Data Hygiene Guide: Getting Your Data AI-Ready

Before you invest in AI, automation, or a new CRM, answer this question: is your data clean enough to be useful? If your team maintains shadow spreadsheets, if your reports require manual adjustment before being shared, or if different systems show different numbers for the same metric — your data hygiene needs work first.

The five dimensions of data quality: accuracy (is the data correct?), completeness (are required fields populated?), consistency (do the same entities match across systems?), timeliness (is data current?), and uniqueness (are there duplicates?). Score each dimension for your key data sets. Any dimension below 80% will undermine downstream systems.

Start with a data audit: export your core datasets (customers, products, transactions) and measure. What percentage of customer records have complete contact information? How many duplicates exist? How many records have contradictory data across systems? These numbers establish your baseline.

The highest-ROI cleanup targets: duplicate records (merge them), incomplete records (enrich or archive them), inconsistent formats (standardise: dates, phone numbers, addresses), orphaned records (data that references deleted entities), and stale data (records that haven't been updated in 12+ months).

Prevention matters more than cleanup. Implement validation at the point of entry: required fields, format masks, dropdown selections instead of free text, and automatic deduplication on create. It's 10x cheaper to prevent dirty data than to clean it after the fact.

For AI readiness specifically: AI models need structured, labelled data. Free-text fields are harder to process than structured fields. JSON data is better than CSV. Consistent naming conventions matter. If your data is clean enough for a human to use without manual adjustment, it's likely clean enough for AI.