DATUM has already built what other consultancies take 18 months to assemble — integration, architecture and data teams. Metadata-first. Governed DataOps. Controlled publishing to the business. Operational in 4–8 weeks.
It competes against the cost, time and risk of building everything from scratch.
Sector and size aside, the organizations that do move forward with data resolve the same four patterns from day one — not five years later.
Having lakehouse, platform or reporting is the substrate. DATUM adds the model that decides which data is reliable, who owns it and how it operates. Technology and model arrive together.
Policies and glossaries written without execution stay in archives. DATUM turns each policy into an active mechanism: real ownership, automatic controls, measurable compliance.
Ad hoc pipelines force every use case to start from scratch. DATUM builds on idempotent DataOps: the same input always produces the same output, with quality and lineage integrated from the first flow.
AI doesn't fail because of algorithms — it fails because of the quality, semantics and traceability of training data. DATUM publishes datasets with live metadata, complete lineage and measurable quality to feed AI in production.
It is about model, roles and responsibility.
Each of these profiles has its own page with its day-to-day, its modus operandi and its success metric. Any of the six is a good entry point.
It is the nervous system of your organization's data.
DATUM covers the complete data cycle — from strategy to data products — in an integrated, governed and secure-by-design model.
The platform is yours, not ours.
Processing, transformation and advanced analytics engine. Unified Lakehouse with integrated governance. AI and ML on certified data.
Bronze → Silver → Gold layers with ACID transactions. Full traceability, schema evolution and time travel. Agnostic to the underlying storage cloud.
Event streaming between systems based on Apache Kafka. Continuous source integration without interruptions. Real-time data with governance applied.
Storage/compute separation for maximum cost efficiency. High-speed analytics and self-service BI for business without depending on IT.
What scales is the adoption, not the core.
The time other consultancies spend building from scratch, we spend on adoption and value. No endless projects, no vendor dependencies.
Technology stack configured. Data Office and Data Committee established. First DataOps pipelines active. Minimum metadata operational. Roles with real mandate.
Each business domain formally onboarded into the governance model. Data Owners and Stewards activated with their own KPIs. Critical entities catalogued and quality defined. No impact on operations.
Embedding the model across the organisation. Training, communities of practice and visible leadership. Each stabilised domain scales to the next DATUM TIER.
Each block has its own billing model. They can be contracted in sequence or in parallel depending on the client's context.
Formal maturity diagnosis, critical domains and target model. The basis for deciding what to build and in what order.
Technical deployment of DATUM and end-to-end validation. First pilot domain partially included.
Progressive incorporation of domains. Sized by the real complexity of each domain.
Advanced capabilities and managed operation when maturity requires it.
Example with 2 M-sized domains (Customer Managed). Real timelines depend on each organisation's context and are confirmed in the Assessment.
Indicative example — 2 M-sized domains · Customer Managed · no add-ons. Your real plan is generated automatically in the simulator or confirmed in the Assessment.
They are results from real clients.
Anonymised data from real deployments in banking, healthcare, retail and insurance. No estimated projections.
Not on tools.
Metadata is not documentation — it's the engine that governs platform behaviour. Glossary, lineage and dictionaries operate in real time, not as static files.
Governance is not documented, it's executed. Real ownership, least privilege by design, active policies. No data without an owner, no access without control.
The same input always produces the same output. Running the pipeline once or a hundred times produces the same result. No surprises, no variability, no dependency on who built it.
Any data is traceable in its origin, transformation and consumption. The platform is auditable in real time — not reconstructible after the fact. Complete traceability and auditability.
Not a project that gets delivered and abandoned. An operating model that grows with the organisation. Data technical debt does not accumulate.
Classification, least privilege, masking and continuous auditing integrated into the governance model — not added at the end. GDPR, ISO 27001 and local data protection by design from the first domain.
Without changing the core. Without starting from scratch.
They do not replace core capabilities. Activated when client maturity requires it. Recurring billing model.
Extends DATUM with near real-time capture of events and continuous flows, integrating Confluent Cloud into the governed DataOps circuit. Data in motion is traced, qualified and published to the same standards as data at rest.
Incorporates advanced Master Data Management capabilities into DATUM's governed model. Matching, deduplication, golden record and continuous multi-source master data operation with full lineage.
Enables the Data Mesh model on DATUM. Each Data Owner manages their catalogue, defines their data products and publishes them to other domains with explicit, governed consent.
Extends DATUM with specific governance capabilities for AI/ML use cases. Dataset traceability, sensitive attribute control, feature lineage and audited registry of models in production.
Adds an operational and economic observability layer to DATUM. Data health dashboards, quality and freshness SLAs, proactive alerts and cloud infrastructure cost control per domain.
Extends DATUM's connectivity capabilities to legacy technologies, non-standard systems or particularly complex sources requiring specific adapters outside the base catalogue.
Accelerates DATUM deployment in regulated sectors with pre-built data models, business glossaries and KPIs aligned with sector reference standards.
Governed publication to Power BI with metrics, KPIs and traceability. Data definitions live in DATUM; Power BI consumes without duplicating logic.
No client names — results that speak for themselves.
Design of the global data ecosystem architecture, legacy platform integration and analytical modernisation for a financial institution operating across multiple countries.
Definition of the corporate data strategy, implementation of a federated governance model and design of a modern architecture based on Databricks, Confluent Cloud and Snowflake to industrialise data exploitation and optimise platform costs.
Definition of the data strategy and a Data-Driven architecture to activate precision oncology analytics, clinical data governance under HL7 FHIR and mCODE standards, and interoperability between heterogeneous systems.
DATUM is the alternative to 18 months of ad hoc integration, architecture and operation — with measurable deliverables per phase, not at the end of the project. The first clients who launch now access founding conditions with frozen pricing and a subsidized pilot.