SERVICES / DATA
Data you can decide on
We help you build governed data platforms that give every team the same trusted numbers.
When finance, operations and sales each have their own number, meetings go on reconciling rather than deciding. We build data platforms with definitions agreed once, access controlled at the data layer and lineage you can follow from source to report, on the platform that fits your estate.
What we do
Data platform design
A lakehouse or warehouse architecture chosen for your workloads, skills and cloud. We work with Microsoft Fabric, Databricks, Snowflake, Google BigQuery and Amazon Redshift, and recommend one only after we understand your estate.
- Platform options with trade-offs
- Storage, compute and cost model
- Deployed in your account and region
Data engineering
Pipelines that ingest, clean and model data from the systems you run, tested and monitored like any other production code.
- Batch and streaming ingestion
- Data quality checks in the pipeline
- Pipelines defined as code
Governance and cataloging
Ownership, classification, access and lineage designed in. We integrate with Microsoft Purview, AWS Lake Formation and Google Cloud Knowledge Catalog (formerly Dataplex).
- Data ownership and stewardship model
- Classification and access policies
- Lineage from source to report
Analytics and reporting
A shared semantic layer and reports built on it, so a metric means the same thing wherever it appears.
- Agreed metric definitions
- Self-service reporting on governed data
- Data ready for AI and search use cases
Our solutions
Bring scattered spreadsheets and departmental reports onto one governed platform.
- Inventory of current reports and sources
- Shared metric definitions
- Retire duplicate reports once replaced
Move an on-premises or aging warehouse to a cloud platform in phases, with old and new reconciled until cutover.
- Workload assessment and platform choice
- Parallel run with reconciliation
- Phased cutover by subject area
Make the data your AI use cases depend on findable, governed and scoped to what each user is allowed to see.
- Classification and access rules applied first
- Curated, documented datasets
- Lineage for audit and review
How it runs
Blueprint
A short first engagement: current sources, reports and pain points mapped, with a recommended platform and the reasons for it.
Foundation
Platform, security, governance and pipelines-as-code set up in your cloud account and region.
Deliver by domain
One subject area at a time, from ingestion to reports, each one useful on its own.
Hand over
Runbooks, data ownership agreed and your team trained to extend the platform.
What you get
- A data platform in your cloud account and region
- Pipelines, models and tests defined as code
- A catalog with owners, classifications and lineage
- Agreed metric definitions and reports built on them
- Documentation and runbooks for your team
Questions we get asked
Which data platform do you recommend?
The one that fits your workloads, skills, cloud and budget. Microsoft Fabric, Databricks, Snowflake, Google BigQuery and Amazon Redshift are all options; we recommend what fits, not what we resell.
Can you work with our existing warehouse?
Yes. Often the right first step is to govern and document what you already have before deciding whether anything needs to move.
How do you handle sensitive data?
Data is classified and access policies are applied at the data layer, using the governance tools native to your platform, before it is opened up for reporting or AI.