Answers in weeks, not quarters.
We don't hand over an endless report when the project closes. Every two weeks you get something you can use to decide.
Our own method, in five stages.
- 01
Assessment and baseline
We measure maturity, technology and technical debt to set the starting point.
- 02
Architecture and design
Target operating model, architecture and cases prioritized by return.
- 03
Foundations engineering
Pipelines, dimensional model and a governed semantic layer.
- 04
Activation: data as a product
Models and dashboards as products, with owners and adoption.
- 05
Scale and continuous operations
DataOps and MLOps: monitoring, continuous quality and an expanded roadmap.
Prioritized use cases, not massive projects.
We replace the year-long project with a portfolio of use cases ranked by return. We work in short cycles, and every delivery answers a business problem in a measurable way.
Portfolio ranked by return
Each use case is prioritized by its return and its impact on the financial statements.
Short cycles
Scoped deliveries that reach production, instead of one big launch at the end.
Foundations before models
AI is built on governed data and a managed operation, never the other way around.
How we collaborate
- A 30-minute weekly progress meeting
- A demo every two weeks of what was built
- Direct channel on Teams or Slack
- Shared project board
- Sponsor: sets priorities and removes blockers
- Data owner: validates definitions
- Key users: test and adopt
- Project lead: a single owner from start to finish
- Data engineering: platform, pipelines and quality
- Analytics and AI: models, dashboards and assistants
Always yours
Code in your repository
Everything we build lives in your account, versioned and reviewed.
Documentation
Every model, pipeline and dashboard is documented for your team.
No lock-in
We transfer the knowledge and don't tie you to proprietary licenses.
For long-term programs: a data operating model.
When the goal is to transform the entire data function, the same method is organized into five stages on a common framework.
Strategic foundations
Vision, outcomes and principles for an information-driven organization.
Use cases
Value creation focused on corporate priorities.
Operating model
People and culture, governance and technology for better analytics practice.
Assessment and baseline
We map analytical maturity, assess the current technology landscape and measure technical debt to set the baseline.
- Maturity assessment
- Technology inventory
- Measured technical debt
- Baseline
Architecture and design
We design the target operating model: the cloud architecture, the portfolio of business cases prioritized by return, and the governance frameworks.
- Target operating model
- Cloud architecture
- Cases prioritized by return
- Governance framework
Foundations engineering
We build the foundational infrastructure: critical pipelines, dimensional modeling and governed semantic layers from day one.
- Production pipelines
- Dimensional model
- Governed semantic layer
- Quality tests
Activation: data as a product
We package models and dashboards as products for executives, define data owners and train leaders for adoption.
- Data products
- Executive dashboards
- Owners by domain
- Leadership training
Scale and continuous operations
We move to a managed DataOps and MLOps model: pipeline monitoring, continuous quality and roadmap expansion.
- Pipeline monitoring
- Continuous quality
- Model operations
- Expanded roadmap
Related
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