The Smart Grid is poised to transform the energy industry using digital information to enhance efficiencies and performance for utility customers. As the Smart Grid continues to expand, demand for advanced big data analytics capabilities to better manage grid operations is growing as well. Utilities are looking for ways to effectively manage the large volumes of complex and unstructured data flooding the network to provide real-time, predictive and highly actionable intelligence to the enterprise.
An organization’s staff is its most valuable asset, and the human resources function is critical to the stability and growth of business operations.
One can aptly describe many of today’s organizations as complex adaptive systems. Dynamic interaction patterns and emergent relationships characterize their complexity, while their ability to change and self-organize capture their adaptiveness. To survive, traditional mechanistic, highly structured organizations characterized by rigid, vertical communications have transformed to organic, rapidly changing organizations that exhibit nearly amorphous communication patterns. Organizations that learn to adapt usually survive or even thrive; conversely, those that don’t adapt, either dissolve or become insignificant.
We’d like to introduce you to the newest member of our team, Dan Szot, who has joined us as VP Sales, Life Sciences and Healthcare. Dan brings more than 20 years of sales leadership and field operations experience to the Cambridge Semantics team, specializing in enterprise sales, research discovery and clinical applications. With his broad experience in all phases of the pharmaceutical business – from drug ideation to commercialization and product life-cycle management – Szot possesses a keen understanding of the value that cutting-edge data discovery and analytics can offer the industry.
Gartner predicted that more than 8.4 billion connected devices will be in use in 2017, a 31 percent increase from last year. That number is projected to grow to a trillion-nod network over the next several years. We are in the midst of an Internet of Things (IoT) revolution and for enterprises it is creating unprecedented volumes of new data to intelligently master.
Superior decision making is an essential aspect of life, whether it be in business, national security, health, environment, and every other aspect of human existence. Look no further than geopolitical affairs, such as North Korean or Iranian relations, to understand the importance and impact of decisions on human life, indeed the entire planet. This is not an exaggeration. From an Information Technology point of view, the goal is to provide complete and accurate information on demand to support decision making.
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.”
Today marks the 100th day of the New Year, and we’d like to take a moment to reflect on a few of the more interesting developments we’ve seen thus far in the fast-moving big data analytics arena.
At Gartner's Data & Analytics Summit 2017, Alok Prasad, President of Cambridge Semantics, was joined by Peter Horowitz of PricewaterhouseCoopers for their session entitled “Accelerating Insight: Smart Data Lake Customer Success Stories”. During this presentation, they discussed how Cambridge Semantics’ in-memory, massively parallel, semantic graph-based platform, Anzo Smart Data Lake®, delivers an accelerating edge to data-driven organizations, while maintaining trust with data security and governance.
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.