Hosted by Eric Kavanagh (The Bloor Group).
Presentation by Dave Wells (The Eckerson Group).
Sponsored by Infoworks.
Today’s analytics use cases are highly data dependent. Analytic models, and especially AI/ML models—are data hungry. They need a reliable supply of trustworthy data to be useful and sustainable. Today’s data management systems are complex and challenging with data deployed across multiple cloud platforms in combination with on-premises databases. Multiple platforms and data silos are barriers to fast and reliable data supply for analytics. The dilemma for modern analytics is this paradox: We need more data and more kinds of data for advanced analytics. But more data and more kinds of data create barriers to operationalizing analytic models.
DataOps holds promise to resolve the dilemma, offering the ability to quickly build, deploy, and evolve reliable data pipelines and readily support deployment, operationalization, and evolution of analytic models.
You Will Learn:
– What DataOps is and how it works
– The challenges of cloud, multi-cloud, and hybrid data environments
– The complexities inherent in modern data pipelines
– Reasons for the high rate of failure to operationalize analytic models
– How DataOps addresses the barriers to operationalization
– Six myths of cloud data management and the realities of cloud
– How technology enables and supports DataOps