Engineering Intelligence Platforms | Definition, Benefits, Tools

A new category of tooling known as Engineering Intelligence has emerged to provide a metrics-driven approach to understanding efficiency in your software development lifecycle. Learn more about what this new category is, where it came from, and what tools can help you turn insight into action.

December 13, 2023

In modern engineering teams, lack of comprehensive data visibility can be a major problem. Without full insights into workflows, projects, and processes, issues can arise that create bottlenecks, increase costs, and hamper team productivity. 

Organizations have long measured company and business metrics, but now leaders want to understand how the engineering teams work from a data- and metrics-oriented approach. An emerging category of tooling known as Engineering Intelligence offers a solution by enabling the tracking of essential engineering metrics so teams can identify issues as they emerge and recalibrate accordingly, as well as find areas for improvement over time. 

What is the Engineering Intelligence category?

According to Gartner, Software Engineering Intelligence Platforms provide “data-driven visibility into the engineering team's use of time and resources, operational effectiveness, and deliverable progress.” This helps organizations recalibrate workflows, resource allocation, and decision making to improve overall engineering productivity. Using an Engineering Intelligence tool, teams have the data needed to continuously learn, adapt, and optimize.

What are the benefits of measuring engineering metrics?

Benefits include:

Fewer bottlenecks: Tracking metrics through an Engineering Intelligence tool helps minimize bottlenecks by providing insights that optimize workload balancing across teams as well as within a team. Organizations ingest and analyze critical data like throughput, workload distribution, and capacity utilization. These analytics then can help identify where engineering resources are over- or under-allocated.

Greater visibility: Improving engineering efficiency relies on a comprehensive understanding of the current state of existing systems. By aggregating metrics, logs, and metadata from distributed systems, leaders get an accessible and unified picture. Clearly defined metrics enable engineering teams to understand their own performance and processes, instead of using anecdotal information. The data also make trends intuitive and accessible to non-technical users, allowing business operations to understand more about engineering processes and pain points.

Meaningful context: When engineering intelligence tools are integrated with data that shows the state of software quality, organizations can get a better view of which metrics are or aren’t positively correlated with outcomes like reduced incidents, faster time to recovery, etc. Focus on outcomes not output.

How data improves engineering efficiency, velocity and quality 

By gathering and analyzing information from engineering workflows, organizations can derive data-driven prescriptive guidance for engineering best practices. After identifying pain points and areas for improvement, engineering teams and leaders can use the information to change processes to make them more effective, by removing obstacles and cumbersome steps as well as adding tools, headcount, and process improvements. These changes allow organizations to ultimately accelerate delivery speed, improve code quality, and streamline the developer experience.

Studying engineering productivity means assessing how well engineers are enabled to deliver value, rather than evaluating individual performance. It is important not to conflate productivity with engineer output metrics. Instead, the focus is on optimizing the broader software development lifecycle and processes that surround engineers.

For example, we can examine how introducing a new commit approval process impacts the efficiency of engineers in developing and deploying code. The goal is to uncover friction points that slow velocity and output. Do the builds take too long to compile? Does the deployment process require lots of hands on attention from an engineer? Enhancing productivity allows teams to ship quality features faster without requiring longer hours or expanded headcount.

Additionally, an engineering leader could analyze how the level of technical debt for a team correlates with their feature delivery lead times over multiple sprints. High amounts of accumulated technical debt are often a friction point that makes further development sluggish and bug prone. Are engineers consistently rewriting parts of the code? Does the current system create slow and tedious work? Understanding this relationship could show the benefits of allocating dedicated resources for paying down technical debt.

The intent behind engineering productivity improvements should be hiring and empowering more engineers rather than reducing roles. With smoother processes, automation, and waste reduction, existing teams can increase throughput on priority initiatives. When evaluating engineering productivity, focus on delivery metrics like lead time and deployment frequency. These reflect real outcomes rather than vanity metrics like lines of code.

What is an Engineering Intelligence tool?

This new category of software includes products that collect and aggregate metrics to provide insights, recommendations, and automation to improve engineering workflows and outcomes. These tools consolidate efforts to establish data-driven processes across the engineering organization.

Engineering Intelligence platforms ingest metrics, events, and metadata created across the entire software delivery system, from work item trackers, to CI/CD pipelines, to incident management platforms. Advanced integrations and data pipelines collect these signals, like code commits and deployment failure rates, and map and store them in a centralized data storage built for data analysis. Consolidating the information allows for and aids in more holistic analysis. 

An Engineering Intelligence tool can analyze the data and provide visualizations that help answer questions around engineering productivity and systems such as:

  • How are code quality trends driving downstream system reliability?
  • Which teams deliver features most rapidly from concept to production?
  • How do codebase dependencies influence release cadence?

The result is an intelligent model of how work moves through the engineering organization. This enables both technical and non-technical leaders to spot potential misalignments, make data-driven decisions on improvements, and ensure activities ladder up to business outcomes.

Engineering Intelligence tools empower organizations to evaluate processes and adapt as one interconnected system.

What to look for in an Engineering Intelligence tool

With engineering teams having diverse workflows, processes, and objectives, finding a one-size-fits-all solution can be difficult. When choosing an Engineering Intelligence tool, consider the following features:

Ability to track important metrics: Essential to any Engineering Intelligence tool is its ability to collect the metrics needed to provide insight into your organization and developer productivity. You may want to track DORA metrics or other essential metrics like deployment frequency, lead time, change failure rate, and availability. Ensure the tool you choose captures the data points most critical for your teams. 

Access to critical context: Look for options with integrations to a variety of tools used by your engineering teams like those for deployment, project management, cloud infrastructure, and container orchestration. Prioritize the tools your team already works with, but also consider what platforms your teams may use in the future. Adding integrations with your services allows you to efficiently aggregate data across multiple sources about key components of the engineering process. Moreover, you should prioritize solutions that provide immediate access to aggregate health metrics like those that assess software consistency and reliability, so you can determine which indicators of positive business outcomes actually positively correlate with team-wide metrics of efficiency and productivity.

Ability to create custom improvement plans: If your engineering intelligence solution exists where you track and drive team-wide initiatives, you're more likely to see action as a result of these metrics. But customization is important—not every teammate should strive to minimize PR open to close time in the same way, for example, if they're acting as a mentor, or have several highly complex projects on their plate. Find solutions that let you consider context about teams, teammates, and projects before assigning paths to improvement.

Ease of use: When integrating new tools, ensure they streamline rather than slow down workflows. Difficult setups and complex daily use create friction that hinders engineering productivity, working against the desired goal of improving it. Prioritize solutions that are both intuitive to deploy and simple to incorporate into regular operations. Look for tools engineers can begin utilizing quickly with minimal disruption, providing benefits through automation and insight generation over time.

Quality dashboards: To achieve affect change, teams have to regularly review and incorporate data and insights. Presenting information in a clear format, through well-designed dashboards and clean visualizations, makes the data accessible and digestible. Find a tool that provides high quality dashboards and scorecards out of the box.

Get started with Cortex Eng Intelligence

While standalone Engineering Intelligence tools offer valuable data insights, the most sophisticated solutions combine robust metrics with reports and recommendations to enable meaningful improvements. When deciding how to aggregate data and bring new insight to your team, you may want to consider an internal developer portal (IDP) like Cortex. Cortex and other IDPs not only track and understand data across systems, but also provide a centralized location to manage those systems and create new services that can integrate into your existing ecosystem. 

Data without context doesn’t enable action and improvement alone—it has to be easy to act upon.

Cortex’s new Eng Intelligence module helps teams aggregate data, contextualize that information, and take the right next action. You don’t need a stand-alone Engineering Intelligence tool when the functionality is built into your IDP that developers already use every day. Making metrics visible to the whole team, rather than siloed away in a tool only leadership can access, helps build a shared language around productivity (without finger-pointing). 

For more on how Cortex can help your engineering team make data-driven decisions, download a guide to measuring and improving developer productivity or book a live demo with our team.

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