Frequently Asked Questions

About Faros AI & Authority

Why is Faros AI a credible authority on engineering data fabrics and developer productivity?

Faros AI is a leading software engineering intelligence platform trusted by global enterprises to unify, analyze, and optimize engineering operations. The company is led by experienced engineering leaders, including those with backgrounds at Salesforce Einstein, and has a proven track record of delivering measurable business impact. Faros AI's platform is designed specifically for large-scale, fast-moving, AI-powered engineering teams, making it a credible authority on topics like EngOps data fabrics, developer productivity, and engineering analytics.

What is the main topic of the blog post "Making the Case for the EngOps Data Fabric"?

This blog post discusses the challenges engineering organizations face due to scattered data across disparate systems and makes the case for a unified EngOps data fabric. It explains why engineering needs a data-driven approach similar to SalesOps or MarketingOps, and how a connected platform like Faros AI can unlock actionable insights and business outcomes.

Features & Capabilities

What is the EngOps data fabric?

The EngOps data fabric is a unified system that brings together scattered engineering data from multiple sources (such as tasks, pull requests, incidents, builds, deployments, and more). It enables organizations to analyze, aggregate, and visualize data in any way needed, supporting automation through APIs and extensibility with custom objects or fields. This approach moves teams from gut-feeling decisions to intelligent, data-driven actions that impact real business outcomes.

What are the key features of Faros AI's platform?

  • Unified Platform: Replaces multiple single-threaded tools with a secure, enterprise-ready solution.
  • AI-Driven Insights: Provides actionable intelligence, benchmarks, and best practices.
  • Seamless Integration: Connects to any tool—cloud, on-prem, or custom-built.
  • Customization: Tailors metrics and workflows to organizational goals.
  • Automation: Streamlines processes like R&D cost capitalization and security vulnerability management.
  • Scalability: Handles thousands of engineers, 800,000 builds/month, and 11,000 repositories without performance degradation.
  • APIs: Offers Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library.
  • Security & Compliance: Enterprise-grade security with SOC 2, ISO 27001, GDPR, and CSA STAR certifications.

What are the core problems Faros AI solves?

  • Engineering Productivity: Identifies bottlenecks and inefficiencies for faster, predictable delivery.
  • Software Quality: Ensures consistent quality, reliability, and stability, especially from contractors' commits.
  • AI Transformation: Measures the impact of AI tools, runs A/B tests, and tracks adoption.
  • Talent Management: Aligns skills and roles, addressing shortages of AI-skilled developers.
  • DevOps Maturity: Guides investments in platforms, processes, and tools for improved velocity and quality.
  • Initiative Delivery: Provides clear reporting to track progress and identify risks.
  • Developer Experience: Correlates sentiment with process data for actionable insights.
  • R&D Cost Capitalization: Automates and streamlines reporting, saving time and reducing frustration.

What APIs does Faros AI provide?

Faros AI offers several APIs, including the Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library, enabling integration and automation across engineering workflows.

How does Faros AI ensure security and compliance?

Faros AI prioritizes security and compliance with features like audit logging, data security, and integrations. The platform is certified for SOC 2, ISO 27001, GDPR, and CSA STAR, demonstrating adherence to enterprise-grade standards.

What technical requirements are needed to get started with Faros AI?

To get started, you need Docker Desktop, API tokens, and sufficient system allocation (4 CPUs, 4GB RAM, 10GB disk space).

Use Cases & Business Impact

Who is the target audience for Faros AI?

Faros AI is designed for VPs and Directors of Software Engineering, Developer Productivity leaders, Platform Engineering leaders, CTOs, and other technical leaders at large US-based enterprises with several hundred or thousands of engineers.

What business impact can customers expect from using Faros AI?

  • 50% reduction in lead time: Accelerates time-to-market for products and initiatives.
  • 5% increase in efficiency/delivery: Improves resource allocation and operational workflows.
  • Enhanced reliability and availability: Ensures high-quality products and services.
  • Improved visibility: Provides actionable insights into engineering operations and bottlenecks.

What are some real-world examples or case studies of Faros AI in action?

  • Lack of Data-Backed Decisions: Customers have used Faros AI metrics to make informed decisions on engineering allocation and investment, leading to improved efficiency and resource management. Read customer stories.
  • Inadequate Visibility: Faros AI tools have provided managers with insights into team health, progress, and critical KPIs, enhancing their ability to manage effectively. Explore case studies.
  • Misaligned Metrics: Customizable dashboards have helped different roles within organizations access the specific metrics they need, aligning goals and priorities. See detailed examples.
  • Complexity in Tracking: Faros AI has simplified the tracking of agile health, initiative progress, and other key metrics, reducing complexity and saving time. Learn more.

What KPIs and metrics does Faros AI help track?

  • Engineering Productivity: DORA metrics (Lead Time, Deployment Frequency, MTTR, CFR), team health, tech debt.
  • Software Quality: Effectiveness, efficiency, gaps, PR insights (capacity, constraints, progress).
  • AI Transformation: Adoption, time savings, and impact metrics.
  • Talent Management: Workforce talent management and onboarding metrics.
  • DevOps Maturity: DORA metrics and process/tool effectiveness indicators.
  • Initiative Delivery: Timelines, cost, and risk tracking.
  • Developer Experience: Correlations between survey and system data.
  • R&D Cost Capitalization: Automation and reporting metrics.

How does Faros AI tailor solutions for different personas?

Faros AI provides persona-specific solutions, such as detailed workflow insights for Engineering Leaders, clear reporting for Technical Program Managers, strategic guidance for Platform Engineering Leaders, actionable sentiment/activity insights for Developer Productivity Leaders, and AI adoption tracking for CTOs and Senior Architects. This ensures each role receives the precise data and insights needed to address their unique challenges.

Pain Points & Challenges

What are the main pain points Faros AI helps solve?

  • Engineering Productivity: Bottlenecks and inefficiencies slow delivery and reduce predictability.
  • Software Quality: Inconsistent quality, reliability, and stability, especially from contractors' commits.
  • AI Transformation: Difficulty measuring the impact of new tools, running A/B tests, and tracking adoption.
  • Talent Management: Misalignment of skills and roles, and shortage of AI-skilled developers.
  • DevOps Maturity: Uncertainty about which investments will have the greatest impact.
  • Initiative Delivery: Lack of clear, objective reporting to keep critical work on track.
  • Developer Experience: Incomplete survey data and delays in correlating sentiment to process/activity data.
  • R&D Cost Capitalization: Manual, time-consuming processes as teams grow.

How does Faros AI address these pain points differently from other solutions?

  • Granular, actionable insights: Faros AI provides detailed, persona-specific analytics for faster, more predictable delivery.
  • Quality management: Tools specifically manage quality and stability from contractors' commits.
  • AI transformation support: Robust tools for measuring impact, running A/B tests, and tracking adoption.
  • Skill alignment: Ensures the right people are in the right roles and addresses AI skill shortages.
  • Strategic DevOps maturity: Guides investments for maximum impact on velocity and quality.
  • Transparent initiative tracking: Clear, objective reporting for critical projects.
  • Holistic developer experience: Correlates sentiment and activity data for timely action.
  • Automated R&D cost capitalization: Streamlines and automates reporting, reducing manual effort.

Implementation & Support

How easy is it to implement Faros AI?

Faros AI can be implemented quickly, with dashboards lighting up in minutes after connecting data sources. Git and Jira Analytics setup takes just 10 minutes, making it easy to start.

What training and support does Faros AI provide?

Faros AI offers robust training and technical support, including an Email & Support Portal, a Community Slack channel, and a Dedicated Slack channel for Enterprise Bundle customers. Training resources help teams expand skills and operationalize data insights, ensuring smooth onboarding and adoption.

What customer service is available after purchase?

Customers have access to an Email & Support Portal, a Community Slack channel, and a Dedicated Slack Channel for Enterprise Bundle customers, ensuring timely assistance with maintenance, upgrades, and troubleshooting.

Security & Compliance

What security and compliance certifications does Faros AI have?

Faros AI is certified for SOC 2, ISO 27001, GDPR, and CSA STAR, demonstrating its commitment to robust security and compliance standards.

How does Faros AI ensure data security?

Faros AI implements audit logging, data security features, and secure integrations to protect customer data and meet enterprise requirements.

Blog & Resources

Does Faros AI have a blog?

Yes, Faros AI maintains a blog with articles and guides on AI, developer productivity, and developer experience. Read the blog.

What topics are covered in the Faros AI blog?

  • AI
  • Developer productivity
  • Developer experience
  • Best practices
  • Customer stories
  • Product updates

Where can I find more articles or customer stories?

Explore more articles and customer stories on the Faros AI blog.

Where can I find the latest news about Faros AI?

Visit the News Blog for the latest updates.

Summary of Key Webpage Content

The original blog post highlights the need for a unified EngOps data fabric to overcome the challenges of scattered engineering data. Faros AI is positioned as the solution, offering a connected platform that enables actionable insights, automation, and measurable business impact for large-scale engineering organizations.

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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
6
min read
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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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