Overview
Most organizations have more data than they know what to do with — and dashboards that nobody trusts. Elytic Labs builds analytics foundations and BI implementations that close the gap between raw data and the decisions that actually run the business. That means clean models, reliable metrics definitions, and reports that stakeholders open because they're useful — not because someone made them.
Evan Watson approaches analytics engineering the same way he approaches software: with rigor around data quality, version-controlled transformation logic, and a strong bias toward simplicity. A dashboard that answers three questions clearly is worth more than one with forty charts and no single source of truth.
What's included
- Analytics strategy & tooling assessment — evaluating your current stack, identifying gaps, and recommending the right combination of warehouse, transformation, and visualization tools for your team's size and maturity
- Data modeling & transformation — dbt project design and build-out, dimensional modeling, slowly changing dimensions (SCDs), and business logic codified in version-controlled SQL
- Metrics layer design — defining a single source of truth for key business metrics (revenue, churn, activation, retention) so every team is working from the same numbers
- BI tool implementation & dashboard build — Looker, Metabase, Tableau, or Power BI — from initial setup and semantic layer configuration through to delivered, production-ready dashboards
- Self-serve analytics enablement — structuring your data models and BI environment so non-technical stakeholders can explore data safely without breaking things or producing wrong answers
- Data quality & testing framework — dbt tests, source freshness monitoring, anomaly detection, and alerting so bad data gets caught before it reaches a report
- Documentation & training — data dictionary, metric definitions, and a handoff session so your team can maintain and extend the analytics layer independently
Technologies & tools
dbt · Looker · Metabase · Tableau · Power BI · BigQuery · Snowflake · Redshift · PostgreSQL · Apache Spark · Airflow · Python · SQL · LookML · Git
Ideal for
- Growth-stage companies whose reporting still lives in spreadsheets stitched together by one overworked analyst
- Data teams spending more time answering "where does this number come from?" than actually doing analysis
- Organizations that have a data warehouse but no transformation layer or consistent modeling conventions
- Product and finance teams who don't trust their dashboards and have stopped using them
- Companies onboarding a new BI tool and needing a senior practitioner to implement it correctly from the start
On metrics alignment
The most common analytics failure isn't technical — it's definitional. Elytic Labs begins every analytics engagement with a metrics alignment workshop: getting Finance, Product, and Operations in a room to agree on how key terms are defined before any models are built.