Supporting analytics and data science in an enterprise involves more than building a data lake or using cloud services. Too often, the focus is on technology when it should be on data practices and management. This session discusses architecture principles, the relationship between governance and architecture, and the data architecture and data management needed to support multiple competing enterprise workloads.

The focus in our market has been on acquiring technology, and that ignores the more important part: the ecosystem within which this technology exists and the data architecture that lies at its core. If one expects longevity from data infrastructure, then it should be a designed rather than accidental architecture.

Architecture starts with uses and includes the data, technology, methods of building and maintaining, governance, and organization of people. What are the design principles that lead to good design and data architecture? What assumptions limit older approaches? How can one modernize an existing data environment? How will this affect data management? This session will help you answer these questions.

You will learn:   

• How data architecture constrains both governance and technology architecture
• Analytic workloads and their impact on data architecture and technology choices
• How to separate infrastructure from application in your data systems
• Tradeoffs and considerations in multi-use data infrastructure
• When to standardize data, how to curate it, and govern its delivery and use over time