Foundations matter.
Data quality is one of the most important pillars of a Semantic Architecture. Regardless of our particular projects or roles well defined semantics are only valuable if there is accurate, representative, and timely data. Unfortunately, too many organizations are unable to (or shouldn’t) trust their data enough to create the information experiences where people learn, make decisions, or connect.
Semantic professionals have a large role to play in building the foundations of data quality and in understanding the scope and capability of the information they are using. This talk breaks the data life cycle into phases and describes the role of people, technology, and experiences in each phase.
Collection, encoding, storage, decoding, and presentation each have unique needs and dependencies which need to be considered and the organizations they are working with. Issues of representation, accessibility, appropriateness, accuracy, and completeness all come into play. Practitioners will be able to think through the capabilities and limitations of the data in their organizations with a flexible framework developed in this presentation.
By asking deeper questions about the data our work is dependent on we can start to address the larger issues that arise in terms of data and how it applies to our work in AI, search, navigation, etc.