Two reports, two numbers.
A C-level meeting about revenue is turning into a fight because the finance team and the operations team cannot agree on the number. The root cause? The same metric is defined and calculated differently in four data systems and manually reconciled. We build the data layer that makes one number the trusted number.
Why the Dashboard Does Not Get Used
The number cannot be trusted.
The data is already a day old, but the business needs to decide today.
Every new question is a project.
The AI initiative is stalled because the data layer lacks governance.
What We Build
Data Platform Architecture
Data platforms engineered around your data, not a reference architecture. We will design a data warehouse, a data lakehouse, or a hybrid solution, depending on your needs. Query patterns, data governance, and regulatory requirements are also factored into the design.
Ingestion and Data Pipelines
Batch and streaming pipelines that stitch together data from applications, SaaS, databases, files, and third-party APIs. We build in monitoring to keep pipelines running and data fresh.
Data Modeling and Semantic Layer
Dimensional models and semantic layers that standardize definitions of metrics, dimensions, and segments so that they can appear consistently across dashboards, notebooks, reports, and AI applications.
Data Quality
Design and implementation of automated data freshness, volume, schema, and logic tests so that downstream systems are not polluted with bad data.
Business Intelligence
BI dashboards built around decisions, not deliverables. Governance and smart defaults baked in so that the decisions that matter to your business are actually happening.
Real-Time Analytics
Streaming pipelines that power use cases that require seconds instead of hours or days. Fraud detection, operational analytics, personalization engines, and similar use cases all require fresh data to function.
Data Governance
Implementation of data lineage, cataloging, access controls, retention policies, PII identification and masking, and other governance controls at the data layer. These controls secure your data but also enable enterprise AI initiatives by establishing an audit trail.
Technology stack
- Data Warehouses
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Snowflake
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BigQuery
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Databricks
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Amazon Redshift
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Azure Synapse
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ClickHouse
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PostgreSQL
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- Data Pipelines
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Airflow
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dbt
- Fivetran
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Kafka
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Spark
- cloud-native ETL
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- Business Intelligence
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- Power BI
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Tableau
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Looker
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Metabase
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Superset
- Data Governance
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- Cloud-native catalog
- OpenLineage
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dbt tests
- Great Expectations
- Streaming
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Kafka
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Amazon Kinesis
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Google Pub/Sub
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Flink
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The Data Work AI Depends On
Most stalled enterprise AI programs are blocked at the data layer rather than the model layer. Before a retrieval system or predictive model can deliver business value, four things need to be true:
Accessible
Data needs to be accessible to systems, not just users with the right login credentials
Permissioned
Permissions should be enforced by systems so that AI applications respect the same security controls as humans
Modeled
Business entities and metrics need to be consistently modeled so that AI applications can reason about the same concepts
Documented
Data fields need to be documented so that retrieval systems can reason about what the data means, not just what it contains
Standalone or first step
We can deliver this data work as a standalone engagement or as the first step in a broader enterprise AI initiative.Frequently Asked Questions
Get a range for your data project
Start with a data readiness assessment
Two weeks. Current state, quality findings, governance gaps, and a prioritized roadmap.
