Last month, Cambridge Semantics held its inaugural Life Sciences Smart Data Discovery and Analytics Forum in Boston. At this forum, leaders in Life Sciences and Big Data met to discuss the application of smart data technology to research and development, clinical trial and healthcare data to gain more actionable insights and shorten the overall product lifecycle.
A picture is worth a thousand words they say, and this remains true for data discovery and analytics. For centuries, graphs and maps have been used to help us simplify, understand and convey large amounts of data in a universal manner. And with the evolution of technology, we can now visualize increasingly complex information at lightning-fast speeds.
The evolution of big data over recent years has resulted in a new role that has been gaining industry traction – the Chief Data Officer (CDO). As executives begin to recognize the business value residing in their data, they are focusing now more than ever on finding ways to manage and govern this data. The CDO is emerging as a key player in the enterprise by helping to establish and maintain data governance, quality, architecture and analytics, enabling firms to more productively harness information to manage risk, reduce cost and drive innovation.
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.
Whether they are doing research for regulatory compliance, trade surveillance, 360-degree customer awareness, drug discovery or clinical trial site selection, enterprises can benefit from the power of semantic graph analytics. Developing a complete Smart Data Lake™ solution by combining business analysis tools for modeling, mapping and analyzing graph data with a massively parallel, in-memory, semantic graph database enables enterprises to utilize interactive, open standards-based, semantic graph analytics at big data scale.
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).
State Street Bank, The EDM Council, Dun & Bradstreet, Wells Fargo and Cambridge Semantics completed an engagement to harmonize State Street's Interest Rate Swap data with Dun & Bradstreet's entity hierarchy data using the Financial Industry Business Ontology (FIBO) and Cambridge Semantics' Anzo Smart Data Lake®.
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.
On my flight back to Boston from Grapevine, TX, where we spent three exhausting and exhilarating days at Gartner BI & Analytics Summit, I am reflecting on the great interest shown in Cambridge Semantics Smart Data Lake at this event.
Unstructured data is all around us: in news stories, web pages, journal articles, social media posts, patents, research reports, presentations, and a variety of other sources. These items are unstructured in that they don’t start out with a predefined, explicit schema or structure. Historically, these documents have been read by humans looking to find information relevant to their particular tasks or roles. In today's deluge, however, the need for scalable reading, repeatability, traceability, and speed has driven the advent of text analytics platforms.