$39M
Annual savings from an operations data programme
Independent data practice
Define the data once. Run everything on it.
We build the governed data layer of an organisation, the technology that serves it and the operating model that keeps it true, so AI works on facts you can trace. Then we measure what the business gets.
Reported results from delivered work, 2017 to 2026.
−70%
Data retrieval time after a platform move
$2bn+
Annual pass through cost made auditable end to end
What we do
Six ways in. One method through all of them.
Governed data layer
Masters, one taxonomy and an Attribute Registry, installed on your platform and owned by your people.
Data operating model
Ownership, stewardship, one intake for every data and AI initiative, and cost you can trace to the domain.
Operationalisation
The target operating model, the finished programme or the new site, made to run on day one. Delivered with a network of senior partners when the work spans practices.
Data and AI delivery lifecycle
A fourteen phase method from idea to run, built for data products as well as applications. Proven on our own products first.
Data and privacy for product companies
A data plane, data contracts and privacy by design for software and AI products, set up before launch rather than after the first audit.
Executive briefings
Keynotes and leadership sessions on data, operations and AI.
AI on evidence
AI that works on facts you can trace.
Discover
Interviews, transcripts and documents in. Processes, roles, data, rules and pain points out, each with its source, plus an agenda of what is still unproven.
Diagnose
The as-is of an operating model, read from your own systems and documents, with evidence, in a fixed week. For companies that want AI and have no governance yet.
Govern
Glossary, ownership, classification and rule registers for your data catalogue. Every entry carries the clause it came from. Conflicts stay open until an owner decides.
Clear
The gate on a migration or a transformation. Every object is checked for classification, ownership, rules and disposition before it moves, with proof of why.
Feed
Verified statements land as governed tables in your own platform, with provenance, versioned and deletable by subject.
Audit trail
What wrote it, what checked it, which source it quotes and which version of the rules applied. Every transformation leaves a record an auditor can follow.
Track record
Where we have delivered
Financial services and market infrastructure
Custody, fund accounting, billing, client data and portfolio governance, under GDPR and BCBS 239.
Retail and pharmacy
Data and metadata operating models, with machine learning for matching, segmentation and inventory.
Product companies and new ventures
Data and privacy foundations for software and AI products, set up before launch.
How it works
From raw inputs to governed outputs
Inputs are shaped into the governed layer. Everything that reads it, from APIs and events to BI and AI, can change without touching the truth.
Ingest
Databases, APIs, IoT, third-party systems via batch, streaming, and CDC.
Taxonomy
Map to a unified taxonomy that stays governed across clouds.
Masters + registry
Product, client and identity masters, plus Attribute Registry extensions for business needs.
Exposure
REST and GraphQL, Kafka and webhooks, BI and AI endpoints. Disposable consumers of the layer.
The durable asset
Governed masters, taxonomy, Attribute Registry
This is the layer we install. Each client gets its own, on its own platform: Snowflake, Databricks, Azure or AWS. Screens, APIs, apps and AI agents sit on top. We structure first, because analytics, automation and AI all assume the data is already right.
Master Data Layer
Strictly governed entities
- Universal Product Master (UPM)
- Universal Client Master (UCM)
- Universal Identity Master (UIM)
Application Extensions
Dynamic attributes via Attribute Registry
- Business-specific fields without breaking the core
- Versioned hierarchy control
- Ready for RPA, ERP, CRM, BI and AI agents
Next step
Start with a Review.
A fixed scope and a written result: current state, target state, the gap in each layer and a delivery plan. Write to pedro.duarte@datapippd.com, or read how we work in the method paper.