Data Analytics
From raw data to decisions. We build the data platform, the models, and the reporting surfaces that put trustworthy answers in front of the people who need them, without the dashboard sprawl.
Three things we focus on.
Microsoft Fabric / Synapse
Lakehouse architecture for the workloads that justify it. We design for governance and self-service from day one, not bolted on later.
Power BI that gets used
Ruthless about what becomes a report and what stays a query. Models that reflect how the business actually thinks.
Time-to-insight
Cut the gap between "we should look into this" and "here is the answer" from weeks to days. Often a faster team change than a tech change.
Whatever shape fits the work.
Greenfield or modernization of the data warehouse / lakehouse with governed access patterns.
Audit, consolidate, and rebuild the existing report sprawl into a single trusted Power BI workspace.
Senior data engineer / analyst working alongside your team for a defined window.
What we get asked to do.
- Build a Power BI executive dashboard connected to live operational data
- Modernize a legacy SSRS or Excel reporting environment to Power BI
- Design and deploy a Microsoft Fabric lakehouse for enterprise analytics
- Build a governed self-service analytics workspace with row-level security
- Consolidate and rationalize a sprawling report inventory into a single trusted workspace
- Integrate ERP and CRM data into a unified business intelligence platform
- Build a near-real-time operational dashboard from streaming data sources
- Create a financial close reporting package with automated data refresh
What we bring to data analytics.
Built for the people who use it
Every Power BI deployment we ship is designed around the people who actually open it each week, not the executive who commissioned the dashboard. Adoption is a design requirement, not an afterthought.
Data engineering is in scope
A report is only as good as what feeds it. We own the pipeline, transformation, and data quality work, not just the reporting layer. Most analytics disappointments trace back to a brittle data foundation we would have fixed first.
Fabric where it earns its keep
Microsoft Fabric is powerful for the right workloads. We know which workloads those are and which ones are better served by a simpler approach. We do not propose Fabric migrations to justify the engagement.
Governance designed in from day one
Row-level security, access controls, data classification, and sensitivity labels are architecture decisions, not compliance tasks. We design them in from the first sprint so they do not have to be backfilled after an audit.
What clients typically see.
Ready to talk about data analytics?
Tell us what you are trying to change. We will either be useful, or point you to who would be.