I spoke recently at the annual National Cooperative of Health Networks (NCHN) conference in beautiful Bozeman, Montana. The topic was electronic sharing of information between behavioral health providers and...
During the Madrid HL7 WGM last week there was a lively discussion on inclusion of FHIR versions in resource instance data to allow systems to cope with the changing schemas/profiles during the STU process and the following normative period.
A meeting last week prodded me to organize my thoughts on how pragmatic data governance has evolved and what impact DevOps and agility has on data governance. When working with aspiring DevOps organizations, we work tirelessly on the culture
Semantic (adj): related to meaning in language or logic.
As Analysts' EIM Practice Director I advise our clients as they seek to transform their data into information for the benefit of their organizations. We work with structured data
When a team claims to be "agile," my very first question is, "tell me about how you test?" Nothing else cuts to the heart of agility like the line between manual, infrequent testing, and the high-frequency automated testing essential to DevOps.
Implementing DevOps in BI with a goal of agility requires a holistic look the entire development pipeline. For instance, analysis and modeling must collect information needed to evaluate the value and impact of data elements as an input to test