DLG Data Foundation Programme
Strengthen the data, quality, pipelines, governance and access required for reliable analytics and AI.
What the engagement can cover
Current-state data architecture
Defined in relation to the organisation, selected workflow and evidence available.
Critical data-product mapping
Defined in relation to the organisation, selected workflow and evidence available.
Quality and observability
Defined in relation to the organisation, selected workflow and evidence available.
Integration and pipeline review
Defined in relation to the organisation, selected workflow and evidence available.
Metadata/governance
Defined in relation to the organisation, selected workflow and evidence available.
AI-readiness roadmap
Defined in relation to the organisation, selected workflow and evidence available.
Typical outputs
How decisions are made
Agree objective and constraints.
Gather the minimum evidence required.
Develop the proposed approach.
Test against quality, value and risk.
Stop, adapt, deploy or scale.
What DataLgorithmics does not assume
Scope, duration, technology choices, commercial terms and expected benefits are not fixed in advance. They depend on the organisation, selected use case, data, integration requirements, risk, delivery model and evidence gathered during the engagement.
Connect learning with delivery
Engagements can be combined with Academy pathways so executives, business teams, engineers and governance functions build the capability needed to operate the resulting systems.
Explore Academy pathwaysDiscuss DLG Data Foundation Programme
Share the organisation, current challenge, intended outcome and timeframe.
Request a scoping conversation