{ "schema": "yarn-pack/2", "id": "framework-dcam", "version": "0.1.0", "name": "DCAM (EDM Council) (framework self-assessment)", "engagement": "AISG — framework self-assessment: workshop prep, workshop, or diagnostic strand", "intro": "A guided conversation against DCAM (EDM Council), not a form. Answer in your own words and name the document or record that shows it if you can. About 16 minutes. Assesses the eight DCAM v3.1 components at component level as an AI-readiness baseline. It does not score DCAM’s capabilities and sub-capabilities, and it is not a licensed DCAM assessment.", "tone_default": "professional", "prefill_fields": { "department": [ "Executive", "Finance", "Operations", "Customer / Sales", "Technology / IT", "Data & Analytics", "People & Culture", "Risk & Compliance", "Marketing", "Product" ] }, "scales": [ { "id": "maturity5", "name": "Maturity (distilled model)", "levels": [ { "value": 1, "label": "Does not exist", "gloss": "No capability. Absent, or purely ad hoc / accidental." }, { "value": 2, "label": "Partially exists", "gloss": "Emerging and inconsistent. Pockets of activity, not joined up." }, { "value": 3, "label": "Fully exists", "gloss": "Defined, documented and operating across the organisation." }, { "value": 4, "label": "Fully exists & optimised", "gloss": "Measured, refined and improving against targets." }, { "value": 5, "label": "Fully exists & adaptive", "gloss": "Continuously self-adjusting; a source of advantage." } ], "signals": { "1": [ "no ", "not ", "none", "never", "don't", "do not", "nothing", "absent", "unaware", "haven't", "ad hoc", "ad-hoc", "nonexistent", "no idea", "not really" ], "2": [ "some ", "starting", "beginning", "emerging", "pilot", "trial", "informal", "inconsistent", "pockets", "a bit", "occasionally", "early", "experiment", "trying", "patchy" ], "3": [ "documented", "defined", "standard", "standardised", "established", "policy", "framework", "process", "consistent", "across the", "in place", "formal", "governed", "rolled out" ], "4": [ "measured", "metrics", "optimis", "improving", "kpi", "monitored", "reviewed", "refined", "benchmarked", "targets", "tracked", "mature", "regularly review" ], "5": [ "continuous", "adaptive", "self-", "automated end", "best in class", "best-in-class", "competitive advantage", "industry leading", "always", "real-time monitoring", "feedback loop" ] } } ], "categories": [ { "id": "direction", "name": "Direction and operating model", "order": 1, "target_default": 3 }, { "id": "foundations", "name": "Data foundations and control", "order": 2, "target_default": 3 } ], "audiences": [ { "id": "lead", "name": "Governance / risk lead", "desc": "Owns the policy, the register or the risk framework", "deep_dive_sections": [] }, { "id": "owner", "name": "System or use-case owner", "desc": "Runs an AI system or use case day to day", "deep_dive_sections": [] }, { "id": "exec", "name": "Executive / sponsor", "desc": "Accountable for the outcome, not the mechanics", "deep_dive_sections": [] } ], "sections": [ { "id": "core", "title": "DCAM elements", "blurb": "Everyone answers these. Whoever owns data management and whoever is driving AI investment: the data and governance leads, the AI sponsor, architecture, and the person who signs off funding for foundation work.", "optional": false, "questions": [ { "id": "dcam-1", "type": "scored_text", "category": "direction", "name": "Data Management Strategy", "text": "If I asked for the one document that says what you are doing with your data before you put AI on it, who owns it, and when was it last changed?", "scale": "maturity5", "scored": true, "star": false, "rubric": { "1": "DCAM 1 Not initiated: no written data-management strategy; AI investment is planned without any statement of the data foundations it depends on.", "3": "DCAM 3 Developmental: a written strategy exists with a named owner, is endorsed, and names the data foundations each current AI investment depends on.", "5": "DCAM 5 Achieved: the strategy is re-baselined on a cadence and moves with the AI portfolio; the gap between AI ambition and data readiness is measured and closing." }, "help": "Evidence that would show it: Data-management strategy document with owner and endorsement date; Roadmap tying each AI initiative to the data foundations it needs; Latest DCAM baseline heat-map for this component.", "adaptive": { "allow_probe": true, "allow_skip": false, "max_probes": 1 }, "ai_drafted": false }, { "id": "dcam-2", "type": "scored_text", "category": "direction", "name": "Business Case & Funding", "text": "Where does the money for fixing data come from this year, and is it a line someone defends, or whatever is left after the AI projects are paid for?", "scale": "maturity5", "scored": true, "star": false, "rubric": { "1": "DCAM 1 Not initiated: no business case for data management; foundation work is unfunded or buried inside individual AI project budgets.", "3": "DCAM 3 Developmental: an approved business case exists with a funded line for data-management capability, separate from and ahead of the AI initiatives that rely on it.", "5": "DCAM 5 Achieved: funding is renewed against measured maturity gains and benchmark results; the case is re-argued on a cadence, not made once and left." }, "help": "Evidence that would show it: Approved business case for data-management capability; Budget line or funding decision for foundation work; Maturity gains or benefits reported back to the funder.", "adaptive": { "allow_probe": true, "allow_skip": false, "max_probes": 1 }, "ai_drafted": false }, { "id": "dcam-3", "type": "scored_text", "category": "direction", "name": "Data Management Program", "text": "When did you last score your data capabilities, who did it, and what changed in the plan because of the result?", "scale": "maturity5", "scored": true, "star": false, "rubric": { "1": "DCAM 1 Not initiated: no programme; data work happens project by project with no capability list, no scoring and no roadmap.", "3": "DCAM 3 Developmental: a programme exists with a named lead, a capability inventory scored for maturity, and a roadmap that traces to the scores.", "5": "DCAM 5 Achieved: the programme re-baselines on a set cadence; the heat-map is refreshed and the roadmap re-cut from each new score rather than an old slide." }, "help": "Evidence that would show it: Programme charter with lead and scope; Capability inventory with a maturity score per sub-capability; Roadmap showing the score each item addresses; Re-baseline schedule and the last two results.", "adaptive": { "allow_probe": true, "allow_skip": false, "max_probes": 1 }, "ai_drafted": false }, { "id": "dcam-4", "type": "scored_text", "category": "direction", "name": "Data Governance", "text": "Pick a dataset an AI system reads today. Who owns it, who agreed it could be used that way, and where is that written down?", "scale": "maturity5", "scored": true, "star": false, "rubric": { "1": "DCAM 1 Not initiated: no data owners, no forum, no decision rights; who may use which data for AI has no answer, or a different one each time.", "3": "DCAM 3 Developmental: named owners per data domain, a standing governance forum with a charter, and decision rights written down and followed for AI data use.", "5": "DCAM 5 Achieved: governance decisions are evidenced, reviewed against the benchmark and re-baselined; ownership changes propagate without a project to force them." }, "help": "Evidence that would show it: Data ownership register with a named owner per domain; Governance forum charter, membership and minutes; Decision-rights matrix covering data use by AI.", "adaptive": { "allow_probe": true, "allow_skip": false, "max_probes": 1 }, "ai_drafted": false }, { "id": "dcam-5", "type": "scored_text", "category": "foundations", "name": "Data & Technology Architecture", "text": "If a team wanted to plug a new model or cloud service into your data tomorrow, what would they check it against, and does that document exist today?", "scale": "maturity5", "scored": true, "star": false, "rubric": { "1": "DCAM 1 Not initiated: no documented data or technology architecture; platforms, cloud services and AI tooling are adopted without a target state.", "3": "DCAM 3 Developmental: one documented architecture covers data and technology together, including cloud-native and AI/ML integration, with an owner and a current state.", "5": "DCAM 5 Achieved: the architecture is a living asset; new cloud or AI/ML components are assessed against it before adoption, and it is re-baselined on a cadence." }, "help": "Evidence that would show it: Unified data and technology architecture document with owner; Current-state and target-state views including cloud and AI/ML; Approved tooling and model list with residency and retention stated.", "adaptive": { "allow_probe": true, "allow_skip": false, "max_probes": 1 }, "ai_drafted": false }, { "id": "dcam-6", "type": "scored_text", "category": "foundations", "name": "Business Data Knowledge", "text": "Take a field an AI model uses. Can someone show me its business definition, where it came from and who maintains that, without asking around?", "scale": "maturity5", "scored": true, "star": false, "rubric": { "1": "DCAM 1 Not initiated: no glossary, metadata or taxonomy; the meaning of a field lives in people’s heads and AI is grounded on data nobody can define.", "3": "DCAM 3 Developmental: a business glossary, metadata and taxonomy exist for the data domains in use, are owned, and are consulted when data is prepared for AI.", "5": "DCAM 5 Achieved: glossary, metadata and taxonomy are maintained as data changes; new data sources are catalogued as they arrive, not after the AI is built." }, "help": "Evidence that would show it: Business glossary with owners and definitions; Metadata catalogue covering the data AI systems use; Taxonomy or classification scheme in use; Corpus register naming what each AI answers from.", "adaptive": { "allow_probe": true, "allow_skip": false, "max_probes": 1 }, "ai_drafted": false }, { "id": "dcam-7", "type": "scored_text", "category": "foundations", "name": "Data Quality Management", "text": "How do you know the data your AI runs on is fit for it this month, and what happened the last time a measure fell short?", "scale": "maturity5", "scored": true, "star": false, "rubric": { "1": "DCAM 1 Not initiated: no data-quality rules or measures; quality problems surface in AI outputs and are fixed case by case.", "3": "DCAM 3 Developmental: quality dimensions and rules are defined for the data AI depends on, measured on a schedule, with issues logged, owned and worked.", "5": "DCAM 5 Achieved: quality is measured continuously against a measurable model; thresholds trigger action and corrections reach every system grounded on the data." }, "help": "Evidence that would show it: Data-quality rules and thresholds per critical dataset; Quality measurement results and trend; Data-quality issue log with owners and resolution; Withdrawal or correction propagation record.", "adaptive": { "allow_probe": true, "allow_skip": false, "max_probes": 1 }, "ai_drafted": false }, { "id": "dcam-8", "type": "scored_text", "category": "foundations", "name": "Data Control Environment", "text": "Show me the last time you proved a restricted class of data could not reach an AI tool. Who ran that test, and what did it find?", "scale": "maturity5", "scored": true, "star": false, "rubric": { "1": "DCAM 1 Not initiated: no defined data controls; access, privacy and audit rely on platform defaults and nobody can show a control operating.", "3": "DCAM 3 Developmental: risk, security, privacy and audit controls are defined for data an AI can reach, with owners, and tested evidence that they operate.", "5": "DCAM 5 Achieved: controls are tested on a cadence and adapt as data and AI use change; privacy and protection are proven, not asserted, before each new use." }, "help": "Evidence that would show it: Controls library mapping data risks to controls and owners; Access-enforcement test results on stores an AI can reach; Privacy and security control test evidence; Boundary declaration per third-party data relationship.", "adaptive": { "allow_probe": true, "allow_skip": false, "max_probes": 1 }, "ai_drafted": false } ] } ], "grids": {}, "outputs": [ "Level per element and per category, gated", "Contested-element view (spread of 2 or more)", "Coverage of evidence: confirmed, stated, inferred", "Where to start, foundations first", "Printable client report" ], "report_defaults": [ "rpt-maturity-standard" ], "playbook": { "sequence": [ "direction", "foundations" ], "sequence_note": "Lead with the benchmark gap, baseline all eight components, then act on the heat-map. Foundations before AI, and re-baseline on a cadence or the score becomes a vanity number.", "actions": { "direction": { "to_3": [ "Write and endorse the data-management strategy, with a named owner", "Fund data-management capability as its own line, ahead of the AI that relies on it", "Stand up the programme: capability inventory, first maturity score, roadmap from the score", "Name data owners and set decision rights for data use by AI" ], "to_5": [ "Re-baseline on a set cadence and re-cut the roadmap from each new score", "Report the gap between AI ambition and data readiness, and show it closing" ] }, "foundations": { "to_3": [ "Document one architecture covering data, technology, cloud-native and AI/ML integration", "Build the glossary, metadata and taxonomy for the data AI uses", "Define quality rules and measure the data AI depends on", "Define and test risk, security, privacy and audit controls over data an AI can reach" ], "to_5": [ "Catalogue new sources and assess new components before AI is built on them", "Test controls and quality continuously; prove protection before each new use" ] } } } }