HubSpot
Salesforce
Pipedrive
Intercom
MailchimpSee what’s actually in your CRM data, then clean it up.
Cleanse Data shows you every field’s health and every value’s variations: how many distinct job titles you really have, which fields are abandoned, and which picklists have gone feral, with counts, down to the record. Fix the simple things on the spot; send the rest to the right tool, one click away.
Distinct values
47
Records analyzed
12,684
A cleanup starts when every variation, empty field, and abandoned property becomes visible.
Cleanse Data turns “we know it’s messy” into an inventory of exactly what exists, how often it appears, and which records hold it, so every cleanup decision starts with evidence.
Your CRM shows records. A cleanup needs to see fields.
You know the Industry field is inconsistent. You do not know whether that means six variants or sixty, which ones dominate, or which are one-record typos. The usual audit exports everything, builds a pivot for one column at a time, and starts over when the data changes.
Cleanse Data flips the view. Every distinct value, its record count, and the records behind it appear together. The mess stops being a feeling and becomes a list.
How a CRM data cleanup starts
Inventory every field and its empty and unique-value counts. Explore the value distribution of the fields that look unhealthy. Fix small, inspected issues directly, then route larger standardization, bulk updates, inline edits, or deduplication to the module designed for that job.
Start above the data, with the fields themselves.
See every field on the object: label and underlying name, type, writability, unique-value count, and empty-value count. Export the full inventory to CSV for the fastest first-hour field audit an admin can run.
Picklist abuse. An Industry field meant to hold 15 values showing 200 unique ones announces a standardization project, and its size.
Abandoned fields. High empty-value counts flag fields nobody fills anymore and candidates for retirement or consolidation.
Twin fields. “Phone” and “Phone Number” with different internal names reveal the confusion before it damages a merge or import.
| Field label | Type | Unique | Empty |
|---|---|---|---|
| Industry | Text | 203 | 1,884 |
| Job Title | Text | 4,912 | 724 |
| Lifecycle Stage | Picklist | 9 | 86 |
| Phone Number | Phone | 7,502 | 4,981 |
| Legacy Segment | Text | 6 | 11,942 |
Every value in the field, ranked by how often it appears.
Select a field and every distinct value appears below. Sort most-common-first to see impact, or alphabetically to line up adjacent variants such as “Tech,” “Technology,” and “technology.” Filter first to explore one team, source, or date range.
| Record | Industry | Owner |
|---|---|---|
| Acme Systems | Technology | Alex Morgan |
| Orbit Labs | Tech | Priya Shah |
| Northstar | technology | Jamie Lee |
The two-minute fixes, done in two minutes.
Select inspected values or individual records and make a straightforward update without leaving the module. Turn every “Assistant Professor” into “Associate Professor,” delete known junk, or save the exploration as a template for the next review.
Inspection is the safety step. Update and Delete apply immediately after confirmation; this module does not add a separate preview step. Use it as a scalpel on values and records you just inspected. Wider changes belong in the preview-equipped modules below.
Selected values
Assistant Prof. · Asst Professor · Assistant Professor
Set Job Title to
Associate Professor
Diagnosis here. The right treatment, one click away.
Cleanse Data is the manual instrument for exploring your fields, hunches, and edge cases. Once the pattern is visible, continue in the module built to resolve it safely.
Dozens of variants of the same value
Map and standardize them with Transform Data.
Standardize field variations in one templated passA field that needs standardizing forever
Declare the correct values with Data Logic.
Turn recurring cleanup into continuously applied logicA flat change across a filtered set
Set, clear, or purge it with Bulk Operations.
Preview and automate a high-volume changeA handful of one-off oddities
Filter and fix them inline with Grid Edit.
Make visible record-level edits in a gridValues that reveal redundant records
Resolve the cause with Merge Duplicates.
Find, compare, and safely merge duplicate recordsWant universal checks to run automatically? Use Health Assessment for continuous, automated diagnosis; use Cleanse Data for the questions those checks do not ask.
Export the mess. Upload the standard.
Export a field’s Value Distribution and Insycle adds a Mapped column beside every value. Fill in the correct counterpart, upload the CSV as a Blueprint, and Data Logic applies the standard to existing and future records.
The list of variations you uncovered is the raw material of a standard: what exists is the first step toward deciding what should. One exported CSV and one Mapped column later, the exploration becomes the policy.
See how teams make the last cleanup the last one| Value | Count | Mapped |
|---|---|---|
| Technology | 3,842 | Technology |
| Tech | 846 | Technology |
| technology | 503 | Technology |
| Softwear | 68 | Software |
| Technolgy | 29 | Technology |
Replace one-column-at-a-time auditing with a live field view.
| Capability | CRM reports | Export + Excel pivots | Insycle Cleanse Data |
|---|---|---|---|
| Field inventory | Records only | Column headers, at best | Every field: type, unique values, and empty counts |
| Value variations | Only reportable fields | One pivot per column, rebuilt each time | Every distinct value, counted, sorted, and live |
| Drill to records | Separate report | VLOOKUP back to the export | One click from any value |
| Finding abandoned fields | No field-level inventory | Manual column by column | Sortable empty-value counts |
| Acting on findings | Leave and start over | Re-import and verify | Fix inspected records or route to the right module |
| Toward automation | , | , | Blueprint CSV with Mapped column included |
Explore, diagnose, and choose the right next action.
Explore database fields and values
Inventory the object, then drill into any field’s real contents.
Explore CRM fields and valuesFix data inconsistencies
See the variants first, then choose the safest correction path.
Find and fix inconsistent CRM dataConsolidate legacy fields
Use empty counts, internal names, and content differences to find retirement candidates.
Consolidate and retire legacy fieldsIdentify incomplete records
Find the fields and values where missing data is concentrated.
Identify incomplete CRM recordsDeclutter low-quality data
Inspect junk patterns and the records behind them before purging.
Declutter and purge low-quality recordsStandardize job titles and industries
Map the variations you discover into consistent values at scale.
Standardize CRM values with Transform DataTurn variations into a standing standard
Convert the field’s current contents into an applied Blueprint.
Apply a lasting standard with Data LogicGive the new admin, or the experienced operator, the field-level view the CRM never provided.
Use an approved customer quote about finally seeing what the database contained, completing a fast audit, or turning an undefined cleanup into a concrete plan.
Approved customer proof
Audit or visibility outcome, role, company, and source
Server-rendered G2 proof
Rating, review count, and source link
Where should a CRM data cleanup begin?
How do I see all the values in a CRM field?
Select the object and field in Cleanse Data. Value Distribution lists every distinct value with its record count and lets you open the exact records behind one or several values.
How do I find unused fields in my CRM?
Review Field Statistics and sort by Empty Values. Fields with very high empty counts are likely candidates for investigation, retirement, or consolidation; compare their labels, internal names, types, and actual contents before making the decision.
How do I know which field variations to standardize?
Use the value counts to prioritize. High-volume variants are strong candidates for a saved mapping in Transform Data or a continuously applied Data Logic standard. One-off typos may be faster to fix directly after inspection.
Can I preview updates or deletions in Cleanse Data?
Cleanse Data applies an Update or Delete after confirmation without a separate preview step. Use it only for values and records you inspected directly. Route larger changes to Transform Data or Bulk Operations, which are designed for previewed work at scale.
Connect your CRM and see every field’s real contents in minutes.
Inventory the fields, count the variations, and open the records behind every value.
From exploration to enforcement
The variations you found are a standard waiting to be declared.
Turn the field’s current values into a Blueprint, then apply the correct result continuously with Data Logic.
See how exploration becomes enforcement