Data Readiness Scorecard

If finance, sales, and the data warehouse each report revenue today, do the three numbers match?

Yes, every time, with a documented definition behind each system

How many of your top 10 metrics have a written, signed-off definition?

Can a new analyst answer 'what is our Q4 ARR' without pinging Slack for clarification?

Can you reliably link the same customer or patient across all your systems?

Yes, master data management is in production with measured match rates

No, the same entity exists under multiple keys with no resolution

If your CRM and your billing system disagree about the same account, which wins?

There is a named system of record per entity, documented and enforced

If a number in a dashboard moves 15% week over week, can you trace it to the source?

Maybe, depending on which dashboard and which data engineer is around

When a source schema changes, what happens to your downstream reports?

We get an alert in advance, schema contracts catch it before deploy

A named role with budget, reporting to a C-level, with operational SLAs

A working group across IT, Finance, and Ops with quarterly reviews

Nobody owns it, it is everyone's problem and therefore no one's

Steering committee with documented decision rights, change log, and rollback plan

A small group reviews and approves, decisions are recorded somewhere

A senior analyst makes the call when asked, no formal review

Is there an executive who would lose budget if a major data quality incident happened next quarter?

No, data quality is a tax everyone pays but no one budgets for

How often do your daily data pipelines fail, and how do you find out?

Less than 1% failure rate, automated alerts, on-call rotation

On-call engineer is paged, fixes within 1 hour, post-mortem follows

Someone on the data team picks it up Monday morning, fixed within the day

Your data foundation is ready for AI workloads in production.

You are in the top 15% of organizations we assess. Your metric definitions are governed, identity resolution is solved, lineage is queryable, ownership is named, and pipelines are reliable. AI deployments on top of this stack ship to production at roughly 6x the rate of the broader population.

Use the audit to validate the foundation against a specific AI use case, identify the 1 or 2 remaining edge cases, and ship a 90-day production deployment plan.

You have real infrastructure but specific gaps will block AI.

You are in the middle 60% of organizations we assess. Some dimensions are mature (often metrics or pipelines), others have known gaps (usually identity resolution, lineage, or governance ownership). AI projects on this foundation reach pilot reliably but stall at the production transition because the gaps surface as soon as autonomous decisions hit live data.

Use the audit to identify the 2 or 3 highest-impact remediations and sequence them as a 90-day roadmap. Most clients in this tier ship a production AI deployment within 6 months of the audit.

AI projects on this foundation are likely to fail at the pilot-to-production wall.

You are in the bottom 25% of organizations we assess. The data layer underneath is unstable in 3 or more of the 5 dimensions, which is the exact pattern we see in the 75% of enterprise AI projects that fail to reach production. Investing in AI tooling before fixing the foundation is the textbook waste that drives the $4M pilot trap.

Use the audit to map the foundation gaps, build a remediation plan that pays for itself within 6 to 12 months, and decide whether AI is the right next investment or whether governance and data quality come first.

Your data foundation across the five dimensions that decide whether AI can ship: metric definitions, identity resolution, lineage and traceability, governance and ownership, and pipeline reliability. Each is scored, then combined into an overall readiness tier.

About 5 minutes. It is 12 questions, no preparation needed, and you get your tier, a numeric score, and a per-dimension breakdown at the end.

Answers are scored 0 to 4 per dimension, weighted, and combined. The scoring is intentionally conservative and rewards specifics, a named owner, a documented definition, over vague answers, which is what separates a foundation that is ready from one that is not.

It uses the same rubric, in a lighter, self-serve form. The 30-day Analytics Truth Audit is the deep version: a full inventory of your data assets, metric-conflict analysis, and a 90-day roadmap, validated against your real environment.

No. Your answers are used only to generate your scorecard and are stored in our own systems so we can follow up if you ask. We never sell or share them.

An on-screen result with your tier, score, and per-dimension breakdown, a personalized 90-day remediation plan, a downloadable PDF, and a copy emailed to you.

These results use the same rubric as our paid 30-day Analytics Truth Audit. Book the audit to validate them against your real environment and get a costed, sequenced 90-day plan.

Find out in 5 minutes whether your data foundation is ready for AI. 12-question diagnostic across 5 dimensions, with a personalized 90-day remediation plan.

Downloadable PDF with the full breakdown, yours to share internally

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75% of enterprise AI projects fail to reach production. The reason is almost never the AI model. It is the data layer underneath. This scorecard surfaces the foundation gaps that will block your next AI deployment, before you spend the next $1M on tooling.

12 questions across 5 dimensions. No marketing fluff. Same rubric we use on paid 30-day Truth Audits. You will get a tier, a numeric score, and a personalized remediation plan, with a PDF built from your answers to download and share.

No credit card. No 30-minute discovery call. Email gate at the end so we can deliver the PDF and follow up if your tier suggests we should talk.

Your score, per-dimension breakdown, and 90-day remediation plan unlock on the next screen, with a personalized PDF you can download and share with your team.

We will send the PDF, plus a short follow-up if your tier suggests a 30-day Truth Audit would be the right next step. No spam, you can unsubscribe with one click.

Pulse Convert claims 88% accuracy. The 12% gap is where the cost lives. Our migration checklist walks through the questions the vendor demo will not surface.