What’s Possible, What to Consider, and Best Practice During Migration

When migrating to a new CRM, many firms take the opportunity to improve data quality, particularly around industries, practice groups, and current role information.

Below is an overview of the enrichment options available within Ezekia, along with important considerations around scale, data quality, and third-party restrictions.


1. Bulk LinkedIn Enrichment

A common question is whether LinkedIn data can be refreshed in bulk across an entire database.

LinkedIn’s official policies prohibit the use of its APIs for CRM enrichment, sales prospecting, or large-scale profile export unless under specific contractual partner agreements. As a result:

  • Mass LinkedIn enrichment jobs are not supported.
  • Profiles cannot be refreshed automatically at scale via API.
  • There is no background bulk LinkedIn update mechanism.

This is a restriction imposed by LinkedIn’s platform policies.


2. LinkedIn Browser Extension (Manual Enrichment)

Ezekia provides a LinkedIn browser extension that allows users to:

  • Parse publicly visible LinkedIn profile data
  • Update individual person records
  • Add new records directly from LinkedIn

Important notes:

  • This operates on a per-profile basis.
  • It does not use LinkedIn APIs.
  • It cannot be run as a bulk background job.

This is best suited for enriching priority or actively worked profiles rather than refreshing an entire legacy database.


3. AI Profile Enrichment (Built-In Feature)

Ezekia includes AI Profile Enrichment functionality directly within:

  • Person profiles
  • Company profiles

This feature uses publicly available information to enhance records.

Company Profiles

Company enrichment is typically stronger and more reliable, as organisations usually have websites and a broader digital footprint.

Person Profiles

AI can assist with summarisation and inferred categorisation; however:

  • It relies solely on publicly available data.
  • It operates on a per-record basis.
  • AI-generated outputs may occasionally contain inaccuracies (“hallucinations”), particularly where data is sparse or ambiguous.
  • Human review is strongly recommended before confirming updates.

For this reason, enrichment is intentionally designed with visibility and user oversight, rather than allowing mass automated background updates that could unintentionally compromise data quality.

AI enrichment is highly effective for improving key profiles — but is not intended for bulk reclassification of thousands of records.


4. Native Integrations

Ezekia integrates with several third-party data and sourcing platforms, including:

  • BoardEx
  • SourceWhale
  • ChatGPT
  • Other supported integrations

These integrations enhance workflow and data capture but are generally record-driven rather than designed for full-database refreshes.


5. HelloSky (Broader-Scale Enrichment Option)

HelloSky is a native integration that supports:

  • Advanced candidate search
  • Introduction of enriched profiles
  • Updating existing records where matches are identified

While primarily designed as a sourcing tool, it can assist with broader data refresh initiatives.

Key considerations:

  • Licensed per user (pricing set by HelloSky)
  • Only one licensed user is required to operate the integration
  • Updates occur based on matched profiles within search activity

This approach can support structured, batch-style updates (for example, by sector or industry cohort).


6. Field-Level Updates

Where integrations are used:

  • Field updates follow configured mapping rules.
  • Fine-grained “single-field only” refreshes depend on integration capabilities.
  • LinkedIn-specific API field refreshes at scale are not supported.


7. Managing Data Quality & Match Accuracy

When enriching data, especially at scale, it is important to consider:

  • Ambiguous matches (common names, outdated profiles)
  • Inferred categorisations
  • Overwriting legacy data without review

Best practice is to:

  • Run pilot batches (e.g. 50–100 records)
  • Review accuracy before scaling
  • Standardise taxonomy (Practice Group, Industry, Sector Specialisation) before enrichment
  • Use reporting to identify gaps post-migration

Data integrity should always take priority over speed of automation.


8. Future Developments

Ezekia is developing enhanced People Moves and Profile Enrichment capabilities.

This is currently in early development and not yet production-ready. Timelines are subject to change as best-in-class data providers are evaluated.

Recommended Approach During Migration

For firms migrating from another CRM:

  1. Migrate existing data as-is.
  2. Standardise taxonomy and field structure first.
  3. Identify gaps using reporting.
  4. Enrich strategically:
    • AI enrichment for priority profiles
    • LinkedIn extension for active candidates
    • Integrations such as HelloSky where broader refresh is required
  5. Pilot before scaling.

A structured, phased approach typically delivers better long-term data quality than attempting full-database automation.