Data Foundation

HIPAA-ready semantic models, master patient identity, HEDIS and value-based-care metric definitions, lineage from EHR and claims to certified report.

ALCO and credit-risk metric certification, regulator-ready data warehouse design, lineage that survives a Fed exam, governed semantic models for capital and liquidity reporting.

IPEDS-ready data foundation, enrollment and retention metric certification, accreditation data packages, lineage from SIS and LMS to board report.

FOIA-ready data warehouse, audit-trail lineage, IPEDS and CEDS metric foundations, defensible source-to-report path for federal and state agencies.

Dimensional and normalized models designed for the questions the business actually asks, so joins are predictable and a query returns the same answer every time. Star schemas, slowly changing dimensions, and conformed dimensions across domains.

One place where revenue, margin, active user, and churn are defined once, in code, and every dashboard and AI model reads from it. Built in dbt, LookML, or a native warehouse semantic layer, so the definition travels with the number.

The core storage and compute pattern, sized to your data and your team. Snowflake, BigQuery, or Redshift for a warehouse; Databricks or Microsoft Fabric for a lakehouse when you need data science and BI on the same copy of the data.

Reliable extract and load from Salesforce, your ERP, databases, and cloud apps into the warehouse, with transformations version-controlled in dbt. Batch or streaming, with backfills and schema changes handled without breaking downstream reports.

Automated tests on the models that matter: uniqueness, referential integrity, freshness, and accepted-value checks that run on every pipeline. A data quality score you can watch move, and alerts before a bad number reaches a leader.

Documented lineage from source to report, ownership for every certified metric, and a change process that keeps definitions stable. The structure a governance program, and later an AI agent, can trust without a rebuild.

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.

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.

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.

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.

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.

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. We size the choice to your workloads and your team, not to a vendor preference.

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.

It depends on how many source systems feed the warehouse, the condition of the data, whether the warehouse and pipelines exist or need building, and how many domains and metrics you certify first. We scope every engagement against fixed milestones and deliverables before any work starts, so you see the number before you commit. Most foundations run 8 to 16 weeks. The largest cost driver is almost always the state of the source data, and that is also where most of the value sits.

Data foundation consulting: semantic models, certified metric definitions, and data governance to make your analytics trustworthy and AI-ready.

Semantic models, certified metrics, and data governance for trustworthy analytics.

We do not start by spinning up a warehouse. We sequence the work so the models land on a design that holds, the metrics are certified once, and your team can run the foundation after we leave.

Map the source systems, the current warehouse or lakehouse, and exactly where the numbers diverge today.

Choose the warehouse or lakehouse pattern and design the data models for the questions the business asks.

Stand up the pipelines and dbt transformations, load the sources, and version-control every model.

Define the metrics once in the semantic layer, add data quality tests, and document lineage from source to report.

Hand over ownership, alerting, and a change process, so the foundation stays trustworthy instead of drifting.

We certify the domains that drive decisions first. These are the factors that move the effort.

Each Salesforce, ERP, database, and cloud app connected into the governed layer adds integration.

Duplicate, conflicting, and undocumented data takes more to model and reconcile than clean sources.

Building or rebuilding the warehouse and pipelines differs from connecting into one you already run.

How many domains and metrics you certify first sets the build; the long tail follows.

Dashboards and AI keep breaking because the data underneath is messy.

You want one governed reporting layer, not another point fix.

The numbers disagree only by definition: see Semantic Layer Engineering.

You mainly need ongoing monitoring: see Real-Time Data Observability.

You need to retire and merge overlapping systems: see System Consolidation.

The difference between a foundation the whole company trusts and a pile of extracts people reconcile by hand is modeling and governance, not the warehouse brand. Here is what changes.

Defined once in the semantic layer; every tool reads the same number.

Redefined in each spreadsheet and dashboard, and they disagree.

Dimensional models built for the real questions; joins are predictable.

Automated tests for freshness, uniqueness, and integrity on every run.

Errors found by the executive who spots a wrong number in a meeting.

Documented source-to-report path with an owner for every metric.

Nobody is sure where a figure came from or who maintains it.

Most analytics failures are not visualization problems. They are data problems. We design and build the semantic layer, metric definitions, and data models that make every downstream system trustworthy.

A data foundation is the semantic layer, metric definitions, and data models that make every downstream report and AI system trustworthy. Most analytics failures are data problems, not visualization problems. Thinklytics designs and builds that foundation on your existing warehouse, so the numbers agree and everything you build on top of it holds.

We go one layer deeper than most consulting firms. Instead of redesigning dashboards, we fix the semantic model, the metric definitions, and the governance gaps that cause every downstream system to produce different numbers.

A data foundation is more than a warehouse with tables in it. It is the modeling, the semantic layer, the pipelines, and the tests that turn raw source data into numbers every downstream tool agrees on. Here are the six parts we build, and what each one delivers.

The data foundation has the highest ROI in industries where the cost of incorrect, drifting, or undocumented metrics is regulatory, financial, or clinical. These are the segments where we have shipped the most semantic-model and metric-certification work.

Start with a 30-day Analytics Truth Audit. We identify exactly what is broken in your data layer and give you a 90-day roadmap to fix it.