Data Foundation
Fix your data layer before your dashboards or AI models. We build semantic models, certified metric definitions, and data quality frameworks.
What this service covers
- data foundation consulting
- semantic model design
- metric definitions
- data quality consulting
- data layer
- business glossary
- data modeling
Proof: client outcomes from this practice
- We recovered $4.8M a year in misrouted claims by lifting member match accuracy from 75 to 94 of every 100 records, restarting three stalled ML pilots. , Express Scripts
- Implemented a data governance framework for four product lines that cut audit prep time from six weeks to four days and prevented $1.8 million in regulatory fines. , Informatica
Frequently asked questions
What is a data foundation and why does it matter?
A data foundation is the semantic layer, metric definitions, and data quality infrastructure that sits between your raw data sources and your dashboards or AI models. Without it, every downstream system produces different numbers. With it, every team works from the same certified truth.
How long does a data foundation engagement take?
Most data foundation engagements run 8 to 16 weeks depending on scope. We deliver a statement of work with defined milestones so you know exactly what you are getting and when.
Do we need to replace our data platform first?
No. In most cases we fix the semantic layer and metric definitions on top of your existing platform. Platform replacement is rarely the right first step and often the most expensive mistake organizations make.
What does a certified metric definition actually mean?
A certified metric is one that has a single agreed-upon definition, a documented owner, a known lineage from source to report, and a governance process for change management. When a metric is certified, every dashboard and every AI model that uses it produces the same number.
What is a data foundation?
A data foundation is the modeled, governed layer between your raw source systems and everything that reads from them: dashboards, reports, and AI models. It is made of four things: a data warehouse or lakehouse for storage and compute, pipelines that load and transform the data, a semantic layer where metrics are defined once, and the data quality tests and lineage that keep it trustworthy. Get the foundation right and every tool on top of it agrees. Skip it and each team ships its own version of the truth.
Data warehouse vs data lakehouse, which do we need?
A data warehouse (Snowflake, BigQuery, Redshift) stores structured, modeled data optimized for SQL analytics and BI. A lakehouse (Databricks, Microsoft Fabric) puts BI and data science on the same open storage, so structured tables and raw files, including data for machine learning, live in one place. If your work is mostly reporting and metrics, a warehouse is simpler and cheaper to run. If you also need heavy data science, streaming, or large unstructured data next to your tables, a lakehouse earns its complexity.
What is the modern data stack?
The modern data stack is the set of cloud tools most teams now assemble for analytics: a cloud warehouse or lakehouse at the center (Snowflake, BigQuery, Databricks, Microsoft Fabric), managed ingestion to load raw data, dbt for version-controlled transformation and the semantic layer, and a BI tool like Tableau or Power BI on top. The value is not the logos, it is the pattern: raw data lands, transformations are code you can test and review, metrics are defined once, and reporting reads from a governed layer. We build the foundation layers of that stack so the reporting on top holds.
Do you work with Snowflake, Databricks, and Microsoft Fabric?
Yes. We build and model on Snowflake, Databricks, and Microsoft Fabric, and on BigQuery and Redshift, and we use dbt for transformation and the semantic layer across all of them. We are platform-neutral: in most cases we design the foundation on the warehouse or lakehouse you already run rather than pushing a migration. When a platform change is the right call, we say so and scope it against the workloads that justify it.
Request the 30-day Analytics Truth Audit to scope this engagement for your environment.