CRM Data Migration: Guide to Keeping your Data Clean

For organizations to streamline their operations, improve customer insights, and make better decisions, they need to migrate data between various CRM systems and other applications. Yet, the success of these undertakings crm data migration best practices often depends on whether data integrity remains intact throughout migration and beyond; in addition to their consistency and accuracy. Therefore, this article discusses some best practices aimed at conducting a quality test after the conclusion of a data migration attempt with a focus on keeping up with the data reliability as well as achieving maximum system performance. Quality Assurance Testing is vital for maintaining data integrity, consistency, and accuracy between CRM (source) and target applications.

Introduction

Today’s businesses are driven by data-centric insights that fuel growth, improve customer experiences as well as maintain a competitive edge. One way of managing such data properly entails migrating it from legacy systems or diverse sources into central platforms like CRM systems. Inappropriately managed, however, data migration can lead to increased efficiency and enhanced accessibility to information but also comes with potential risks like loss of information, inconsistency, or inaccuracies.

Post-data migration; they must implement strong quality checks to ascertain the completeness; accuracy as well as consistency of migrated data. Such quality checks not only focus on how much information was carried through but also at different levels in a database where inconsistencies exist and may simply be exposed during mapping or identity resolution processes

Understanding the Importance of Quality Checks

Quality checks post-data migration serve as a safeguard against potential data discrepancies that could impact business operations and decision-making processes. By verifying data integrity, organizations can mitigate risks associated with erroneous data that could lead to financial losses, compliance issues, and diminished customer trust. Moreover, conducting thorough quality checks facilitates seamless integration between CRM systems and other applications, enabling efficient data exchange and synchronization across platforms.

Challenges on Post Data Migration Between CRM and Other Applications

Data Integrity and Quality Challenges

• Data Loss or Corruption
  • Data might be lost or corrupted during migration due to technical errors or incomplete data transfer.
• Inconsistent Data Formats
  • Differences in data formats between source and target systems can result in data mismatches.
  • Differences in data formats or structures between the source and target systems can lead to mismatches or inconsistencies.
• Duplicate records
  • Unintentional duplication of records during migration. Migrating data may result in duplicate records if not properly managed, leading to confusion and errors.

Best Practices for Quality Checks Post Data Migration

Here are some best practices for quality checks after migrating data between your CRM and other applications.

• Data Reconciliation
  • Reconcile the data between the source (CRM) and target applications to ensure that all records have been accurately migrated. That can be done through
    • Record Counts
    • Field-by-Field Comparison
• Data Correction
  • Once found the data inconsistencies try to correct those data after verifying.
• Comparison report
  • Generate comparison reports to check how many data are the same or different.

Implementing a Comprehensive Data Comparison Solution

In addition to establishing a robust data quality framework and implementing automation tools, integrating a specialized data comparison tool such as DBSync Compare can improve accuracy and the effort in optimizing post-migration analysis has increased dramatically. 

Check out this whitepaper on why data comparison is important ->

Here is how DBSync Compare can be used to optimize data matching and comparison:

Introduction to DBSync Data Compare 

DBSync Data Compare is a powerful tool designed to compare and synchronize data between source applications and databases. It provides a well-designed user-friendly interface and robust functionality for better monitoring, data validation, and detection of discrepancies after data migration and data correction.

Basic features of DBSync Data Compare 

Profile

DBSync Data Compare profiles keep connection settings for source apps and databases. They specify which objects and tables need syncing allowing for multiple source and target database instances to fit different integration requirements. 

Source and Destination Configuration

The software helps users set up and check CRM & database connections. It uses separate tabs for source and destination info. This makes sure data retrieval and comparison are safe and dependable.

Mapping

Users can tailor how the tool compares data between source and destination tables. They can map fields using unique identifiers (like organization IDs or keys or fields) to get accurate comparison results.

• Auto Match

Upon selection of auto match will automatically select the primary field/object from the source & destination connector.

• Fetch Latest Data 

Upon selection, the server retrieves data from the source and target systems and converts it into CSV (Comma-Separated Values) format. This format ensures compatibility and ease of manipulation for subsequent comparison and analysis tasks. It also makes sure the format of the source & target data are the same.

Data Comparison

Overview of the Data Comparison Workflow

• Maps and selections 

Users access a data comparison screen that displays a selection box listing all mapped objects from the mapping screen. By selecting individual mapped objects, users initiate a request to the server, and the downloaded CSV data streams are loaded into memory and compared using the library. This library provides efficient utilities for comparing large datasets, enabling quick identification of differences, similarities, and data inconsistencies between the source and target records.

• Exclude columns

The solution includes a feature to exclude specific columns from the comparison process. Users can select columns they wish to exclude, and these columns will be omitted from both the source and destination datasets during the data comparison phase. This ensures that only relevant data attributes are considered, streamlining the comparison process and focusing analysis on critical data points.

• Analysis and description of results

The results of the comparison are presented in tabs:

All: Displays all records from the source and target systems.

Source Only: Prints records related to the source system only.

Only in Target: Highlights records exclusive to the target application.

Same: Displays records with the same data in the source and target.

Different: Flags records with differences between source and target data structures.

• Interactive Update Options

Users can directly update a specific row in the comparison interface. By selecting a specific row and clicking “Update Source” or “Update Destination”, the changes can be applied to the source CRM or target application. Updated reports are then downloaded and merged back into a CSV file for the next comparison cycle.

• Iterative Comparison and Result Refinement:

After updating records, the comparison process re-evaluates the merged datasets to reflect the impact of recent changes. This iterative approach ensures that all updates are accurately reflected in the final comparison results, providing a comprehensive view of data alignment post-migration.

Logging and Progress Tracking

Comprehensive logging capabilities provide real-time status updates and detailed progress reports during data comparison. This transparency ensures visibility into the comparison process and facilitates timely issue resolution.

Case Study: 

Using DBSync Compare to Improve Quality Assurance Picture a situation where a big company moves customer information from old CRM systems to one main Salesforce setup. By using DBSync Compare, the company checks millions of records step by step, makes sure complex data connections are right, and helps different regional branches work together. This method doesn’t just make the data more trustworthy – it also helps the company make better choices and work more.

Conclusion: 

Adding a strong data comparison tool like DBSync Compare to your quality control system is key to lowering risks tied to moving data. By using its high-level features to profile, map, and compare data in real-time, companies can keep their data accurate, make their work smoother, and build trust with those who have a stake in the business in today’s world where data drives everything.

Meet with our experts to see how DBSync can help you migrate data with accuracy