Data Warehouse Consulting That Starts with Your Questions
The right architecture depends on what the business needs to know, where the data lives, and what you can spend. Our data warehouse consulting weighs all three before recommending a warehouse, a lakehouse, or a simpler data mart.
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Business questions
The reports, analyses, and AI uses the data must support.
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Sources
Systems, volumes, update frequency, and data quality today.
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Constraints
Budget, skills, security, and existing cloud commitments.
Recommend
- Data warehouse
- Lakehouse
- Data mart
- Keep and model
Request a free data warehouse consultation
Data Architecture Consulting, Layer by Layer
Our data architecture consulting designs each layer of the warehouse so data arrives reliably, is modeled once, and reaches users in the form they need.
Ingestion
Pipelines that load each source on schedule, with checks for missing or late data.
Modeling
A tested model with business definitions, history, and one version of each metric.
Serving
Access for BI tools, analysts, and applications, with permissions by role.
From Data Warehouse Design to the First Trusted Report
We build in stages so the business sees value early: the first release covers the reports leadership needs most. Cognativ has built data platforms behind a data business that reached a 75% share of its category and a healthcare data company valued at 20x at exit.
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Assess
Inventory sources, questions, and the current reporting pain.
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Design
Choose the architecture, platform, and model for the first release.
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Build
Load, model, and test the first sources and reports.
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Extend
Add sources and users release by release, with quality checks.
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Data Warehouse, Data Strategy, or Business Intelligence First?
Start with the warehouse when the questions are clear and the problem is scattered, slow, or inconsistent data. Start with a data strategy when it is not yet clear which uses of data deserve investment. Start with business intelligence when the data is in place but reports are missing or not trusted.
Whichever comes first, the design leaves room for the other two, so nothing has to be rebuilt later.
Explore data strategy consulting
Data Warehouse Consulting Questions
What to expect before you start.
What does a data warehouse consultant do?
A data warehouse consultant decides how your data should be stored and modeled for reporting and analysis, designs the warehouse, and plans how sources are loaded and kept accurate.
Which data warehouse platform should we use?
The method is platform-neutral: Snowflake, BigQuery, Amazon Redshift, Azure Synapse, and Databricks each fit different needs. We recommend based on your sources, skills, cloud, and cost.
What is the difference between a data warehouse and a lakehouse?
A warehouse stores structured, modeled data for reporting. A lakehouse also handles raw and unstructured data for analytics and AI on one platform.
Can you modernize an existing warehouse?
Yes. We assess what works, what costs too much, and what blocks new uses, then plan a staged migration that keeps reports running.
How do you keep warehouse costs under control?
By sizing compute to real workloads, modeling data once, and monitoring usage, with cost targets agreed in the design.
How does an engagement start?
With a free first consultation about your reporting problems, then a source and requirements assessment with a fixed scope and fee.
Data Platform Case Studies
Data platforms built to serve reporting, products, and growth.
Data Platform Reaches 75% Category Market Share
Data Platforms
Data Aggregation
Data Management
Technology Strategy
Data Platform Reaches 75% Category Market Share
Cognativ helped reposition a data management offering around a clearer market category, stronger operational narrative, and more defensible growth strategy.
The repositioning helped the business establish 75% market share in an emerging category while clarifying its value for enterprise buyers.
Explore the Data Category Win
Healthcare Data: 20x Exit Valuation
Data Platforms
API Development
Data Integration
Data Marts
Healthcare Data: 20x Exit Valuation
Cognativ helped a traditional third-party payer reposition from a claims-processing service provider into a strategic healthcare data broker.
The transformation reshaped the company’s go-to-market model and increased exit valuation by 20x.
See the 20x Exit Story
Health Insurance Reconciliation Drops 70%
Data Integration
Claims Analytics
Business Reporting
Data Aggregation
Health Insurance Reconciliation Drops 70%
Cognativ integrated fragmented claims, enrollment, care management, finance, and reporting data into a unified insight engine for a health insurance organization.
The work reduced manual reconciliation by 70%, improved audit readiness, and gave leadership real-time visibility.
Review the Data Unification
B2B Distribution Throughput Increases 35%
ERP Modernization
API Integration
Inventory Management
Warehouse Automation
B2B Distribution Throughput Increases 35%
Cognativ helped a B2B distributor modernize legacy ERP limitations, automate warehouse workflows, and improve operational visibility.
The transformation increased throughput by 35%, raised average order size by 20%, and lifted gross margins by 15%.
See the Throughput GainsRelated AI Insights
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