Data Management Framework
Provide the end-to-end disciplines to manage data across its lifecycle.
Methodology
Lifecycle model across capture, storage, integration, modelling, retention and disposal aligned to recognised data-management domains.
Components
Data lifecycle; Architecture & modelling; Integration & pipelines; Storage & retention; Metadata management; Lifecycle controls.
Governance
CDO/data architecture own; platform teams operate; governance council oversees standards. L1 Initial L2 Developing L3 Defined L4 Managed L5 Optimised Ad hoc Basic, siloed Standardised & Quantified & integrated Predictive & embedded governed
Maturity levels
Implementation roadmap
Diagnose (assess maturity) → Design (tailor framework & governance) → Build (policies, standards, controls, pipelines) → Embed (training, culture, adoption) → Assure (test, benchmark, re-score).
Deliverables
Framework document, governance & RACI, policy/standard templates, maturity score & roadmap, board reporting pack.
Advisory opportunities
Data management operating model; Architecture review; Lifecycle controls build.
Across the Data & AI ecosystem
Knowledge graph · 7 relations