Analytics Maturity Self-Assessment (interactive worksheet)

{"Title":"Analytics Maturity Self-Assessment (interactive worksheet)","IntroductionHtml":"

This interactive worksheet helps you quickly surface capability gaps across six areas: data quality, data catalogs & metadata, data pipelines & integration, analytics delivery & BI, model operations, and governance, organization & skills. Rate each statement on a 1–5 scale where 1 = Not established and 5 = Optimized. Answer based on typical practice over the last 90 days.

Scoring guidance: Each domain has four statements. Sum the four ratings to get a domain score (minimum 4, maximum 20). Band definitions: 4–7 = Emerging; 8–11 = Developing; 12–15 = Proficient; 16–20 = Advanced. After saving, your responses are stored; platform features can compute totals, generate a gap analysis, and produce an exportable executive summary.

","SubmitLabel":"Save assessment","SuccessMessage":"Your responses have been saved. To see computed domain scores, an auto-generated gap analysis, and an exportable executive summary slide, open the Assessment Results view. (If automatic scoring is not enabled for this site, you can export responses and compute scores externally.)","DataType":"AnalyticsMaturitySelfAssessment","SchemaVersion":"1.0","Fields":[{"Key":"dq_1","FieldType":"scale","Label":"Data quality is actively measured and reported with defined metrics and tolerances.","Min":1,"Max":5,"HelpText":"1 = No measurements; 5 = automated real-time metrics, quality SLAs, and alerts."},{"Key":"dq_2","FieldType":"scale","Label":"Critical data elements have clear owners, definitions, and single authoritative sources.","Min":1,"Max":5,"HelpText":"1 = No ownership or definitions; 5 = documented business glossary and authoritative sources."},{"Key":"dq_3","FieldType":"scale","Label":"Data validation and cleansing are integrated into pipelines before analytics use.","Min":1,"Max":5,"HelpText":"1 = Manual or ad-hoc cleaning; 5 = automated validation with rejection/repair workflows."},{"Key":"dq_4","FieldType":"scale","Label":"We measure and act on downstream impacts of poor data quality (rework, incorrect decisions).","Min":1,"Max":5,"HelpText":"1 = No measurement; 5 = quantified impact metrics used in prioritization."},{"Key":"dc_1","FieldType":"scale","Label":"An accessible data catalog and metadata layer helps users discover datasets and understand lineage.","Min":1,"Max":5,"HelpText":"1 = No catalog; 5 = searchable catalog with lineage and usage metrics."},{"Key":"dc_2","FieldType":"scale","Label":"Schemas, business glossary terms, and metadata are actively maintained and versioned.","Min":1,"Max":5,"HelpText":"1 = Unmanaged; 5 = versioned metadata with change processes."},{"Key":"dc_3","FieldType":"scale","Label":"Self-service users can find trustworthy datasets without repeated analyst involvement.","Min":1,"Max":5,"HelpText":"1 = Analysts required for all queries; 5 = self-service with governance controls."},{"Key":"dc_4","FieldType":"scale","Label":"Lineage and provenance are available for critical analytic datasets.","Min":1,"Max":5,"HelpText":"1 = No lineage; 5 = end-to-end lineage for critical data flows."},{"Key":"dp_1","FieldType":"scale","Label":"Data ingestion and ETL/ELT processes are monitored, documented, and reliable.","Min":1,"Max":5,"HelpText":"1 = Frequent silent failures; 5 = monitored pipelines with alerts and retries."},{"Key":"dp_2","FieldType":"scale","Label":"We have standardized connectors and schemas for common sources (ERP, CRM, IoT, files).","Min":1,"Max":5,"HelpText":"1 = One-off connectors; 5 = standardized reusable connectors and templates."},{"Key":"dp_3","FieldType":"scale","Label":"Pipeline performance and cost are tracked and optimized regularly.","Min":1,"Max":5,"HelpText":"1 = No tracking; 5 = routine performance and cost optimization."},{"Key":"dp_4","FieldType":"scale","Label":"There are tested rollback and recovery procedures for pipeline failures.","Min":1,"Max":5,"HelpText":"1 = No recovery plan; 5 = automated rollback and tested DR procedures."},{"Key":"ad_1","FieldType":"scale","Label":"Analytics outputs (dashboards, reports) are tied directly to decisions and owners.","Min":1,"Max":5,"HelpText":"1 = Outputs created without clear use; 5 = every analytic output linked to a decision and owner."},{"Key":"ad_2","FieldType":"scale","Label":"Dashboards and reports follow design standards that emphasize action and clarity.","Min":1,"Max":5,"HelpText":"1 = Inconsistent and confusing; 5 = standardized, actionable designs with user training."},{"Key":"ad_3","FieldType":"scale","Label":"We have a repeatable delivery process for analytics requests with SLAs and acceptance criteria.","Min":1,"Max":5,"HelpText":"1 = Ad-hoc delivery; 5 = ticketing, SLAs, and acceptance tests."},{"Key":"ad_4","FieldType":"scale","Label":"Users can access near-real-time insights where business value requires it.","Min":1,"Max":5,"HelpText":"1 = Only batch reports; 5 = near-real-time where needed and affordable."},{"Key":"mo_1","FieldType":"scale","Label":"Models in production are monitored for performance drift and data drift.","Min":1,"Max":5,"HelpText":"1 = No monitoring; 5 = automated drift detection and alerting."},{"Key":"mo_2","FieldType":"scale","Label":"There is a repeatable CI/CD pipeline for model deployment and rollback.","Min":1,"Max":5,"HelpText":"1 = Manual deployments; 5 = automated CI/CD with tests and canary releases."},{"Key":"mo_3","FieldType":"scale","Label":"Model ownership, validation, and re-training policies are defined and enforced.","Min":1,"Max":5,"HelpText":"1 = No policies; 5 = enforced policies with audits."},{"Key":"mo_4","FieldType":"scale","Label":"We have an inventory of production models with metadata and performance history.","Min":1,"Max":5,"HelpText":"1 = No inventory; 5 = searchable inventory with history and owners."},{"Key":"go_1","FieldType":"scale","Label":"Data governance roles (stewards, custodians, owners) are assigned and active.","Min":1,"Max":5,"HelpText":"1 = No roles; 5 = assigned roles with active accountability."},{"Key":"go_2","FieldType":"scale","Label":"Policies for access, privacy, and compliance are documented and enforced.","Min":1,"Max":5,"HelpText":"1 = Unclear policies; 5 = documented and enforced policies with audits."},{"Key":"go_3","FieldType":"scale","Label":"We run regular training and onboarding so teams use analytics tools and follow governance.","Min":1,"Max":5,"HelpText":"1 = No training; 5 = regular role-based training and onboarding."},{"Key":"go_4","FieldType":"scale","Label":"There are clear investment and prioritization processes for analytics initiatives.","Min":1,"Max":5,"HelpText":"1 = Ad-hoc funding; 5 = formal prioritization, ROI criteria, and portfolio reviews."},{"Key":"overall_priority","FieldType":"checkbox","Label":"Domains to prioritize for improvement (select any):","Options":[{"Value":"data_quality","Label":"Data quality"},{"Value":"catalogs_metadata","Label":"Catalogs & metadata"},{"Value":"pipelines_integration","Label":"Pipelines & integration"},{"Value":"analytics_delivery","Label":"Analytics delivery & BI"},{"Value":"model_ops","Label":"Model operations"},{"Value":"governance_org_skills","Label":"Governance, organization & skills"}],"HelpText":"Select the domains you believe should receive priority investment."},{"Key":"overall_comments","FieldType":"textarea","Label":"Notes & observations (what surprised you, quick wins you see, constraints):","HelpText":"Capture context, examples, or proposed quick wins. These notes help prioritize improvement actions."},{"Key":"request_export","FieldType":"yesno","Label":"Would you like an exportable executive summary (slide) generated from these responses?","HelpText":"If yes, the platform can prepare a formatted summary for download or sharing (subject to site capabilities)."}]}