DATA QUALITY DEDUPLICATION

Dynamics 365 data cleanup: duplicates removed, data quality fixed, reporting you can trust

When reps stop trusting the CRM, they work around it — and every workaround creates another duplicate, another stale record, another reason the pipeline number in the board deck doesn't match what sales actually believes. DesertCRM provides direct senior Dynamics 365 CE expertise to fix the data and the process creating the mess. Phoenix, AZ · remote US-wide.

What it is: a data-quality assessment and cleanup of your Dynamics 365 CE environment — duplicate records, data-model drift, validation gaps, and the integrations creating duplicates in the first place. Who it's for: organizations where pipeline and reporting numbers are no longer trusted, storage keeps growing, or an import years ago left data nobody's cleaned up since. How it starts: a Health Check — fixed scope, 5 business days, $1,500 — focused specifically on data quality. What you get: a table-by-table data-quality profile, a merge/dedupe strategy, and a roadmap for fixing it through a Stabilization Sprint or Fractional Administration.

Health Check scoped to Data Quality · $1,500, fixed · 5 business days

Signs your data has drifted past what anyone trusts

Data-quality problems compound quietly — each import, integration cycle, and bypassed required field adds a little more that nobody notices until the pipeline review turns tense.

Duplicate accounts, contacts, and leads

Repeated imports and integrations have created multiple records for the same company or person, with activity history split across them.

Duplicate detection rules unpublished

Rules exist in the environment but were never published or activated, so they've provided zero protection since the day they were created.

Option sets drifted

Dropdown values were added ad hoc over time — near-duplicate entries, deprecated options still selectable, no consistent naming.

Required fields bypassed by imports

Bulk imports write directly to Dataverse and skip form-level required-field and business-rule validation entirely.

Stale opportunities inflating pipeline

Open opportunities that should have closed months ago are still sitting open, distorting forecast and pipeline totals.

Orphaned custom tables

Custom tables built for a process that no longer exists are still present, still holding data, and still confusing anyone building a new report.

Inconsistent lead source and campaign fields

The same channel is recorded under several different values across years of records, making attribution reporting unreliable.

Dashboards nobody trusts

Leadership has quietly stopped relying on the CRM dashboards and reverted to a manually maintained spreadsheet instead.

Storage capacity growing unexplained

Attachments, audit log history, and async job history are accumulating quietly and eventually forcing a storage or licensing decision.

Cleanup that addresses the source, not just the symptom

A one-time dedupe pass without fixing what's creating duplicates is temporary — the same records reappear within a few sync or import cycles. The approach treats both.

  • Data-quality assessment — table-by-table profiling of duplicate rate, required-field compliance, and stale-record volume
  • Merge/dedupe strategy — native Dataverse merge, duplicate detection jobs, and bulk processes, sequenced and tested before running at volume
  • Data-model rationalization — orphaned tables and drifted option sets reviewed against what's actually in use
  • Validation rules — business rules and required-field enforcement tightened at the form and record level
  • Integration-side fixes — the import or sync process that's creating duplicates corrected so cleanup holds
  • Storage hygiene — attachment, audit-log, and async-job retention reviewed and right-sized
  • Reporting rebuild — dashboards rebuilt on validated, deduplicated fields instead of patched on top of data nobody trusts

Honestly out of scope: cleanup without fixing the source of duplicates is temporary. If the integration or import process isn't corrected, the same records reappear — the engagement includes that fix specifically so the cleanup holds.

Health Check → Stabilization Sprint → Fractional Administration

The same funnel as every DesertCRM engagement, scoped to data quality.

1

Health Check, focused on data quality

Fixed scope, 5 business days, $1,500. The audit weights duplicate profiling, data-model drift, and reporting trust rather than spreading evenly across every CE module.

2

Stabilization Sprint

Scoped after assessment. Implementation of the merge/dedupe strategy, validation rules, and integration-side fixes as a fixed-price project confirmed in writing before work starts.

3

Fractional Administration

Ongoing senior ownership of data quality — monitoring duplicate rates, reporting integrity, and storage hygiene over time. See Fractional Administration.

How records get merged, at what scale

Situation Method Notes
Small number of known duplicate pairs Native Dataverse merge Preserves activity history on survivor
Table-wide duplicate backlog Duplicate detection jobs + bulk merge Run against tuned detection rules
Ongoing duplicate prevention Published, tuned duplicate detection rules Applied at create/update time
Duplicates from an integration Integration-side matching fix Stops new duplicates from being created

For Microsoft partners: bring DesertCRM in white-label for a data-quality engagement your team doesn't have the bench for. See Partner Staff Augmentation.

Discuss Partner Capacity

Data cleanup questions, answered straight

If you clean up duplicates now, won't they just come back? +
Yes, if the source is left untouched. Cleanup without fixing the integration or import process that's creating duplicates is temporary — the same records reappear within a few sync cycles. The engagement addresses both: the existing duplicate backlog, and the duplicate detection rules and integration logic that should have prevented them.
Will merging duplicate records lose data or break history? +
Native Dataverse merge is designed to preserve activity history, notes, and related records on the surviving record. The merge/dedupe strategy is planned and tested before it's run at volume, with a clear rule for which record survives a given match.
Our duplicate detection rules exist but were never published. Is that common? +
Very common. Duplicate detection rules that were created but never published or activated provide no protection at all — the system behaves as if they don't exist. Publishing and tuning them correctly is usually one of the fastest wins in a data-quality engagement.
Can you fix dashboards that report the wrong numbers? +
Yes, but the dashboard is rarely the actual problem — it's usually reporting accurately on data nobody trusts. The engagement rebuilds reporting on validated, deduplicated fields rather than patching the dashboard on top of unreliable data.
Do you address storage growth from attachments and audit logs? +
Yes. Storage hygiene — attachment volume, audit log retention, and async job history — is reviewed as part of the engagement, since it's often growing quietly and eventually forces a licensing or storage-tier decision.
How is this different from the general D365 Health Check? +
The general Health Check covers data quality as one of several review areas. This page describes the same engagement structure — Health Check, Stabilization Sprint, Fractional Administration — scoped and weighted specifically toward duplicates, data-model integrity, and trusted reporting.

Ready to find out what's actually in your data?

A data-quality-focused Health Check: 5 business days, $1,500, fixed. Fee credited toward Fractional Administration started within 30 days.

Direct senior Dynamics 365 CE expertise