
• Quality Degradation: Data inaccuracy, corruption, validation failures, or unexpected schema changes
• Timeliness Violations: Data not refreshing within expected timeframes, stale metrics, or delayed reporting
• Processing Failures: Pipeline breakages, ETL/ELT job failures, model errors, or instrumentation logic failures that impact downstream processes
• Security Concerns: Unauthorized data exposure, access control breaches, or data leakage
• Collection Disruptions: Interruption to event tracking, data capture mechanisms, or source system failures

Incident Criteria - not all issues qualify as incidents. An incident must meet one or more of these criteria:

• Whether there is immediate impact on downstream models or dependencies, business operations or data consumers
• Whether there is an SLO breach
• Whether there is immediate action required
• Whether there is potential for permanent data loss or corruption if not addressed immediately

2. Incident Severity

Incident severity is determined by evaluating three key dimensions:

• Business disruption and impact
• Data criticality and impact
• Downstream dependencies

Based on the factors above, we formulate the severity levels below:

• Sev1: Production data pipelines failed, customer-facing teams blocked, critical business decisions cannot be made, data breach/exposure, impending or actual data loss affecting multiple systems/metrics, or moderate to severe degradation in business-critical metrics.
• Sev2: Significant workflow disruption requiring manual workarounds, data pipeline delays impacting downstream consumers, potential security vulnerability/compliance issues, or partial system/service degradation.
• Sev3: Inconvenience but work continues with minimal impact, non-critical data pipeline delays, internal-only data inappropriately accessed, performance degradation in development/staging environments, or minor data quality issues with known workarounds.
• Sev4: Nice-to-have features/improvements missing, cosmetic issues, documentation updates needed, or technical debt items with no immediate operational impact.

When in doubt between two severity levels, choose the higher one initially. Severity can be downgraded or re-classified as more information becomes available and understanding improves.

3. Incident Creation

Once you decide to create an incident, you can follow the following steps:

1. Use the incident template in the appropriate project folder
2. Document all essential information
3. Add incident and sev labels
4. Assign to the right DRI and tag relevant team members
5. Communicate via Slack

Incident VS Issue: Use /type incident to convert issues to incidents when escalation is needed.

Tooling:

Depending on the types of incidents:

• If you have to collaborate with SREs on-call (i.e. in case of Postgres pipeline issues), then use incident.io to log and track incidents. However, there should always be a corresponding Incident within GitLab in our Analytics project (created by data team members).
• For all other types of incidents, create a brand new issue or incident on GitLab here with incident label. You can use /type incident to convert the issue to incident.

Note: For cases when there is minimal impact on data and manual steps or correction is needed, please raise a bug rather than an incident.

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