Is Your Data Ready
to Power AI?

Is your business-critical data stored in a centralised system — a data warehouse, database, or CRM — rather than primarily in spreadsheets, shared drives, or email attachments?

Can your team query or extract data from core business systems without needing manual export help from IT every time they need it?

Is there a defined process for identifying and correcting data quality issues — such as duplicate records, missing values, inconsistent naming conventions (e.g. "Acme Corp," "Acme Corporation," and "ACME" as separate entries), or undocumented manual adjustments?

Do you know which data sources your team trusts for decision-making versus which ones they treat with caution — and is this documented or communicated formally, rather than held as institutional knowledge by one or two people?

Are your key business metrics — such as “revenue,” “customer,” or “active user” — formally defined in a shared data dictionary or glossary that the organisation uses consistently?

Do your sales, finance, and operations teams calculate core business metrics — such as revenue, active customer count, or conversion rate — using exactly the same methodology and the same data source, producing the same number when asked the same question?

Is the data used for your key business decisions refreshed frequently enough to match the pace of those decisions — for example, daily data for daily operational choices, or weekly data for weekly performance reviews?

Have you documented the actual update frequency of each key data source — and communicated those data lags clearly to the teams relying on them for analysis or decision-making?

Is your structured data consistently formatted across systems — with reliable column names, intact joins between tables, clear reporting hierarchies, and field-level documentation that lets someone new understand what each field represents without asking the person who built the system?

Are your important business documents — policies, SOPs, contracts, procedures, and meeting decisions — stored with clear version control, approval status, and named ownership, rather than buried in email threads, scanned image files, or disorganised shared drives where AI cannot reliably distinguish a final policy from an outdated draft?

Is there a named person or team responsible for data quality, data access control, and data standards across your organisation — not just informally, but as an explicit accountability?

Are there defined rules about who can access which data — managed systematically through role-based access controls — rather than granted on a case-by-case basis through informal requests? This matters for AI because AI tools inherit the access permissions of the account they run under.