Data and Application Modernization in the Age of the Cloud

Data modernization is key to unlocking the full potential of your IT investments, both on premises and in the cloud. Enterprises and organizations of all sizes rely on their data to power advanced analytics, machine learning, and artificial intelligence.

Yet the path to modernizing legacy data systems for the cloud is full of pitfalls that cost time, money, and resources. These issues include high hardware and staffing costs, difficulty moving data and analytical processes to cloud environments, and inadequate support for real-time use cases. These issues delay delivery timelines and increase costs, impacting the return on investment for new, cutting-edge applications.

Watch this webinar in which James Kobielus, TDWI senior research director for data management, explores how enterprises are modernizing their mainframe data and application infrastructures in the cloud to sustain innovation and drive efficiencies. Kobielus will engage John de Saint Phalle, senior product manager at Precisely, in a discussion that addresses the following key questions:

• When should enterprises consider migrating and replicating all their data assets to modern public clouds vs. retaining some on-premises in hybrid deployments?
• How should enterprises modernize their legacy data and application infrastructures to unlock innovation and value in the age of cloud computing?
• What are the key investments that enterprises should make to modernize their data pipelines to deliver better AI/ML applications in the cloud?
• What is the optimal data engineering workflow for building, testing, and operationalizing high-quality modern AI/ML applications in the cloud?
• What value does real-time replication play in migrating data and applications to modern cloud data architectures?
• What challenges do enterprises face in ensuring and maintaining the integrity, fitness, and quality of the data that they migrate to modern clouds?
• What tools and methodologies should enterprise application developers use to refactor and transform legacy data applications that have migrated to modern clouds?