Global data management is not a newly coined term. However, what it stands for is actually widening in scope particularly around data-in-motion and data-at-rest. Significant technology trends such as IoT, cloud, AI/ML, blockchain, and streaming data have given rise to excessive data volumes and also innovative use cases. The scope for global data management now extends all the way from ingestion, processing, storage, governance, security to analysis. With a good number of endpoints served through the cloud and major application footprints remaining on-premisess, it is pertinent to have a global data management strategy that supports hybrid models and more specifically, a multi-cloud model.
Many modern businesses struggle to balance the demands of rapidly innovating through new technologies like machine learning with the need to keep data safe and secure, all while responding to a constantly changing regulatory landscape. This puts data stewards, data engineers, architects, data scientists, and analysts under intense pressure as they must contend with existing and new applications, multiple logical and physical data stores and sources, diverse data types, and data spread across several deployment environments.
Attend this session led by Matt Aslett, Research Director at 451 Research and Dinesh Chandrasekhar, Director, Hortonworks to learn more about creating a framework for your enterprise that offers guidance on how to think about global data management—priorities, responsibilities, key stakeholders, compliance, and growth.