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Migrating to a new Salesforce org is one of the most consequential decisions in a CRM programme. The default instinct — bring everything — is also the fastest way to replicate five years of dirty data, dead contacts, and stale opportunities into a system that was supposed to give the revenue team a clean start. Before uploading a single record, three questions determine whether a dataset belongs in the new org or in an archive. The answers will shape the performance, usability, and compliance posture of the CRM from day one.
A new Salesforce org is a high-performance operational environment. It is not a data warehouse, a compliance archive, or a historical record-keeping system. When organisations migrate indiscriminately — importing all Opportunities, all Contacts, and all Activity history regardless of age or quality — they introduce what practitioners call “dark data”: records that no one queries, that slow down page loads, that clutter list views, and that introduce noise into reports and forecasts.
Dark data also degrades AI-driven features. Salesforce Einstein and other forecasting tools derive their models from the data in the org. A training dataset polluted with five-year-old closed-lost Opportunities at outdated stages, with missing fields and dead contacts, produces forecasts that are systematically less reliable than one trained on two years of clean, well-structured data.
The discipline required is not technical — it is decisional. The question is not “can we migrate this?” but “does this data drive an action or an insight that the revenue team needs in the next 12 months?”
Apply three sequential tests to every dataset before migration. A dataset that fails any one of the three tests does not belong in the new Salesforce org.
Historical data earns its place in the new org if it supports one of two specific use cases: year-over-year trend reporting that the sales leadership team actively uses, or AI-driven forecasting models such as Salesforce Einstein that require historical training data. If the dataset does not directly serve either of these purposes, it belongs in a flat-file archive or a data warehouse — not in the operational CRM.
This test eliminates the largest category of unnecessary migration: closed-lost Opportunities from three or more years ago that are neither referenced in active reports nor required for forecasting. They represent real commercial history, but not operational value. Move them to a data warehouse where they remain queryable without degrading Salesforce performance.
Dirty data migrated into a clean org is not a migration problem — it is a data quality problem that has been relocated. Records with missing required fields, outdated pipeline stages that no longer map to the current sales process, contacts whose email addresses bounce, or Accounts without an assigned owner all represent pre-existing quality failures. Moving them into the new org imports those failures directly into the environment that the sales team is being asked to trust and adopt.
Assess each dataset honestly: if the effort required to clean the data before migration exceeds the operational value of having that data in the new org, leave it behind. Archive the raw file. The organisation retains access to the information without paying the ongoing performance and usability cost of carrying dirty records in an active CRM.
Some industries and geographies impose mandatory data retention periods. Financial services, healthcare, and certain enterprise B2B segments may require seven or more years of commercial records to be retained in a searchable format. This is a legal obligation that cannot be resolved by deletion.
However, retention and migration are distinct decisions. An organisation can satisfy a seven-year retention requirement by storing historical data in a searchable external database or a dedicated compliance archive, without loading that data into the operational Salesforce org. This approach maintains full compliance, keeps the historical record accessible for audit purposes, and preserves the performance and usability of the live CRM environment. Retention does not equal migration.
For a B2B software or tech company migrating to a new Salesforce org, the practical scope that balances operational value against data quality risk is:
The rule of thumb is two years of clean, relevant history for most B2B sales organisations. Two years of Opportunity data is sufficient for year-over-year reporting, quota attainment benchmarking, and forecasting model training. It is not so extensive that migration becomes a multi-month data cleaning project.
Quality over quantity is not a compromise — it is the standard that makes the new CRM worth adopting.
Data excluded from migration requires a documented archiving strategy, not deletion. The options are:
Flat-file archive: Export records to CSV or structured format, store in a secure location (internal file server, cloud storage, or data warehouse), and document the archive location in the CRM migration record. Suitable for data with low query frequency and no compliance requirement.
Data warehouse: Load historical Salesforce data into a dedicated data warehouse (Snowflake, BigQuery, Redshift) where it remains queryable by analysts without affecting operational CRM performance. Recommended for organisations with active BI reporting or compliance obligations requiring searchable historical records.
Dedicated compliance database: For regulated industries with mandatory retention periods, a purpose-built compliance archive with access controls and audit trail is the appropriate solution. Salesforce itself offers data archiving solutions for this purpose.
The migration log should document, for each excluded dataset: the reason for exclusion (failed Utility Test / Data Integrity Check / Compliance Boundary), the archive location, and the responsible owner for that archive.
How far back should I migrate Opportunity data to a new Salesforce org?
Two years of closed Opportunity data is the standard recommendation for most B2B sales organisations. This provides sufficient history for year-over-year reporting and forecasting model training without introducing large volumes of stale records. If your forecasting tool requires longer training data, extend to three years but only for closed-won Opportunities with complete field data.
What should I do with leads and contacts that have bounced email addresses?
Do not migrate them. Bounced contacts represent dead data — they cannot be contacted, cannot be used for email campaigns, and add noise to list views and reports. Export them to a flat-file archive before migration. If a bounced contact is associated with an active Account that is being migrated, create a note on the Account record indicating the contact’s status rather than migrating the contact record itself.
Is it acceptable to migrate data and then clean it in the new org after migration?
This approach is strongly discouraged. Cleaning data after it has been migrated into a live Salesforce org is slower, more disruptive, and more error-prone than cleaning before migration. Users working in the new org encounter dirty records during the cleaning period, which undermines adoption. Clean the data in a staging environment or using a transformation tool before loading into the new org.
How do I handle data retention obligations when migrating to a new org?
Identify the specific retention periods that apply to your industry and geography before migration planning begins. For each dataset subject to a retention obligation, design an archiving strategy — external database, data warehouse, or Salesforce data archiving — that satisfies the obligation without requiring the data to be present in the operational CRM. Document the retention policy and the archive location in writing before the migration goes live.
What is “dark data” in a Salesforce context and why does it matter?
Dark data refers to records that exist in the CRM but are never queried, reported on, or acted on by the revenue team. It accumulates through indiscriminate data migration, CRM imports without a data governance policy, and historical records that predate the current sales process. Dark data degrades list view performance, introduces noise into pipeline reports, reduces the accuracy of AI forecasting tools, and creates cognitive overhead for reps navigating the CRM day-to-day.
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