AI TRAINING
Data Hygiene for SMEs Without a Data Team
Leave with a clean, AI-ready dataset and a repeatable hygiene routine your team can maintain alone.
What it covers
A one-day hands-on workshop covering the essentials of data quality for small and mid-sized businesses that lack dedicated data staff. Participants learn to identify and fix common data problems — duplicates, inconsistent naming, broken schemas — using tools they already have (Excel, Google Sheets, or a basic CRM). By the end, each participant leaves with a personal data-hygiene checklist and a documented clean-up workflow ready to apply to their own datasets.
What you'll be able to do
- Detect and resolve duplicate records and naming inconsistencies in a real spreadsheet or CRM export
- Define and apply a column schema with data types and validation rules for a key business dataset
- Set up a simple backup and versioning routine using existing tools (Google Drive, OneDrive, or similar)
- Produce a one-page data-hygiene checklist tailored to your team's main data sources
- Assess whether a dataset is ready to feed into an AI or automation tool, and identify what still needs fixing
Topics covered
- Identifying and removing duplicate records in spreadsheets and CRMs
- Enforcing consistent naming conventions and field formats
- Schema sanity checks: column types, mandatory fields, and validation rules
- CRM hygiene best practices (contacts, accounts, deal stages)
- Basic deduplication techniques without code
- Backup routines and versioning for small teams
- Preparing a dataset for AI or automation tools
- Building a repeatable data-hygiene checklist
Delivery
Delivered in-person or live-online (half-day morning session plus structured lab in the afternoon). Participants must bring a real dataset — anonymised if needed — to work on during the lab. Hands-on exercises account for roughly 60% of the day. Materials include a reusable hygiene checklist template, a schema validation worksheet, and a recorded recap sent after the session. A follow-up 30-minute Q&A call can be added as an option.
What makes it work
- Assigning one named 'data steward' per key dataset, even if it's a part-time role
- Documenting a simple naming convention and field glossary that the whole team can reference
- Scheduling a short monthly data-review ritual to catch drift before it compounds
- Validating data at entry point (dropdown lists, required fields) rather than cleaning it downstream
Common mistakes
- Cleaning data once as a project rather than establishing an ongoing routine
- Letting every team member invent their own naming conventions with no shared standard
- Assuming the CRM or SaaS tool handles data quality automatically without any configuration
- Skipping backups until a corruption or accidental deletion causes a crisis
When NOT to take this
If the organisation already has a data engineer or analytics team managing a centralised data warehouse, this workshop is too basic — they need a data-quality framework or dbt-based pipeline review instead.
Providers to consider
Sources
This training is part of a Data & AI catalog built for leaders serious about execution. Take the free diagnostic to see which trainings your team needs.