Data Maturity Assessment

Score your data quality, governance, integration, reporting, and analytics, and get a sequenced data roadmap.

Example scenario—not a forecast or verified benchmark. Enable JavaScript to edit inputs, view charts, save locally and export your analysis.

Example inputs

Customer, job, and product records are complete and consistent (no duplicates, standard fields).: 2
When two reports show the same metric, they agree.: 2
Staff routinely fix or work around bad data before they can use it.: 4
Data is entered once, at the source, by the person who knows it.: 3
Each core dataset (customers, jobs, finance) has a named owner.: 2
Our key metrics have written definitions everyone uses.: 2
Access to sensitive data is controlled by role.: 3
Our CRM, operations, and accounting systems share data automatically.: 2
We can get all data about one customer in one place.: 2
Important data lives in personal spreadsheets or inboxes.: 4
Leadership has a dashboard that updates without anyone building it by hand.: 2
Month-end numbers are available within 10 business days.: 3
Managers can answer their own data questions without asking an analyst or the owner.: 2
We forecast cash, demand, or capacity from data rather than gut feel.: 2
We use data to decide pricing, marketing spend, or staffing.: 3
We have tested decisions with data (A/B tests, pilots with a control group).: 1
Our data is clean and connected enough to train or feed AI tools.: 2

Data maturity: 29/100 (Level 2: Reactive). Foundations score 30/100 and usage 29/100; start by cleaning the core records.

Connect CRM, operations, and accounting through native integrations or a sync tool so numbers stop being re-keyed.

Use the data you have for one forward-looking model (cash, demand, or capacity forecast) and review it monthly.

Pick your 3 most-used records (customer, job, invoice), define required fields and naming rules, and clean duplicates monthly.

AI tools amplify bad data. Get foundations to 60+ before investing in AI analytics.

Days 1–30: Pick your 3 most-used records (customer, job, invoice), define required fields and naming rules, and clean duplicates monthly.
Days 1–45: Name an owner for each core dataset, write one-page definitions for your top KPIs, and set access rules by role.
Days 30–90: Connect CRM, operations, and accounting through native integrations or a sync tool so numbers stop being re-keyed.

Level cut-offs (25/45/65/82) are a simplified five-level data maturity model, not a formal standard.
The roadmap follows a foundations-first sequence (quality, governance, integration, then reporting and analytics); areas scoring 70+ are skipped.
AI readiness threshold of 60/100 on foundations is a rule of thumb.