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Data audit
Sources, current state, decision-making maturity, and the analytics gaps. Output: a maturity assessment with prioritized roadmap.
Data your CFO will actually trust.
Most analytics projects produce dashboards nobody opens. Ours produce a data foundation your CFO trusts and your operators use, because the numbers tie out, the lineage is clear, and the definitions are governed.
What the data and BI work covers.
Data Analytics & BI is our solution for organizations that have data but not insight. We build the modern data stack: Snowflake, Databricks, or BigQuery foundations with dbt models, a semantic layer, and the BI tools your operators actually use. The result is one set of numbers everyone trusts, with the lineage to explain any value to anyone who asks.
Where it helps most
Data foundation builders
You’ve outgrown the executive Excel dashboard. Time for a real data platform.
BI consolidation
You have Tableau, Power BI, Looker, and three flavors of “my CSV”, and the numbers don’t agree.
Analytics maturity uplift
You have a data warehouse, but it’s a swamp. We help you bring order to it.
AI/ML enablement
You want to build ML models but the data foundation isn’t ready. We build the foundation first.
What we deliver.
Each item below is a concrete deliverable that we scope, build, and stay accountable for.
Modern data stack
Snowflake, Databricks, BigQuery. dbt for transformations, Fivetran/Airbyte for ingestion, Airflow/Dagster for orchestration.
Semantic layer
dbt Semantic Layer, Cube, Looker LookML. Governed metrics, consistent definitions, defensible numbers.
Source ingestion
Every line-of-business source connected and governed: ERP, CRM, marketing, support, product analytics, and financial systems.
Data modeling
Dimensional and event-based models, slowly-changing dimensions, late-arriving facts. The boring data engineering that determines whether analytics works.
Self-service BI
Tableau, Power BI, Looker, Sigma, or Hex, chosen for the operators who’ll use them.
Data governance
Lineage, access controls, PII tagging, GDPR/CCPA-aware retention, and the data catalog your data team should have.
How an engagement runs.
Five phases, each with outcomes we can measure.
Why work with us.
Numbers tie out
We’re strict about reconciliation: every dashboard ties back to a defensible source.
The semantic layer comes first
We build governed metrics first, dashboards second. Most BI failures are governance failures, not visualization failures.
Engineers, not just analysts
Our data team includes data engineers, not just analytics consultants. We build the platform, and it lasts.
Frequently asked questions.
Still unsure? A 30-minute call with a tech lead answers the rest.
Ask us directlyDo you have a preferred data warehouse?
We’re experienced on Snowflake, Databricks, and BigQuery. We pick based on your existing investments, team skills, and cost profile.
Do you do real-time analytics?
Yes. We use Kafka, Kinesis, and event-driven architectures where the use case needs real time. We don’t over-engineer batch problems with streaming.
What about ML on top of analytics?
Yes; see AI & Data Solutions. The data foundation we build is designed to support ML downstream.
Can you migrate from a legacy DW (SQL Server, Oracle)?
Yes. We’ve done several migrations, including the messy parts: stored procedures, ETL packages, and reports nobody knew were still in use.
How do you handle data quality?
Tests in dbt, freshness SLAs, anomaly detection, and the governance rituals (data review meetings, owner-tagged metrics) that make data quality everyone’s job.
Let’s talk about your project.
Tell us about your team, your stack and the problem you want solved. We’ll come back with how we would approach it.
Response time
Within one business day
Prefer a call?
Headquarters
Charlotte, North Carolina
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