The MOVE Methodology provides practical frameworks to help you articulate your business requirements in a way that technical teams can understand. But even with the right frameworks in place, one of the most challenging aspects of any CRM implementation is data.
Many businesses begin CRM projects with the goal of consolidating their data. It is rarely as simple as it sounds. Data exists in multiple systems (spreadsheets, legacy databases, emails, AI interaction logs, and more), making it difficult to clean and standardise across the board.
The starting point
Review your data. Identify a common identifier that stitches customer records across all systems. Clean it before ingestion into your CRM. And focus your first phase on what is essential for managing relationships, not everything you hold.
Don't Bite Off More Than You Can Chew
In the initial phase, concentrate on core customer information. Use The MOVE Methodology frameworks to determine what data enables you to better understand and manage relationships. Social media insights, AI interaction data, and other enrichment layers can be incorporated later as your CRM evolves.
“During the first phase, many businesses encounter an uncomfortable truth: their data is incomplete, inconsistent, or simply wrong. This is normal. It is also fixable.”
Common Data Challenges to Expect
Integrating data from multiple sources is complex and time-consuming. Here is what most businesses run into:
Varied Data Formats
Data comes in diverse forms: spreadsheets, databases, emails, social media feeds. Standardising and harmonising these formats takes real effort.
Data Silos
Sales, marketing, and customer service often store data separately with different structures and owners. Breaking silos requires coordination across the business.
Legacy Systems
Outdated systems incompatible with modern CRM platforms may require custom development or manual uploads, adding complexity and room for error.
Incomplete Data
Missing information may require side projects to fill gaps before migration can proceed. Budget time for this — it is almost always needed.
Duplicate Records
Multiple source systems often create duplicate customer records. Merging them without losing valuable data sometimes requires external expertise.
Outdated or Incorrect Data
A gap analysis to identify the most accurate and current sources is almost always necessary before migration begins.
Migration and Integration
Migrating existing data into a new CRM platform is one of the most critical steps in phase one. Ensuring data integrity, minimising downtime, and staying compliant with data privacy laws all require careful planning, testing, and execution.
Your implementation partner earns their place here.
Skilled data engineering and IT resources are essential to navigate these challenges. Do not underestimate the effort involved, and do not skip the testing phase.
Keeping Your Data Clean After Migration
After investing time and resources into cleaning your data, the work does not stop at go-live. Data integrity is an ongoing priority, and it comes down to user accountability.
As part of your initial implementation, build a Business Rules Dashboard. This gives you visibility into data quality and ensures the rules you have established are actually being followed.
Business rules create trust. Trust drives adoption.
Define business rules
Set clear expectations for data entry and maintenance. For example, every opportunity in your CRM should have a valid close date.
Create monitoring reports
For every business rule, generate a report that tracks compliance. If users enter incomplete or incorrect data, the report surfaces it immediately.
Use validation and standardised fields
Avoid free-text fields where possible. Pick-lists and validation rules at the point of entry minimise errors and keep data consistent.
Two Common Ways Data Goes Wrong
Incorrect Data Entry
Fields left blank, or incorrect values entered. Validation rules and standardisation reduce this risk significantly.
Process Non-Compliance
Users deviating from established processes. Monitoring compliance with defined business rules catches this early.
For example: if every opportunity requires a close date, generate a report that highlights open opportunities with past close dates. It surfaces the issue so you can take corrective action before it compounds.
Data Flow Between Systems
If your CRM connects to other systems (such as an ERP), clarify where data corrections should occur.
Two-way data flow: Changes in your CRM can push updates back to other systems. Corrections can be made in either place.
One-way data flow: Corrections should be made in the source system to avoid being overwritten during the next data upload.
Getting this wrong creates conflicting records and erodes trust in your CRM.
Data is not a one-time migration project.
The businesses that get CRM right treat data integrity as an ongoing discipline, not a phase one checkbox.