Imagine if you had crystal ball to see the future of your migration before even one record is moved? Better yet, what if that crystal ball could help you see into all the legacy system data that can cause your migration to fail?
Well, Valiance now offers crystal balls in the form of Artificial Intelligence and Machine Learning capabilities with its recently announced Data Quality Services for Life Sciences (DQSLS).
In life sciences like all industries, data quality remains a critical concern. When migrating platforms, manual data entry, incomplete data, siloed systems, duplicates, unstructured sources and hidden data, leading business leaders seek smarter tools to make migrations faster, more accurate and with increased levels of security.
Valiance’s DQSLS leverages artificial intelligence and machine learning models to examine data quality of the source system and establish how well source data fits the target system constraints. DQSLS highlights quality issues that could jeopardize your migration, and is an important early step is assessing legacy system data suitability and migration readiness before moving to a new platform.
Data profiling shows the as-is state of data quality in the source system, a major step towards the creation of a more structured, consistent and reliable set of data in the target environment. It provides the basis for assessing which data to focus on for improvement, setting the stage for the data enhancement phase in your migration project.
New AI/ML features of Tru-Series address the common challenge of limited awareness of unstructured document content, which makes it difficult to identify relevant information, as well as extract that data for migration. Previous approaches required a substantial amount of manual effort, consolidating metadata, as well as the data itself.
Migrations are a complex undertaking and traditional testing approaches often fall short of today’s business and compliance requirements. The ensuing risk can easily result in costly errors. The question is: How sound are your current testing and sampling approaches?