The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints, but the focus in our market has been on acquiring technology, ignoring the larger IT landscape within which this technology lives and the data architecture that lies at its core. If one expects longevity from a platform, the architecture should be designed rather than accidental.
Architecture is more than just software. It starts from use and includes the data, technology, methods of building and maintaining, and organization of people. But what are the design principles that lead to good design and a functional data architecture, and what are the assumptions that limit older approaches? How can one integrate with, migrate from, or modernize an existing data environment? How will this affect an organization’s data management practices?
Mark Madsen explores design assumptions and principles to apply when building multi-use data infrastructure and walks you through a reference architecture to use as you work to unify your analytics infrastructure.
Mark Madsen is a research analyst at Third Nature, where he advises companies on data strategy and technology planning. Mark has designed analysis, data collection, and data management infrastructure for companies worldwide. He focuses on two types of work: the business applications of data and guiding the construction of data infrastructure. As a result, Mark does as much information strategy and IT architecture work as he does analytics.
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