Improve trust in your data and fast-track the cloud migration leveraging AI-powered Data Quality Management (DQM)
Businesses across the globe have accelerated the adoption of cloud. According to Gartner, 75% of all databases will be deployed or migrated to the cloud by 2022. Businesses migrating their on-premises data to the cloud want to take advantage of greater efficiency, scalability, and performance.
But achieving these benefits is unlikely if the data being migrated is not trustworthy. What if the data quality is lost in migration? What happens if the data quality is poor in the first place and the same data is migrated to cloud?
For the service providers in the Connectedness industry, the data quality challenges impacts both business and customer experience to a much larger extent. The legacy applications rarely have complete, consistent, and correct data. This leads to flawed decision making and impacts various functions such as service delivery, fault management, billing, and revenue assurance and many more. Fixing data quality issues, a time-consuming task, that often leads to slippage of project timelines planned for the cloud migration.
Service providers need a holistic data quality strategy and an automated and robust data quality management framework to ensure the migrated data is trustworthy and accelerate the cloud data migration.
Regardless of the tools or technologies used to tackle data integrity issues, discrepancies still happen. This issue must be fixed during the manual registration process to have a
high-quality inventory data.
