Comprehending semantic technology is no longer an arduous task for the back offices of data-savvy organizations. Business users and C-level executives are starting to comprehend the basics of the technologies that are increasingly impacting their jobs.
Conventional data discovery utilizes dashboards, visualizations, search, and other tools to determine appropriate data for integrated, targeted use cases. Smart data discovery techniques, on the other hand, leverage linked data graphs, comprehensive data models, and a semantic standards-based approach to publish results to those same popular tools.
Analytics has profoundly altered the retail industry. The combination of real-time, predictive analytics—in conjunction with traditional historic analytic capabilities—has reconfigured the way organizations facilitate customer engagement, supply chain management, and inventory in both e-commerce and brick and mortar settings.
There is no simple way to define the Association for Cooperative Operations Research and Development (ACORD), or its significance to the insurance industry. It is simultaneously a global community of insurance professionals and a certifying agency, an established semantics framework and standards-based approach to data management, the reference architecture of choice and a burgeoning body of knowledge for business domains and the data landscape.
The analytics prowess of Anzo Graph Query Engine (AGQE), an enormously parallel in-memory querying graph database of semantic data, is almost immeasurable. The scale, scope, and speed of data elements that big data encompasses are boundless.
Data transformation is one of the most vital facets of data management. Prior to integrating data sources, conducting analytics, or utilizing data in most operational applications, data must be transformed from its native state to one suitable for the target system—even if it’s just a data mart.
All of the possibilities of big data analytics, semantic graph databases, and Smart Data Lakes™ have been realized with the emergence of Anzo Graph Query Engine (AGQE).
Exploratory analytics represents the evolution—and perhaps culmination—of conventional analytics and business intelligence options. It’s a combination of real-time data discovery and ad-hoc, graph-aware analytics that provides automatic, self-service insight for end users on all their data.
With no inherent means of adhering to governance and security protocols, data lakes are akin to the Wild West in that they are devoid of order and consistency. Each user manipulates his or her own data at the risk of the reuse of that data for others.