Dimensional structure
Facts, dimensions, grain, history, and relationships organized around analytical use.
BI & analytics
One governed model for measures people need to share
Design Power BI semantic models, dimensional structures, DAX measures, security, and refresh for reuse and performance.
The problem
When business logic lives independently in reports, spreadsheets, and queries, teams spend more time reconciling numbers than acting on them.
Common signals
When this work becomes useful
- Measures differ across reports
- DAX is difficult to maintain or slow
- Models mix incompatible grains
- Self-service users need a trusted foundation
Concrete output
What the engagement can produce
Governed measures
DAX calculations with clear definitions, formatting, time behavior, and validation.
Security model
Role and row-level access aligned with platform identity and business responsibility.
Performance path
Storage, query, calculation, aggregation, and refresh choices tested against real workloads.
Scope variants
Shape the engagement around the need
Model review
Assess structure, measures, DAX, performance, refresh, security, and reuse.
Model redesign
Refactor or replace a fragmented model while reconciling established outputs.
Enterprise semantic layer
Establish reusable subject-area models and change practices across reporting teams.
Delivery approach
From current state to clear ownership
Understand
Map the business need, current system, owners, constraints, and material failure modes.
Define
Agree on the first useful outcome, delivery boundary, evidence, and responsibilities.
Build
Implement in reviewable increments with validation close to the points where meaning changes.
Establish
Document, release, monitor, and leave the system with clear ownership and next decisions.
Technical context
Platforms and disciplines
- Power BI
- DAX
- Tabular models
- SQL
- Snowflake
- Microsoft Fabric
- Row-level security
Engagement fit
Clear boundaries make better projects.
Often a good fit
- Power BI teams with duplicated measures
- Finance and operations analytics
- Organizations building governed self-service
- Teams improving Power BI on Snowflake
Probably not the right fit
- A measure layer without accountable definitions
- Importing every source table directly into reports
- Performance tuning without representative report workloads
Anonymized proof
Representative reporting work has centered on claims, census, operating, financial, portfolio, and location measures that require explicit grain, definitions, and reconciliation.Explore representative work
Questions
What teams often ask first
Can one model serve every report?
Not always. The goal is appropriate reuse by subject and grain, not one oversized model that makes every use case harder.
Will you document the measures?
Yes. Business definition, calculation behavior, source lineage, owner, and important limitations are part of the model work.
Compare scope, prerequisites, investment guidance, and ownership before deciding.
The packaged engagements and delivery models make the commercial boundary visible without forcing every project into the same shape.