Only with a rich and interactive semantic layer can the data and analytics stack deliver true on-demand access to data, answers and insights - weaving data together from across the enterprise into an information fabric. By developing Anzo Smart Data Lake on knowledge graphs, Cambridge Semantics has built the foundation for a truly disruptive Semantic Layer.
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.”
We have been humbled by the number of industry awards Cambridge Semantics has garnered in recent weeks. We know they can only be the result of the hard work and ingenuity of the Cambridge team.
Many retail industry observers are asking why e-commerce giant Amazon purchased the brick & mortar grocery chain Whole Foods. The unanimous answer is: access to real-world customer data that is richer and more robust that what Amazon can acquire from online purchases.
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.
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.
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.
"The legal and ethical collection and analysis of information regarding the capabilities, vulnerabilities, and intentions of business competitors" - Strategic and Competitive Intelligence Professionals
This is how Strategic and Competitive Intelligence Professionals (SCIP), the most well-known global body on Competitive Intelligence (CI), defines the concept of CI. For the uninitiated, the page on the organization's Code of Ethics for CI professionals sheds lights on the innards of the practice. One does not have to belabor the importance of the practice in the context of successfully operating a business. It is in the operational details, as is the case with most details, that the devil lies.
The financial industry is facing a perfect storm of disruptive drivers for data management. While regulators seek accuracy and transparency, institutions are struggling with fragmented data and IT infrastructures. The path forward is “data engineering” – applying consistent semantics with scalable infrastructure to harmonize data and enable traceable and dynamic analytics.
As we approach the end of 2016, we sat down with our CEO, Chuck Pieper, to discuss the future of big data and get his predictions for 2017. Here are his thoughts.