1. Initial Consultation and Requirement Gathering
Objective: Establish a clear understanding of the client's business goals and data needs.
We begin with a kick-off meeting to understand your business, your pain points, and what you're actually trying to achieve — not just what you think you need built. From there, we conduct in-depth interviews with key stakeholders: the people who use the data, the people who make decisions from it, and the people who maintain it.
We also run an initial data assessment. What sources exist? What's the current state of the infrastructure? Is anything actually working, or is the whole thing held together with scheduled exports and spreadsheets?
From all of this comes a requirement document that defines the project scope: specific objectives, deliverables, timelines, and what's explicitly out of scope. That last part matters as much as the first.
Outcome: A shared blueprint that everyone — client and delivery team — has signed off on before work begins.
2. Data Strategy and Roadmap Development
Objective: Build a plan that connects your data work to your business goals, not just your technical backlog.
We run a focused strategy workshop — usually one or two sessions — to align on priorities. Where are the biggest gaps? What needs to exist in 30 days versus 6 months? What does the team need to be able to maintain after we hand things over?
From that, we produce a roadmap with short-term and long-term milestones, and a technology stack recommendation. The stack is chosen for your context: your team size, your existing infrastructure, your budget, and your growth trajectory. We're not going to recommend Databricks to a 10-person SaaS company that just needs reliable reporting.
Outcome: A clear strategy and roadmap that the whole business can understand, not just the data team.
3. Data Collection and Integration
Objective: Get data from everywhere it lives into one place you can trust.
We start by identifying every relevant data source — internal databases, SaaS tools, third-party APIs, flat files, whatever exists. Then we build the pipelines to move it.
In most modern stacks, we use ELT rather than ETL. Tools like Fivetran or Airbyte handle extraction and loading — getting raw data into your warehouse intact. Transformation happens inside the warehouse using dbt. This keeps your raw data preserved, your transformation logic version-controlled, and your pipeline auditable. If something breaks downstream, you can trace it back.
We also handle data cleansing at this stage: removing duplicates, resolving inconsistencies, and flagging quality issues before they become someone else's problem three months later.
Outcome: A unified, clean, and reliable dataset in your warehouse, ready for modeling.
4. Data Modeling and Transformation
Objective: Turn raw data into structures that answer real business questions.
This is where most projects either earn trust or lose it. Good data models are the reason a dashboard number means something. Bad ones are why every meeting starts with "which report is right?"
We build models that represent your actual business logic — not just what the source system spits out. That means defining entities, relationships, and aggregation rules in a way that reflects how your business actually works. We use dbt for this: models are written in SQL, tested, documented, and stored in version control.
We validate everything before moving on. Row counts, referential integrity, null checks, business logic tests — all codified in dbt tests so they run on every future pipeline execution, not just once during development.
Outcome: Well-structured, tested, and documented data models that your team can build on — and trust.
5. Data Analysis and Visualization
Objective: Surface the insights that actually drive decisions.
Before we build a single dashboard, we do exploratory analysis. What does the data actually show? Are there trends, anomalies, or patterns that change what we build? This step saves clients from investing in dashboards that answer the wrong questions.
Then we build. Depending on your stack and use case, we work with Sigma Computing, Apache Superset, or Metabase for internal dashboards. For embedded analytics — customer-facing reporting built into your product — we work with Sigma or custom implementations using tools like Cube Dev as a semantic layer.
We design for adoption, not aesthetics. A dashboard no one uses is just an expensive screenshot. Every report we build is grounded in the actual decisions people need to make.
Outcome: Dashboards and reports that get used — and that people trust.
