Every system we install runs on data. The quality of your automation output is directly determined by the quality of your data input. The good news: getting your data deployment-ready does not require a data engineering team. Most operations can complete this checklist in a few days.
Phase one: data source inventory. List every system where your business stores operational data. CRM, accounting platform, help desk, email marketing tool, spreadsheets, shared drives, paper files. For each source, document: what data it holds, how often it updates, whether it has an API or export function, and who owns it. This inventory is the foundation of every deployment plan we build. Without it, we are guessing.
Phase two: data quality audit. The most common issues we find in pre-deployment audits are duplicate records, inconsistent formatting, missing required fields, and stale data. Start by deduplicating your customer and contact databases. Standardize formats for phone numbers, addresses, dates, and currency. Fill in missing fields where possible. Flag incomplete records for manual review. Cleaning even 80% of your data produces a significant accuracy improvement in automated systems.
Phase three: structural organization. Automation systems work best with structured, accessible data. If your data lives in spreadsheets, ensure columns are clearly labeled, consistently formatted, and free of merged cells or embedded formulas. If you use multiple platforms, map which systems need to share data with the automation and verify that integrations or APIs exist. We need clean pipes between your data and our systems.
Phase four: ongoing data hygiene. Clean data decays. Set up input validation rules on your forms to prevent bad data from entering your systems at the source. Schedule monthly audits to catch drift. Once your automation is running, monitor output quality as a leading indicator. Declining accuracy in an automated system almost always traces back to a data quality issue upstream. Catch it early.