5 Major Approaches to Improve Data Quality During ERP Migration

When migrating data into a new ERP system like Dynamics 365 F&SCM, data quality can make the difference between a smooth transition and a problematic implementation. Here are five comprehensive approaches to ensure your data serves as an asset rather than a liability:

1. Thorough Data Profiling and Assessment

Start with a complete understanding of your existing data landscape before attempting migration. This means examining your data in depth to identify patterns, anomalies, and quality issues.

Think of this as creating a detailed map of your current data territory. You'll want to analyse completeness (are there missing values?), uniqueness (are there duplicates?), consistency (does the same information appear differently across systems?), accuracy (does the data reflect reality?), and conformity (does it follow required formats?).

For example, if you're migrating customer records, you might discover that customer addresses are inconsistently formatted or that 30% of customer records are missing critical tax information. This initial assessment provides the foundation for all subsequent quality improvements.

2. Establish Clear Data Standards and Governance

Before migration, define precisely what "good data" looks like for your organisation. This means creating comprehensive data standards documents that specify required formats, allowable values, naming conventions, and business rules.

This is similar to establishing the rules of the road before beginning a journey. Everyone needs to understand and follow the same guidelines to reach the destination safely. Your standards should address questions like: What is the required format for product codes? How should customer names be structured? What validation rules should apply to financial records?

Alongside these standards, implement a governance structure with clearly defined roles and responsibilities. Who will be accountable for the quality of vendor data? Who has authority to approve exceptions to the standards? Having this framework in place before migration begins prevents confusion and ensures accountability.

3. Implement Multi-Stage Data Cleansing

Rather than attempting to fix everything at once, approach data cleansing as a series of targeted operations, each with specific goals and quality metrics.

Think of this like renovating a house room by room, rather than attempting to fix everything simultaneously. You might begin with automated cleansing to handle straightforward issues like standardising formats and removing obvious duplicates. Then move to more complex cleansing that requires business knowledge—like resolving conflicting customer information or standardising product classifications.

For example, in the first phase, you might focus on standardising all address formats and correcting postcode errors. In the second phase, you might deduplicate customer records using sophisticated matching algorithms. In the third phase, you might enrich the data with additional information needed for the new ERP system.

4. Design and Execute Comprehensive Validation Rules

Develop a multi-layered approach to validation that catches errors before they enter your new system. This typically includes:

• Technical validation: Does the data conform to required formats and structures?

• Business rule validation: Does the data make logical sense from a business perspective?

• Cross-field validation: Are related data elements consistent with each other?

• Cross-system validation: Does the data align with information in other systems?

  
  

This comprehensive validation operates like a series of increasingly fine filters, each catching different types of problems. For instance, technical validation might catch that a date field contains text, while business validation might flag that a purchase order date falls before the vendor was established.

Importantly, validation should be automated where possible and integrated into your migration tools to prevent manual errors and ensure consistency.

5. Implement Iterative Migration Testing Cycles

Rather than conducting a single "big bang" migration, implement multiple test migrations that allow you to identify and resolve issues progressively.

This approach mirrors how software is developed—through repeated cycles of testing and refinement. Begin with small sample migrations to test your processes and tools. Then expand to larger, more representative data sets. With each iteration, track quality metrics to measure improvement.

For example, your first test might migrate just 100 customer records to verify your basic approach. The second might include 1,000 records to test your deduplication logic. The third might include all customers to verify performance at scale. Each cycle provides an opportunity to refine your approach before committing to the final migration.

During these iterations, be sure to involve business users in validating the migrated data. Their domain expertise often uncovers subtle issues that automated tools might miss, such as recognising that a particular customer has been incorrectly classified.

By adopting these five approaches, you'll not only improve the quality of data entering your new ERP system, but you'll also establish the foundations for ongoing data management excellence that will serve your organisation well beyond the initial implementation.