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Self-serve Data Exploration for Engineering

2 posts · Dec 2022 – Oct 2025

What this trail covers

Hex has evolved its data exploration capabilities by enabling engineering, product, and design teams to explore their own data using Hex. This initiative aims to empower non-data teams to answer their own questions, measure feature performance, investigate incidents, and find information without solely relying on the central data team. The adoption of Hex has led to increased productivity, creative use cases, and improved knowledge sharing, while maintaining data governance through AI agents that leverage well-modeled data, documentation, rules files, semantic layers, and reusable analyses as context.

All Hex trails
  1. Self-serve analytics is finally real (and that's terrifying)This post details how Hex's AI features are enabling true self-serve analytics by empowering non-data teams to answer their own questions. It emphasizes the critical role of context (well-modeled data, documentation, rules files, semantic layers, reusable analyses) in making AI agents effective and trustworthy. The post highlights the shift in the data team's role from gatekeeper to architect of trustworthy systems, enabling broader data usage and focusing data teams on higher-value strategic problems.
  2. Modern Treasury Unlocks Data ExplorationThis post details how Modern Treasury extended the use of Hex to its engineering teams, enabling them to perform data exploration tasks previously handled by the data team. It highlights the challenges engineers faced with traditional SQL runner tabs, such as difficulty in building a repository of analysis results and sharing findings. By providing engineers with Hex, Modern Treasury observed a surge in productivity and the emergence of new use cases, fostering institutional knowledge sharing and mitigating governance concerns through meticulous data governance efforts and dbt integration.
  3. What does it mean to build a self-service analytics infrastructure?This post details how Human Interest uses Hex to build a self-service analytics infrastructure. It explains the concept of self-service analytics, how to achieve it by making tooling easy to find and simple to use, and how Hex specifically enables non-technical stakeholders to access data and model outputs. Examples include operational teams identifying churn risks, finance teams attributing revenue changes, and product managers filtering data independently. It also highlights how Hex abstracts complex Python functionality for analysts, allowing them to up-level their skills.