Data strategy and governance
Start with the commercial question. Agree the definitions. Then design the roadmap, the owners and the quality rules so finance, sales and operations stop arguing about whose file is right.
Read the serviceWe help leadership teams agree what the numbers mean, put owners and quality rules on the data behind them, and then put conversational AI and Power BI on Microsoft Fabric, Databricks, AWS, Azure and Snowflake.
Four stages. Click any one for how the work actually looks.
Three services. The platform comes after the question is clear.
Start with the commercial question. Agree the definitions. Then design the roadmap, the owners and the quality rules so finance, sales and operations stop arguing about whose file is right.
Read the serviceDatabricks Genie and similar tools only after the tables are certified. A chatbot on a raw extract is a demo. An answer that finance can audit is a product.
Read the serviceFewer pages. Clear ownership of each measure. Direct Lake in Microsoft Fabric when Friday must be on the screen on Monday.
Read the serviceWe do not arrive with a preferred logo. We work in the platform you already chose.
S3, Glue, Lake Formation, Redshift and Bedrock.
OneLake, Synapse, Purview and Power BI Direct Lake.
Lakehouse, Unity Catalog and Genie on certified tables.
Secure sharing, Snowpark pipelines and Cortex.
Programmes stall when pipelines are built away from the people who own the P and L. We sit with leadership first, write the definitions they will actually defend, and only then shape the lakehouse, the domains and the delivery plan.
Customer, product, supplier and chart of accounts should not live in five versions of Excel. We put master records, schema rules and automated quality checks inside the platform you already run: Unity Catalog, Microsoft Fabric or Snowflake. The catalogue is not a side project.
The output is not a 60 page deck. It is a short sequence: which decisions get a trusted number first, who owns each domain, what gets built this quarter, and what waits.
We set up Databricks Genie and similar natural language interfaces so executives can ask a question in plain language and get an answer from a table that already has an owner, a definition and tests. The query runs against the catalogue. It does not invent a metric.
We will not put a chatbot on a raw dump and call it transformation. If sales, finance and operations still disagree on the customer count, the first job is the definition, not the model.
Use cases are ranked by whether the underlying data is ready. The first live space is small, limited to certified domains, and designed so a risk or finance lead can see exactly which table answered the question.
Where the grain and the freshness justify it, Power BI reads Delta tables in OneLake through Direct Lake. That removes the Sunday refresh and lets leadership inspect a large set without waiting for a scheduled dataset.
We cut the report pack down to the decisions it is meant to support. Each measure has an owner. Self-service sits on the same semantic model as the board pack, so the two views cannot drift apart.
The visual and the natural language question should hit the same certified model. That is how a director can click a page or ask Genie and still get one answer.
We do not arrive to rip out last year's decision. If Glue, Fabric, Databricks or Snowflake is already in place, that is where the pipeline, the catalogue and the consumption layer get finished.
The design should still make sense if a second cloud appears. A new operational source should not need a six month exception process before a trusted table exists.
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