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Data Analytics and Business Intelligence

Decisions you can audit. Dashboards your team actually opens.

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Overview

A modern data stack that survives reorgs.

Most analytics programs start the same way — a pile of dashboards nobody trusts, metric definitions that disagree depending on who built them, and a long backlog of "can you add a filter?" requests. Forrester's research keeps putting unused-report rates around 60%. The cause isn't the BI tool; it's what's beneath it.

We build the modern data stack the way it's meant to work: a governed warehouse, an ELT pipeline with documented contracts, a semantic layer that defines metrics once, and BI on top that's fast, self-serve where appropriate, and traceable from a number on a slide back to the row in the source system. Every engagement starts with the decisions you need to make, then walks back to the data — not the other way around.

We've built data platforms that publish daily retail-shelf intelligence across 60,000+ storefronts and clinical analytics that move on the same shift as the encounter. So we treat "fresh" and "trustable" as both as the bar — not one or the other.

Engagement at a glance

  • 8-week first trustable dashboard
  • One metric, one definition, one owner
  • End-to-end lineage from source to slide
  • Snowflake / BigQuery / Databricks-fluent

2×

Data-leader outperformance (McKinsey)

60%

of reports go unused (Forrester)

8 wks

first dashboard the team trusts

100%

column-level lineage in production

What we deliver

From the warehouse to the boardroom slide

Data Strategy

Working backwards from the decisions the business needs to make to the data and dashboards required to make them. Gartner D&A maturity baseline as the starting point.

Modern Data Warehouse

Snowflake, BigQuery, Redshift, or Databricks SQL — designed for the workloads you have (not the ones the vendor demos with) and modeled in Kimball or data-vault as appropriate.

ELT Pipelines

Fivetran / Airbyte for ingestion, dbt for transformations, tested and versioned. Every model has a contract; every contract has tests; every test runs in CI.

Semantic Layer

Looker (LookML), dbt Semantic Layer, or Cube — so "revenue" means the same thing in BI, in a notebook, and in the LLM-powered chat over your data.

BI & Self-Serve

Tableau, Power BI, Looker, Metabase, Hex. We pick on fit and budget, not vendor preference, and we coach analysts so they own dashboards after we leave.

Governance & Lineage

Cataloging (Atlan, DataHub, Collibra), column-level lineage, access controls, and the data-classification work auditors expect to see for sensitive data.

How we work

A phased, outcome-driven approach

01
Decisions

What needs to be true to decide

02
Metrics

Definitions, owners, SLAs

03
Pipelines

Ingestion + transformation

04
Dashboards

Ship, instrument, iterate

05
Adopt

Training, governance, owners

Stack

Open, swappable, and supplier-portable

Warehouse

Snowflake, BigQuery, Redshift, Databricks

Transformation

dbt, SQLMesh

Ingestion

Fivetran, Airbyte, Stitch, custom

BI

Looker, Tableau, Power BI, Metabase, Hex

Semantic layer

LookML, dbt SL, Cube

Catalog

Atlan, DataHub, Collibra

Orchestration

Airflow, Dagster, Prefect

Reverse ETL

Hightouch, Census

Outcomes

What good looks like

Time-to-insight

From days to minutes

Adoption

WAUs / decisions made

Data quality

Test coverage & freshness

Cost per insight

Right-sized warehouse spend

FAQ

Common questions

Use a managed service. Snowflake, BigQuery, Databricks SQL, and Redshift Serverless all give you elastic compute and separation of storage and compute for less than the cost of running, patching, and scaling a self-hosted alternative. The interesting work is upstream (modeling) and downstream (consumption), not in the storage engine.

As soon as more than one person writes SQL against the warehouse. dbt gives you versioning, testing, lineage, and documentation — none of which are extras when you're trying to keep numbers consistent. The version of "we'll add tests later" we've never seen actually result in tests later.

Yes, when the semantic layer is curated and the tool is matched to the audience. A finance analyst can self-serve in Looker; a sales rep needs a pre-built embedded view. We design the boundary so consumers see what they need and analysts have the freedom they need, without the data team becoming a ticket queue.

On top of a clean semantic layer — never directly against raw warehouse tables. Text-to-SQL hallucinates without governance. We wire LLM agents to your semantic layer's metrics and dimensions so questions get answered against definitions someone has already validated, with the SQL surfaced for auditability.

Industries we apply this in

Other services that often pair with this

See all services

Looking for one number everyone can agree on?

Tell us the three decisions you wish you had cleaner data for. We'll show you the shortest path to a dashboard the team actually opens.