I recall a senior executive of one of the world’s largest consumers of data proudly proclaiming in a much-celebrated announcement that went something like this: “We’ve made the bold decision to migrate our data to the cloud. This move will dramatically improve our information sharing because the contributing data will be stored in a common environment.” That was in about 2012, a long time ago in IT years! No one seemed to notice, however, that it’s not the physical location of data that enables “information sharing.” What happened is that multiple “data clouds” stood up, and data within each of these clouds was still isolated. That is, myriad data collections resided in the cloud(s), but were not related; they remained disconnected. One could say “data islands in the ocean called the cloud.”
A biomedical professional is certain to have come across some variant of the headlines mentioned in the visual above. Combinations of trending buzzwords in technology and healthcare form half of my news feed. The other half merely mirrors the first half.
Data is increasing in supply chain management for retailers. Most of this data is unstructured and flowing all day and night into servers, and there’s much more to come.
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.