What's the difference between data hygiene and data enrichment, and does this cheat sheet cover both?

Data hygiene is about keeping existing records accurate, complete, and consistent — fixing duplicates, enforcing required fields, removing stale contacts. Data enrichment is about filling gaps with external data, like pulling company size or industry from a tool like Clay or ZoomInfo. This cheat sheet covers both, including where they overlap in the data entry points and lifecycle capture sections, so you can see how enrichment fits into a broader hygiene strategy rather than treating it as a standalone fix.

Do I need Einstein Activity Capture to apply the activity data recommendations in this cheat sheet?

No — the cheat sheet actually walks through three distinct options: Einstein Activity Capture, the native Salesforce Gmail/Outlook add-ins, and third-party tools like Weflow. EAC is free and easy to set up, but it doesn't store activities as Salesforce records, which means you can't build reports or automation flows on top of it. If activity data is going to feed your pipeline visibility or forecasting work, that limitation matters and the cheat sheet flags it directly.

Which data entry points should stay human-led versus get automated?

Manual entry for nuanced deal context — like a rep's read on stakeholder dynamics — is still worth keeping human-led, but routine logging of emails, meetings, and call outcomes should be automated wherever possible. The cheat sheet cites 550 hours wasted per rep annually due to bad data, and most of that waste comes from expecting reps to manually log interactions they'll consistently skip. Automate the structured, repeatable inputs; reserve human judgment for the qualitative fields that actually require it.

What do I need to have in place in Salesforce before the validation rules and required fields recommendations will actually hold?

You need clean, agreed-upon stage definitions and field-level ownership before validation rules do much good — otherwise you're enforcing structure on top of ambiguity and reps will find workarounds. The cheat sheet recommends documenting standards in a central guide and assigning clear ownership per object before layering in technical enforcement. If your stage criteria aren't written down and socialized, start there before touching Flow or validation logic.

How do I know if my Salesforce data quality is actually improving after running the hygiene processes in this cheat sheet?

The cheat sheet points to a Data Quality Scorecard dashboard that tracks completeness, duplicates, and outdated records — those are your leading indicators. You should also correlate data quality metrics with pipeline velocity and conversion rates over time, because clean data that doesn't move those numbers isn't solving the right problem. Run a completeness audit on your key opportunity fields quarterly and compare it against the prior quarter to see if the gap is closing.

How often should I run the data health checks described in this cheat sheet?

The cheat sheet recommends quarterly data health checks as the baseline cadence, covering completeness audits, duplicate identification, accuracy verification, and orphan record removal. Monthly is worth it for high-velocity teams where data decays faster — especially contact records, since people change roles frequently. Set it on a calendar, assign an owner, and treat it like a recurring ops review rather than a one-time cleanup project.