Searchlab CRM & Data Alignment
Your CRM is only as useful as the data inside it. This service restructures the object model, cleans and deduplicates historical records, and installs the validation and governance rules that stop decay. It is for teams who no longer trust their own exports.
We begin with a full extract rather than a walkthrough. Records get profiled for duplicates, blank required fields, malformed emails and phone numbers, inconsistent country and state values, orphaned deals with no associated company, and contacts whose owner left the business. That profile becomes the cleanup scope, and it is almost always larger than the internal estimate.
Next comes the object model. Accounts, contacts, deals and activities each need clear ownership rules and a defined relationship structure. We remove custom fields nobody has written to in twelve months, convert free text fields into picklists where reporting depends on them, and standardise naming so a property called Industry means the same thing in every workflow and report.
Deduplication runs on rules you approve, not on a black box. We match on domain, email and normalised company name, produce a merge preview, and keep an audit trail of what was merged into what. Historical activity is preserved so pipeline history and reporting continuity survive the cleanup rather than resetting to zero.
Governance is the part most projects skip. Required field logic, validation rules, restricted picklists, permission sets and a documented data dictionary all get built. We also define who owns each field, who can create new ones, and what the request process looks like, because uncontrolled field creation is how a clean CRM turns messy again inside two quarters.
Everything in CRM & Data Alignment
Data quality audit report
A scored breakdown of duplicates, incomplete records, stale deals and field usage across your CRM, with a prioritised cleanup plan.
Object and field restructure
Rebuilt property architecture across accounts, contacts, deals and activities, with unused fields retired and reporting fields standardised.
Deduplication and merge run
Rule based matching with a merge preview you approve, plus a full audit log so every merge can be traced afterwards.
Data dictionary
A living document defining every field, its purpose, allowed values, owner and the report or workflow that depends on it.
Validation and required field rules
Input controls that stop malformed data at entry, applied to the fields that actually feed reporting rather than to everything.
Integration mapping
A documented map of which system writes to which field, so two tools never overwrite each other and sync direction is explicit.
Permission and ownership model
Role based access, record ownership rules and reassignment logic for departures, territory changes and team restructures.
Governance playbook and training
Written rules for field creation, data entry standards and quarterly hygiene checks, delivered with a live session for your team.
The process
Extract and profile
We pull a full data export and profile it for duplicates, completeness, format errors and orphaned records. This produces a measurable baseline you can compare against later.
Design the model
We redesign the object and field architecture around what your reporting needs, agree on the system of record for each data type, and get written sign off before touching production.
Clean and migrate
Cleanup runs in a sandbox first, then production in controlled batches with backups at each step. Merges, backfills and field migrations are logged and reversible.
Govern and hand over
Validation rules, permissions and the data dictionary go live. We train the team, set a quarterly hygiene checklist, and name an internal owner for the model.
Questions about CRM & Data Alignment
Will cleanup delete data we might need later?
Nothing is hard deleted without approval. We archive rather than remove, take a full backup before every batch, and keep merge logs so any record can be traced back. Anything ambiguous is flagged for your decision instead of being resolved by us.
How do you handle duplicates that are not exact matches?
Fuzzy matching on normalised company names and email domains catches most of them, but it produces a review queue rather than an automatic merge. You approve the ambiguous set. Exact matches on email or domain merge automatically once you sign off on the rule.
Can you clean data in more than one system?
Yes, and usually you have to. CRM, marketing automation and billing often hold conflicting versions of the same account. We pick a system of record per data type, define sync direction, and reconcile the rest against it rather than cleaning each tool in isolation.
What stops the data getting messy again?
Validation rules at entry, restricted picklists instead of free text, a documented field request process, and a quarterly hygiene check with a named owner. Tooling alone does not hold. The governance habit is what keeps quality steady after handover.
Ready to talk about CRM & Data Alignment?
Tell us where you are stuck. We reply within the hour on WhatsApp, usually sooner.
Or email Searchlabtools@gmail.com