Our data model empowers your team with a drag-and-drop experience in Power BI, streamlining report development for business users.
Data collection is only useful if you can do something with it.
We'll help you transform your data lake into a gold mine of actionable insights.
Leveraging an agile, sprint-based approach, we take your business from raw data into data models to support a fully functioning, validated semantic layer in just a few short weeks.
This semantic layer (think 'business logic') then empowers your team with drag-and-drop analytics and Power BI report creation, making data analysis easier and more reliable than ever.
We view data modeling as the 2nd pillar of an effect BI framework and the key to reliable analysis and reporting.
Brick and Caleb explain how data modeling helps you avoid technical debt and rework.
Blue Margin really helped us get a great data model that we can build on for future reporting. The previous experience they had with other customers allowed us to see some reports that made a lot of sense, so we added them to our scope.
A Dedicated Resource. You get access to a dedicated Senior Dimensional Modeling engineer, and our co-located engineering team with 75+ years of combined experience. Our team works together to tackle your most complex data challenges.
Data Validation and Quality Assurance. From data discovery to model design, business logic definition to reverse engineering, we'll ensure each data model is optimized for high-performing reports and data processing, maximizing compression for speed and efficiency.
Power BI Support. In addition to designing and implementing your data models, we offer Power BI, DAX, and data model support for report developers and business users. We assist with pulling flat files into data models, managing new workspaces and apps, sharing reports, and more.
Private Equity expertise. We have 12 years of experience helping private equity-backed, mid-market companies execute their data strategies.
We're credentialed. We carry Microsoft's Gold Partner distinction in