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Data and AI readiness tool

Determine whether your organisation is ready for a data catalog

Assess the leadership, governance, metadata, technology, operating, adoption, funding, and measurement foundations required to select, implement, and operate a catalog successfully.

  • Transparent weighted scoring
  • No external data transmission
  • Practical first-90-day plan

How it works

Complete twelve evidence-based questions, review the weighted result, and use the prioritised actions to plan the next decision.

1

Assess foundations

Choose Yes, Partially, No, or Not known for each readiness dimension and record concise evidence.

2

Review readiness

Receive a readiness percentage, confidence score, threshold-based guidance, and transparent calculation details.

3

Plan action

Use the prerequisites, vendor-preparation checklist, pilot recommendation, and first-90-day plan.

Data catalog readiness assessment

Answer based on current, demonstrable conditions rather than planned future activity. All questions contribute to the final score.

Executive sponsorship (10% weight)

Is there a named executive sponsor with authority to resolve cross-functional issues and sustain the initiative?

Evidence examples: signed charter, steering committee terms, approved mandate, named accountable executive.

Do not include confidential, personal, or regulated information.
Prioritised use cases (10% weight)

Are high-value catalog use cases prioritised, measurable, and linked to specific user groups?

Examples: data discovery, regulatory traceability, AI data sourcing, impact analysis, glossary adoption.

Do not include confidential, personal, or regulated information.
Metadata standards (9% weight)

Are minimum metadata standards defined for business, technical, operational, and governance metadata?

Evidence examples: mandatory fields, naming rules, glossary conventions, lineage expectations, quality labels.

Do not include confidential, personal, or regulated information.
Data ownership (9% weight)

Are accountable data owners identified for priority domains and critical data products?

Evidence examples: domain ownership matrix, decision rights, escalation paths, owner acceptance.

Do not include confidential, personal, or regulated information.
Stewardship capacity (9% weight)

Is sufficient stewardship capacity available to curate metadata, resolve issues, and support users?

Consider named stewards, allocated time, training, workload, and coverage for priority domains.

Do not include confidential, personal, or regulated information.
Source-system access (8% weight)

Can the catalog access priority source systems, metadata repositories, and relevant platform interfaces?

Evidence examples: approved service accounts, network routes, metadata APIs, technical contacts, security review.

Do not include confidential, personal, or regulated information.
Integration readiness (8% weight)

Are integration patterns, environments, identities, and deployment responsibilities sufficiently defined?

Consider SSO, APIs, scanners, workflow tools, data-quality platforms, CI/CD, monitoring, and support ownership.

Do not include confidential, personal, or regulated information.
Data classification (8% weight)

Are classification and handling rules defined for sensitive, regulated, confidential, and public data?

Evidence examples: classification taxonomy, policy mapping, automated detection rules, handling guidance.

Do not include confidential, personal, or regulated information.
Operating processes (8% weight)

Are recurring catalog processes defined for onboarding, curation, certification, issue handling, and change control?

Evidence examples: workflow maps, service levels, approval roles, review cadence, exception management.

Do not include confidential, personal, or regulated information.
Adoption plan (7% weight)

Is there a practical adoption plan covering user research, communications, training, champions, and support?

Evidence examples: personas, onboarding journeys, champion network, training calendar, feedback loops.

Do not include confidential, personal, or regulated information.
Funding and capacity (7% weight)

Is funding approved for licensing, implementation, integration, operating capacity, and change activities?

Include internal effort, vendor services, platform costs, training, support, and contingency.

Do not include confidential, personal, or regulated information.
Success measures (7% weight)

Are measurable outcomes, baselines, targets, owners, and reporting cadence defined?

Examples: search success, active users, certified assets, time saved, lineage coverage, issue-resolution time.

Do not include confidential, personal, or regulated information.

Methodology, use, and limitations

The checker supports structured discussion and preparation. It does not replace detailed discovery, architecture, security, legal, procurement, or change-management work.

Methodology

Twelve dimensions are weighted according to their practical influence on data catalog implementation and sustainable operation. Scores are deterministic: identical inputs always produce identical outputs.

How to use the result

  • Validate low-scoring items with accountable owners.
  • Turn the top actions into assigned, dated prerequisites.
  • Use the vendor checklist to structure requirements and demonstrations.
  • Re-run the assessment after foundational work or a pilot.

Limitations

The result depends on user-supplied information and cannot independently verify evidence, organisational complexity, platform compatibility, regulatory obligations, costs, or vendor claims. It should inform—not determine—a final investment decision.

Use these assessments to explore adjacent governance, quality, and organisational readiness considerations.

Data Governance Maturity AssessmentEvaluate sponsorship, decision rights, policies, stewardship, controls, adoption, and measurement. Data Quality CalculatorCalculate a structured score across completeness, accuracy, validity, consistency, uniqueness, timeliness, and integrity. Data Maturity CalculatorAssess broader maturity across strategy, governance, architecture, analytics, people, security, and value realisation.

Frequently asked questions

Practical guidance for using and interpreting the data catalog readiness assessment.

What does “data catalog readiness” mean?

It means the organisation has enough leadership support, prioritised use cases, metadata standards, ownership, stewardship, technical access, operating processes, funding, adoption capacity, and measurable outcomes to select and operate a catalog responsibly.

Should we select a vendor before completing this assessment?

Early market research may be useful, but committing to a vendor before defining use cases, requirements, technical constraints, ownership, operating capacity, and success measures can produce avoidable cost and rework.

Why is executive sponsorship weighted highly?

A catalog crosses data domains, platforms, risk functions, and business teams. A sponsor with authority helps resolve competing priorities, secure capacity, enforce decisions, and maintain accountability after implementation.

What counts as sufficient evidence?

Useful evidence is current, specific, and reviewable—for example an approved charter, named owner, metadata standard, source inventory, security approval, allocated budget, workflow, baseline measure, or documented operating responsibility.

How should we answer when work is planned but not implemented?

Select “Partially” when meaningful work is approved or underway but incomplete. Select “No” when the capability is absent or not materially established. Planned activity alone should not normally be treated as “Yes”.

Why does “Not known” receive a small score rather than zero?

Unknown conditions are different from confirmed absence, but they still create delivery risk. The small score reflects uncertainty while the confidence calculation separately highlights missing knowledge.

What readiness score is required to start a pilot?

A score of 65 or higher generally supports a bounded pilot, subject to the specific low-scoring areas. Scores below 65 may still support limited discovery or proof-of-value work, but not an unconstrained implementation.

How often should the assessment be repeated?

Repeat it after major prerequisite work, before final vendor selection, at the end of a pilot, and periodically during scale-up. The same participants and evidence standards improve comparability over time.

Does a high score guarantee implementation success?

No. The assessment does not verify vendor fit, architecture, data protection, contract quality, change effectiveness, implementation capability, or ongoing leadership behaviour. It indicates preparation, not guaranteed outcomes.

Can different business units complete separate assessments?

Yes. Separate assessments can reveal domain-level differences in ownership, source access, stewardship, processes, and adoption. Use a common scoring approach, then compare evidence and dependencies before selecting an enterprise path.

How should the confidence score be interpreted?

The confidence score reflects whether answers are known and whether evidence was supplied. A high readiness score with low confidence should be validated before procurement or implementation decisions are made.

Is any assessment data sent to DataConsultant or another service?

No external API or transmission is used by this page. Basic calculation occurs during the submitted page request, and browser export files are generated locally. The file does not implement persistent server-side storage.