Data Quality Use Case: How to Mitigate the Risk & Failure of Data Migration

Presented by

Maarten Masschelein, Mathisse De Strooper, Milan Lukac, at Soda

About this talk

Data migration is a complex process that involves transferring data from one system to another. Almost 85% of data migration projects overrun time and budget and more often than not, the root cause of these failures is a lack of investment in systems and processes that ensure reliable, high-quality data. When newly-migrated data appears to be inaccurate in a new storage system, end users are likely to distrust the data and go back to the old way of working and therefore, it is crucial for businesses to ensure a smooth and successful migration to avoid any potential risks and failures. Data reconciliation is crucial in the process of data migration. It plays a vital role in various industries, including finance, healthcare, and manufacturing, where large volumes of data are generated and analyzed, to identify and rectify discrepancies or errors that may occur during data collection, storage, or transmission. Data reconciliation also helps in maintaining compliance with regulatory requirements and in improving operational efficiency by streamlining data processes and reducing manual efforts, allowing employees to focus on other critical tasks. This 45-minute session contains: - Data migration use cases and best practices - A product showcase to test, manage, and assure data quality - Guidance on how to automate the reconciliation process to save time and resources
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Soda delivers end-to-end data quality management for the digital era where everyone needs to work with data they can trust to confidently make business decisions. By knowing when data breaks, providing real-time visibility, and enabling teams to find the root cause and prevent future data issues, Soda bridges the gap for data engineers and data consumers to collaborate and get ahead of data issues with full coverage, from observability to proactive data testing and prevention. With Soda, teams can take control of data quality and scale it across the organization, empowering everyone to leverage high-quality, reliable data for business success.