Category: Data Modeling and Analytics

Many organizations want to share data sets across the enterprise, but taking the first steps can be challenging. These challenges range from purely technical issues, such as data formats and APIs, to organizational cultures in which managers resist sharing data they feel they own. Data Governance is a set of practices that enable data to create value within an enterprise. When launching a data governance initiative, many organizations choose to apply best practices, such as those collected in the Data Management Association's Body of Knowledge (DAMA-BOK). While these practices define a desirable end state, our experience is that attempting to apply them broadly across the enterprise as a first step can be disruptive, expensive, and slow to deliver value. In our work with several industry and government organizations, SEI researchers have developed an incremental approach to launching data governance that delivers immediate payback. This post highlights our approach, which is based on six principles.

Have you ever been developing or acquiring a system and said to yourself, I can't be the first architect to design this type of system. How can I tap into the architecture knowledge that already exists in this domain? If so, you might be looking for a reference architecture. A reference architecture describes a family of similar systems and standardizes nomenclature, defines key solution elements and relationships among them, collects relevant solution patterns, and provides a framework to classify and compare. This blog posting, which is excerpted from the paper, A Reference Architecture for Big Data Systems in the National Security Domain, describes our work developing and applying a reference architecture for big data systems.