Data efficacy

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Data efficacy is an enterprise architecture design heuristic[1] first defined by Orbis Technologies’ semantic technology expert Steve Hamby in his presentation at the 2009 Semantic Technology Conference.[2] Conceptually, the idea is to assess the stored enterprise data in terms of the value it brings to the enterprise and the efficiency with which it is stored, accessed and retrieved.[3] The architecture should not allow for inefficient data storage, nor should it accommodate data that has no value to the enterprise.[4] This architecture design principle has become more popular as companies migrate towards large, spread out, computing environments. Data center consolidation prevents massive data replication and inefficient data architectures.[5]

The IT industry continues to discuss the ever-increasing impact of Big Data,[6] a global problem resulting from a society making increasing use of technologies that generate a significant volume of data (blogs, social networking sites, texting, Internet search indexing, video archives, and large-scale eCommerce, etc.).[7] Enterprise architectures that embrace the data efficacy architecture concept typically utilize semantic web technologies to organize, store, and share large amounts of data. This approach provides the tools required for capture and dissemination of complex information and offers accurate solutions that are scalable and reusable.

A Gartner survey conducted in July-August 2010[8] reported that 47 percent of IT staffers surveyed ranked data growth as a top three challenge faced by their IT organization. The demand for information management specialists and innovative technologies is increasing every day as more professions and businesses begin to transfer their records online, smart phone technology evolves, and more social media sites are added to the Internet.[9] Hamby[10] was the first to introduce an information technology enterprise architecture design heuristic that utilizes cloud computing technologies to provide the infrastructure needed for semantic systems to scale to enterprise needs.[11] The heuristic was in response to the technical vision most of his customers shared in common: essentially, a logical technical migration from exposing data and applications via web services to the exploitation of content, and dissemination of knowledge from various subject matter experts to others in the enterprise.

Data efficacy is an option to reduce massive data replication that can occur within other Big Data architecture. Heuristics of the same nature as Hamby’s diminish enterprise-scalability problems associated with RDF size and file disorganization. According to him and other leading cloud analytic specialists, application-based formal ontologies should be approached from two ontologies: a theory-based formal ontology that is rationally driven and contains general categories and a lower level collection of non-formal information needs that is domain-specific, empirically driven, and relies on expert knowledge. Using cloud technologies, rich analytics can use more expressive logic but render RDF[12] and OWL-DL,[13] and analytics and data are processed together to minimize network latencies.


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