Nobody has to remember to run it
Pick daily, weekly or monthly and it goes on its own: fill rates, duplicate values, format patterns and the rest.
Monitor
Data quality, over time
The same analyses run daily, weekly or monthly, and every result is kept. That history gives each field a trend line, so you see the direction and what changed since the last run.
What changes when you monitor data quality
Before Plauti
- Someone has to remember to run the check, and nobody is sure when it last ran.
- A fill rate drops after an import, and nobody notices until a campaign skips every record with a blank field.
- Whether data quality is improving is a matter of opinion in the meeting.
- You can't say how big the problem is, or where to start.
WithPlauti
- It runs itself on your schedule, so nothing is forgotten and the number stays current.
- You get alerted when quality drops, and the drop shows on that field's trend line.
- A chart of the full history. Nothing left to argue about.
- A report that says how bad it is, which problems repeat, and what to fix first.
What monitoring gives you
Set the frequency once per object. Every result is kept.
See whether a field is getting better or worse
Every run adds a point to that field's graph. You get the latest value next to how much it moved since the last run, over any date range you pick.
You decide what counts as good
Set good, warning and critical levels as percentages. Anything that crosses one is flagged amber or red, and any field can have its own levels.
A report you can send to leadership
A PDF per object in plain language: its quality score, the change since the last job, and a card per problem naming the field, the metric and what to do about it.
Alerts when quality drops
Get alerted when something needs your attention.
See what's broken. Fix what matters. Prove it's working.
A trend line, not a single reading
See which way your data quality is moving, not just where it sits today.
Good and bad, by your rulebook
You set the thresholds per field. Anything crossing the line turns amber or red. Alerts when quality drops (coming soon).
Everyone looks at the same picture
One place to look, so nobody argues about whose numbers are right.
Proof the cleanup actually worked
Compare this month with last, and the improvement is something you can show rather than claim.
Does it work the way you need it to?
You set how often it runs
Daily, weekly or monthly, chosen per object. The objects that change fastest can run the most often.
- A frequency per object, not one for everything
- Put it in the quiet hours
You set how much history it keeps
Keep enough runs to see a trend. Older results clear out on their own, unless you switch that off.
- Retention set in days
- Purge results yourself by field, type or date
It reads your data, it doesn't rewrite it
Results live on their own records. Nothing lands on yours unless you ask for it.
- Results stored separately by default
- Writing scores onto records is opt-in, per object
Your permissions still apply
Every calculation runs under the permissions and field security you already have.
- Existing permissions and field security respected
- What someone can see follows their own access
See a scheduled run for yourself.
Interactive product tours. No install, no sales call.
Monitor for SalesforceMonitor data quality
Continuously monitor your CRM data health and get alerts when something needs your attention.
Monitor for SalesforceCheck your data quality score
Every object and field, rated out of 100.
Customer quote
AI readiness
"I think my biggest fear with bad data and AI is that it's going to give poor recommendations or results. There's nothing worse than implementing a project, rolling it out, and then it not doing as well as you expected. Then you get mistrust, and you have to work twice as hard to convince people it's fixed."
Alex Casey — Sales Technology Innovator
Wedgewood Weddings
Frequently asked questions
We just finished a big cleanup. Why keep monitoring?
A cleanup is a point in time. With nothing watching, the same fields drift back and the work gets done twice. Monitoring is what tells you whether the fix held, and which fields are sliding back before the next project inherits them.
We already use an AI assistant to scan our org. Why do we need this?
An assistant answers the question you ask, and you cannot ask what you do not know. It also cannot tell you what a field's fill rate was a month ago unless someone stored that answer somewhere. Monitoring runs the same checks on every field you picked, keeps every result, and holds your own thresholds for what counts as good or bad. And because each result is stored as a record, an assistant can read the answer back instead of scanning the whole org again every time someone asks.
Can I schedule one analysis more often than the rest?
No. A schedule covers every analysis type and field enabled on that object. To keep something out of the recurring run, disable it on the object first. Frequency is per object, so different objects can run at different rates.
When do the numbers update?
After each run, and only then. Saving a record does not change them, so how fresh the numbers are comes down to how often you run it.
Does storing a score touch my records?
Not unless you switch it on. By default the score sits on its own record. If you do switch it on, every scored record is updated on every run, which changes its last-modified date and can set off automations and syncs.
How much storage does this use?
That depends on two things you set: how many objects you monitor, and how long you keep results. Results are stored on their own records, one per scored record, so more objects and a longer retention period mean more of them. Anything past the age you set is cleared automatically, and you can purge by field, analysis type or date range yourself.
What does a recurring run cost us?
It uses API calls. Run it in the quiet hours, keep most objects on weekly, and stagger them across different hours so the load spreads.
See how it works
Talk with one of our experts to see how that works for your use case.