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Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure Annotate traces to improve LLM quality with Datadog LLM Observability What’s new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis Monitor Aruba Central in Datadog How we centralize and remediate risks with Datadog Case Management Accelerate incident response with Datadog and ServiceNow Monitor your application and network load balancer logs Understanding Karpenter architecture for Kubernetes autoscaling Tools for collecting metrics and logs from Karpenter Monitor Karpenter with Datadog What your product data is actually saying Key metrics for monitoring Karpenter Securing Datadog’s platform in the AI age: The role of observability data Four ways engineering teams use the Datadog MCP Server to power AI agents Approaching your observability migration with the right mindset Meet the new Bits AI SRE: Deeper reasoning, twice as fast Key learnings from the 2026 State of DevSecOps study Use plain English to query your multi-cloud infrastructure in Resource Catalog Simplifying troubleshooting across the user journey with Datadog Synthetic Monitoring Protect your OCI resources with Datadog Cloud Security This Month in Datadog - 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Track engineering metrics with customizable, executive-ready reports in Datadog’s IDP
2025-06-10 · via Datadog | The Monitor blog

Engineering leaders often struggle to answer critical questions about their teams’ performance and the reliability of the services they manage. Without centralized insights, it’s difficult to assess whether standards are being met, track product reliability across teams, or identify gaps in the software development life cycle (SDLC). Scattered metrics and inconsistent reporting make it hard to understand where to focus, make data-informed decisions, and identify top areas for improvement.

To address these challenges, Datadog’s Internal Developer Portal (IDP) provides customizable, ready-to-use Engineering Reports. These pages provide out-of-the-box visibility into engineering reliability, software delivery performance, and compliance with engineering standards, while offering actionable, personalized views for platform engineers, team leads, and executives. Engineering teams can access these insights directly from the IDP overview page and distribute recurring reports via email or Slack.

This post walks through how these new features help teams:

Engineering teams often rely on fragmented dashboards or manual reporting to monitor reliability, making it difficult to understand how services are performing or which areas are at risk. The Reliability Overview report addresses this by providing a side-by-side view of service level objectives (SLOs) and incident data, enabling teams to better understand how SLOs and incidents intersect. Built on data from Datadog SLOs and Incident Management, this unified perspective helps engineering leaders assess whether teams are meeting goals for uptime and availability, and determine whether incidents correlate with SLO breaches or if additional SLOs need to be implemented to prevent incidents.

For example, a manager can quickly see how teams and services are tracking against key user journeys, then drill down into specific SLOs that need improvement. The report also includes a new SLO score, which distills multiple SLOs into a single performance signal for faster decision-making.

Unified view of SLOs by service or team.

Below these metrics, you can see incident trends—such as mean time to resolution (MTTR), impacted services, and severity—for broader insights into how well your teams are responding to issues.

Unified view that shows incident count by team and customer impact duration (in days) by team.

This comprehensive view into team performance provides key insights that help you make critical decisions—such as how to balance reliability work with new feature development, or whether SLOs need to be adjusted—and drive proactive improvements to service health.

Monitor engineering standards with Scorecard adherence

Maintaining consistent engineering practices across teams is a common challenge, especially as organizations grow. The Scorecards Performance report in Datadog’s IDP makes it easy to monitor how well teams are aligning with defined standards. Whether you’re enforcing rules around documentation, observability, or production readiness, this report gives platform teams and engineering managers a structured overview of compliance with these standards.

Scorecards Performance report in Datadog IDP.

Here, you can view Scorecard adherence by team or service, highlighting areas within your organization that may need additional support. You can also see trends over time, which help you track whether improvements are sticking or if further guidance is needed. This level of visibility empowers leaders to set realistic goals, celebrate teams that are excelling, and work collaboratively with those that may be falling behind.

Historical trends by Scorecard in a Scorecards Performance report.

By surfacing these metrics in a single place, the report helps engineering managers understand trends in Scorecard adherence at a glance, without delving into individual Scorecards, so they can make informed decisions about how best to improve engineering health.

Identify delivery bottlenecks with DORA metrics

Speed and stability are key indicators of engineering effectiveness, yet many organizations lack a consistent way to measure them. Building on insights from Datadog DORA Metrics, the DORA Metrics Summary report in Datadog’s IDP brings these KPIs front and center, offering visibility into deployment frequency, lead time for changes, change failure rate, and the time to a restore service. These metrics provide a high-level summary of software delivery performance across services, teams, and environments.

Executives and team leads can compare trends across departments, investigate areas that are underperforming, and identify high-functioning teams whose practices can be replicated. For example, a noticeable spike in lead time may indicate friction in the review process, while low deployment frequency could point to bottlenecks in CI/CD pipelines.

DORA metrics report highlighting trends across services.

This long-term view helps teams evaluate the business impact of delivery initiatives, identify gaps in the SDLC, and continuously iterate on development workflows.

Improve engineering outcomes with actionable insights

With the addition of Engineering Reports, Datadog’s IDP becomes a central source of truth for engineering organizations. These new capabilities make it easier to measure what matters, including product reliability, compliance with software development standards, and delivery performance. Teams gain insight into long-term trends while being able to act on issues as they arise.

To get started, check out the Datadog IDP documentation. If you don’t already have a Datadog account, you can sign up for a 14-day free trial.