At the Gartner Data & Analytics Summit 2017, Cambridge Semantics' very own Barry Zane, Vice President of Engineering, and Ben Szekely, Vice President of Solutions, discussed how the Anzo Smart Data Lake® (ASDL) solution empowers business users with on-demand analytics of their rich data during their session entitled “Accelerating Insight with High Octane, Graph Fueled Data.”
Real-world events demonstrate our inability to understand rapidly and accurately what we already know. In other words, we cannot answer questions completely, despite the fact that we may hold the requisite data. For example, if someone attempted to enter the United States (US) at an airport, and US officials initiated a query to the “system” and found nothing, that person may enter the US erroneously. This might occur because US officials asking a question such as “What do we know about this person?” cannot assuredly answer it – and not in a timely fashion.
Real-world events demonstrate our collective inability to rapidly and accurately observe, process, and interpret information in support of decision making. Additionally, one can argue that any sizable enterprise struggles to “know what it knows”. In other words, we often cannot answer questions completely or with certainty, despite the fact that we may hold the requisite data.
Topics: Semantic Web
We’d like to introduce you to the newest member of our team, Sam Chance, who has joined us as managing director of pre-sales. In this newly created position, Sam will work closely with the sales and engineering teams to accurately define and communicate the value of our Anzo Smart Data Lake® (ASDL) platform to our growing roster of customers, while also architecting a customized solution for their environments.
Conventional data analytics utilizes dashboards, visualizations, search, and other tools to determine appropriate data for integrated, targeted use cases. Smart data analytics 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.
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.
When I went to Text Analytics World in San Francisco earlier this month, I was struck at how many of the presenters, particularly consultants, ended their talks describing future directions of text analytics as something that sounded so familiar. They described what would be possible once there's advanced maturity in ontologies, the breaking down of siloes, entity and relationship resolution by multiple methods, and automated linking of it all together into semantic network models of knowledge: flexible exploration of the relevant. They made it sound like a bit of a stretch, almost pie in the sky, but what they briefly described as this destination was curiously similar to what was shown concretely in the last presentation of the conference, my own.
Happy New Year !!
At Cambridge Semantics we use the W3C semantic web standards to create conceptual canonical data models, in particularly using the web ontology modeling language called OWL. The conceptual models are declarative and express information in the way that the domain expert or business user, understands it – usually as a series of interlinked concepts and properties. Unlike most traditional technologies, these conceptual models are independent of how data is stored and provide an abstraction sometimes called “a semantic layer” for our Anzo software.
The result is unprecedented flexibility.