Data Maturity Scorecard
Scores data maturity across 8 dimensions with evidence and builds a 90-day plan that fixes the real blockers before buying new tools.
Before & after
Same prompt, same agent — the only difference is the skill.
Your data strategy depends on a lot of factors—your current team size, what tools you're using, what problems you're trying to solve, and where your data maturity is. I could outline some common priorities like improving data quality, building dashboards, or setting up better data governance, but I'd need to understand your situation better. What's your biggest data challenge right now? Are you more worried about analytics, compliance, or something else?
CDO-LENS DATA STRATEGY FRAMEWORK: Q3 Priorities
CURRENT STATE AUDIT
Data stack: Segment → Snowflake → Looker + dbt
Maturity: Mid-stage (analytics, not yet ML-ops)
Team: 1 analytics engineer, 1 data analyst, 0 data engineers
Pain points: Dashboard staleness, data quality SLAs undefined, warehouse costs 8K/month (underutilized)
PRIORITY TIER 1 (Revenue-impacting, do first)
1. Data Quality & Freshness SLA
- Define: Critical metrics update within 1 hour; weekly reports within 24 hours
- Implement: dbt tests (not null, unique, referential), Soda monitoring
- Owner: Analytics Engineer
- Timeline: 2 weeks
- Expected outcome: 0 "stale dashboard" incidents next quarter
2. Customer 360 Foundation
- Merge disparate user_id identifiers (email, anonymous_id, uuid)
- Impact: Unblock cohort analysis, retention funnels, churn prediction
- Owner: Data Analyst + Analytics Engineer (pair)
- Timeline: 4 weeks
- Expected outcome: Single source of truth for 95% of users
PRIORITY TIER 2 (Operational efficiency, do parallel)
3. Warehouse Cost Optimization
- Audit: 60% of Snowflake spend is on unused/development queries
- Action: Auto-suspend clusters, archive cold tables, kill unused roles
- Expected savings: 3K/month (37%)
- Owner: Shared (1 week effort)
4. Self-Service Analytics Enablement
- Train product team on Looker filters, saved queries
- Reduce support tickets (analytics engineer is 40% support QA)
- Owner: Analytics Engineer (3 hours training)
PRIORITY TIER 3 (Strategic, defer to Q4)
- ML-ops foundation (feature store, model monitoring)
- Advanced analytics (attribution modeling, causal inference)
- Data governance & compliance (cross-functional alignment)
QUARTERLY ROADMAP
Weeks 1-2: Quality SLA + monitoring
Weeks 2-4: Customer 360 + warehouse cost audit (parallel track)
Weeks 5-12: Rollout, stabilize, train stakeholders
Unblock: Hiring 1 data engineer in Q4 (customer 360 was bottleneck)
SUCCESS METRICS
- SLA compliance: 98% of dashboards on-time
- Customer 360 coverage: 95% of users
- Warehouse cost: 5K/month or less
- Analyst time on support: Below 20%About this skill
Find out where your data setup really stands and what to fix first. This skill scores eight dimensions with evidence and builds a 90-day plan around the one or two constraints holding everything back.
What it does
- Gathers stack, team, goals, regulatory context, and recent examples of data going wrong
- Scores strategy, governance, quality, architecture, metadata, security and privacy, analytics use, and literacy on a 1-5 scale
- Requires a line of evidence for every score
- Identifies binding constraints and the cost of the current state
- Lists investments to postpone and the condition for revisiting them
What you get
A scorecard with evidence and gaps per dimension, cost of current state, a 12-action 90-day plan with owners and outcome metrics, a 'not yet' list, risks, and a rescore date.
Best for
New heads of data, CFOs, and CTOs who need a baseline before investing in analytics, AI features, or a platform migration.
SKILL.md preview
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name: data-maturity-scorecard
description: Assess an organization's data maturity across 8 dimensions (strategy, governance, quality, architecture, metadata, security and privacy, analytics use, people and literacy) with evidence-based 1-5 scores, then produce a 90-day plan that fixes the biggest blockers first. Use when the user asks "how mature is our data setup?", "where should our first data leader start?", "data governance assessment", or needs a baseline before investing in analytics or AI. Also triggers on "where should our new head of data start?", "quick read on our data setup", or "why don't our numbers match across teams?".
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# Data Maturity Scorecard
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