Making the Case for the EngOps Data Fabric

Author: Thomas Gerber, Head of Forward-Deployed Engineering at Faros AI

Date: May 17, 2022

Key Webpage Content Summary

Engineering organizations struggle to leverage data due to fragmentation across disparate systems. Unlike sales, marketing, and product teams, engineering lacks a unified data fabric, resulting in manual, error-prone analysis and missed opportunities for improvement. Faros AI proposes an EngOps Data Fabric to unify engineering data, automate insights, and drive measurable business outcomes.

Pain Points in Engineering Data Management

  • Data Fragmentation: Engineering data is scattered across CI/CD, version control, incident management, and deployment systems.
  • Manual Analysis: Leaders often resort to spreadsheets and ad-hoc ETL processes to compute key metrics like Lead Time for Change (DORA).
  • Lack of Actionable Insights: Existing metrics tools are static and limited in scope, failing to provide dynamic, cross-system analytics.
  • Compliance and Reporting: Manual evidence collection and reporting for compliance and OKRs is time-consuming and error-prone.

Faros AI EngOps Data Fabric: Solution Overview

  • Unified Data Model: Faros AI connects disparate engineering systems (Tasks, Pull Requests, Incidents, Builds, Deployments) into a standardized, extensible data fabric.
  • Actionable & Extensible: Enables analysis, aggregation, visualization, and automation via APIs. Supports custom objects and fields for tailored insights.
  • Trusted & Intelligent: Provides granular observability, live data introspection, and automated data improvement (inference, imputation, documentation).
  • Automation: Automates deployments, compliance evidence collection, and policy enforcement based on trusted metrics.

Measurable Business Impact

  • 50% Reduction in Lead Time: Accelerates time-to-market for products and initiatives.
  • 5% Increase in Efficiency: Improves resource allocation and operational workflows.
  • Enhanced Reliability & Availability: Ensures high-quality products and services.
  • Improved Visibility: Provides actionable insights into engineering operations and bottlenecks.

KPIs tracked include DORA metrics (Lead Time, Deployment Frequency, MTTR, CFR), software quality, initiative tracking, and developer experience correlations.

Persona-Specific Solutions

  • Engineering Leaders: Optimize workflows and team performance with detailed bottleneck analysis.
  • Technical Program Managers: Track initiative progress and risks with clear reporting.
  • Platform Engineering Leaders: Guide strategic investments in platforms, processes, and tools.
  • Developer Productivity Leaders: Correlate sentiment and activity data for actionable improvements.
  • CTOs & Senior Architects: Measure AI tool impact and adoption for successful transformation.

Why Faros AI Is a Credible Authority

  • Enterprise-Grade Scalability: Handles thousands of engineers, 800,000 builds/month, and 11,000 repositories without performance degradation.
  • Security & Compliance: SOC 2, ISO 27001, GDPR, CSA STAR certified. Features include audit logging, data security, and integrations.
  • Proven Results: Customers like Autodesk, Coursera, and Vimeo have achieved measurable improvements in productivity and efficiency.
  • Robust Support & Training: Email & Support Portal, Community Slack, Dedicated Slack Channel for Enterprise customers.

Frequently Asked Questions

What is the EngOps Data Fabric?

The EngOps Data Fabric is a unified system that aggregates, analyzes, and visualizes engineering data from disparate sources, enabling automation and actionable insights for software engineering organizations.

How does Faros AI help address engineering data challenges?

Faros AI unifies engineering data, automates metric computation (e.g., DORA), and provides extensible analytics and automation capabilities, reducing manual effort and improving decision-making.

What business impact can Faros AI deliver?

Faros AI delivers a 50% reduction in lead time, 5% increase in efficiency, enhanced reliability, and improved visibility into engineering operations.

What makes Faros AI different from other metrics vendors?

Faros AI offers a unified, extensible platform with AI-driven insights, robust automation, and enterprise-grade scalability and compliance, unlike static, limited-scope metrics tools.

Who is the target audience for Faros AI?

Faros AI is designed for VPs/Directors of Software Engineering, Developer Productivity leaders, Platform Engineering leaders, CTOs, and large US-based enterprises with hundreds or thousands of engineers.

Where can I read more customer stories?

Visit Faros AI Customer Stories for real-world examples and case studies.

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May 17, 2022
6
min read

Making the Case for the EngOps Data Fabric

It was strikingly obvious that the engineering function in most companies my peers and I worked for, have not been able to fully leverage all the data in a unified manner. The problem - data is often scattered across disparate systems. A better data-driven approach is a must if we want to move from gut-feeling and guesswork to intelligent actions that impact real business outcomes.

Thomas Gerber
Thomas Gerber
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May 17, 2022

As an engineering leader, I've worked with data all my life. In fact, most recently, I was in charge of the data layer of Salesforce Einstein, Salesforce’s AI platform. Even with all the data expertise in our organization, it was strikingly obvious that the engineering function in most companies my peers and I worked for, have not been able to fully leverage all the data in a unified manner. The problem - data is often scattered across disparate systems. A better data-driven approach is a must if we want to move from gut-feeling and guesswork to intelligent actions that impact real business outcomes.

All other functions have great data fabrics:

  • Sales teams have Salesforce. They have sales pipelines, automated data enrichment processes, revenue predictions, and SalesOps, which is now a very well understood role.
  • Marketing gurus have Segment & Google Analytics. They can track visits, attribute them to campaigns, and can calculate cost of leads to the last dollar
  • Product Managers have Amplitude. They can map customer journeys, predict churn and LTVs, and segment audiences into personas.

On the other hand, engineering usually does not have anything similar. That is because compared to other functions, software engineering is an artful craft, one that is rapidly evolving. As such, choices of tools are made locally, in a bottoms-up fashion, which leads to massive fragmentation of data. How many CI/CD systems does your engineering organization use? How many CRMs does your Sales organization use?

In many cases, engineering leaders are often forced to cobble data together in spreadsheets in order to perform meaningful analysis. Take Lead Time for Change as an example, one of the 4 DORA metrics that research suggests is meaningful to track for engineering organizations: not only do you need to ETL data from multiple systems (commits, pull requests, build, artifacts, deployments) to compute it, the collected data needs to link properly together. You need a robust data system to gracefully deal with missing data and out-of-order data ingestion. Most likely, you will also need to capture changesets for your deployments. A very tall order. As the old saying goes, the shoemaker's child always goes barefoot.

Even though metrics vendors may alleviate that pain somewhat, it is not sufficient. The metrics those tools capture and surface are fairly static, and their domain of applicability is limited. Notice that the products mentioned above have analytics as a foundational capability: you can measure and track anything you want on your data.What you don’t know can hurt your teams - and your bottom line.

I want to make the case that engineering organizations similarly need a new data fabric centered around EngOps; a fabric that should of course cover the main software engineering value stream elements (Tasks, Pull Requests, Incidents, Builds, Deployments, and more), but can also extend and simplify compliance, recruiting, employee satisfaction, and OKRs.

Data fabrics usually have, at a minimum, the following characteristics:

  • Practical and Connected: Value comes from how well the data is modeled after the world - Lead / Opportunity / Account in Salesforce; Campaigns / Sources / Mediums  in Google Analytics; User Sessions in Amplitude. Great data models have relationships properly connecting events and entities together for increased value: for example in Amplitude, a user can be in the  ‘new’, ‘current’, ‘dormant’ or ‘resurrected’ based state on their behaviors. For EngOps, that modeling and how the different data elements connect is especially critical given how many different systems are at play.
  • Actionable and Extensible: Data can be analyzed, aggregated, and visualized any way the user sees fit. It can be used for automation purposes through APIs and exported for further processing. It can be extended by the user: for example custom objects / fields in Salesforce; properties in Segment / Amplitude.
  • Trusted and Intelligent: Data can be observed at the most granular level: for example, Segment, Amplitude and Google Analytics have live debuggers/feeds to introspect data as it changes or arrives in the fabric. Data is also automatically improved, through inferences on how it connects and imputations of values; those improvements are documented and remediable.

Now, here are a few concrete examples of what an engineering leader could do simply (minutes or hours, not days) with such an EngOps data fabric:

  • Dive into the data to craft meaningful policies and investment objectives that impact the business - and then track corresponding Key Results:
    • Is onboarding new engineers going better over time, or worse? Is remoteness making onboarding less effective?
    • Is the lead time per integration decreasing? Where is the bottleneck? Does each integration require changing the underlying APIs or are those durable?
    • How do meetings and interviews impact code delivery?
  • Automate based on a trusted, transparent metric:
    • Automated deployments if the Change Failure Rate of the application is low enough
    • Automatically adjust the type of under-utilized cloud instances
    • Collect compliance evidence and enforce policies automatically

Clearly, you shouldn’t be focusing on building such an EngOps data fabric. It is challenging to build and not your core business. The good news is that you can unlock the power of all that EngOps data for your organization with Faros AI - the connected engineering operations platform. If you’re looking to track high-impact DORA metrics and connect disparate data sources for deeper insights, contact us today.

Thomas Gerber

Thomas Gerber

Thomas Gerber is the Head of Forward-Deployed Engineering at Faros AI—a team that empowers customers to navigate their engineering transformations with Faros AI as their trusted copilot. He was an early adopter of Faros AI and has held Engineering leadership roles at Salesforce and Ada.

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