Best Practices for Maintaining Data Integrity During Data Migration

Michelle GenserMarch 18, 2020

Case Study: Accelerating Data Migration

As digital transformation continues to drive business, organizations are moving away from legacy IT tools and upgrading to modern data center applications to help drive value for their customers. As this transition occurs, organizations need a fast and efficient approach to data migration that ensures business continuity and protects data integrity throughout the entire process.

Migrating legacy data to a new source system, whether on premise, in the cloud or in a hybrid environment, is often a source of data errors because many applications format data differently. At the most elementary level, the legacy system might take [First Name] [Last Name] data, while the new system takes [Last Name], [First Name] data. Even the use of something as small as a comma could have a negative effect on the migration. Ensuring data integrity, as it migrates from old system to new, is fundamental to sustain an organization’s business structure, while protecting them–especially in complex regulatory environments.

With so much data at stake, data migration is often a large and complex project. Companies require a scalable approach to data quality to identify errors, uncover core causes, prioritize fixes and populate new systems with high-integrity information.

Solving Data Quality Challenges for Data Migration

To better manage data migration projects and continuously improve business results, organizations need an integrated data quality powered data governance solution. With integrated capabilities that include data quality, data governance, metadata management, data catalog and data analytics, companies can quickly migrate diverse data sets, easily uncover analytical insights and monitor business operations to discover data irregularities, exceptions or patterns. Providing this type of audit trail is critical to verify regulatory compliance, find business improvement opportunities and guard against risks. 

When integrated, data quality and data governance allow businesses to complete multiple data migration projects smoothly. By analyzing data for accuracy during data migration from several legacy systems into a modern, centralized system, organizations can catch data errors, identify the original causes of errors and fix issues before they impact the customer experience or adversely affect regulatory compliance.

Quality-Powered Data Governance Benefits During Data Migration 

A traditional data migration approach uses a SQL process to translate formats from the legacy system into the new system. However, SQL-based data migration takes months, whereas integrating data governance and quality can be completed in a much shorter time frame. Here’s why:

  • SQL processes struggle to handle big data. An integrated solution swiftly processes billions of transactions, while allowing the organization to maintain existing investments in specialized business applications and systems.
  • Flexible data management capabilities enable analysts to integrate multiple data sources, change business logic faster than traditional tools and anticipate business trends.
  • Continuous data quality monitoring exposes data anomalies and patterns to guard against risks and uncover opportunities for business improvement.
  • Faster root-cause analysis helps foster rapid data quality resolution with zero impact on the customer.
  • An easy-to-use, visual interface provides transparency into analytic logic and data, enabling analysts to generate trustworthy results, and ultimately, trust in data quality.

With a modern approach to data migration, organizations enable an accurate data transformation on a massive scale with verifiable results.

As a result, businesses reduce time, cost and effort compared to traditional SQL-based migration methods, while establishing visual assurance of data and process integrity so stakeholders across the enterprise can trust and utilize their data.

Are you looking for additional information about managing a data migration project effectively, efficiently and accurately? Check out this case study above and below:

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Case Study: Accelerating Data Migration