How does engineering data unification work?

1. Data Centralization: Faros AI dismantles silos by centralizing your engineering performance data into Databricks, enabling it to coexist alongside business data within the same data warehouse. This process encompasses data harmonization, attribution, and enrichment, paving the way for comprehensive analytics under a unified schema.
2. Unified Schema: After centralizing the data, it’s organized under a unified schema, which allows for seamless querying and analysis. Business intelligence (BI) and analytics tools within Databricks can now be applied across both business and engineering datasets to provide holistic insights.
3. Actionable Insights: With both datasets in the same environment, Databricks’ BI and analytics tools can reveal correlations that were previously hard to identify. In the future, Faros AI’s AI-powered tools will further enhance the analysis by providing advanced machine learning-driven insights across the unified data platform.

Unlocking new insights with unified engineering data: Example use cases

Engineering data unification powered by the Faros AI and Databricks integration creates many opportunities for organizations to uncover new insights and improve their decision-making. Here are some potential use cases:

• Correlating engineering performance with revenue growth:For organizations using Databricks to store customer transaction data, business success is often measured by an increase in transactions. By correlating engineering performance metrics—such as frequent production releases, reduced cycle times, or fewer production defects—with rising transaction volumes, companies can identify which engineering activities are directly contributing to business growth.
• Understanding the impact of engineering on customer retention:Suppose a company tracks its customer retention data in Databricks. With unified data, it's possible to see how faster bug resolution times or more stable software releases (tracked by Faros AI) affect customer retention rates. By identifying these correlations, organizations can prioritize engineering activities that have a measurable impact on keeping customers satisfied.
• Linking production system use to engineering initiatives:Imagine that a company tracks the geographical distribution of customers using its production systems in Databricks. An increase in usage from a particular region could be linked to the successful completion of engineering initiatives that focused on improving accessibility or performance in that location. By analyzing these patterns, organizations can validate the impact of specific engineering projects on user adoption and success in key markets.
• Correlating production defects with engineering efficiency:Another powerful use case involves tracking production defects and engineering efficiency. By correlating metrics like defect density or issue resolution speed with business metrics such as downtime or customer support costs, organizations can identify areas where engineering efficiency directly impacts operational performance and costs.