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.
In September 2016 Cambridge Semantics attended Strata+Hadoop World 2016 in New York, NY. While we were there, Marty Loughlin, our VP of Financial Services, spoke to a gathering of attendees about who we are and what our platform does. Here is his presentation.
"So what?" you might say. Another hyperbole-fueled headline in tech is hardly a notable event. To answer, let's start with what we did.
A data mart is a simple form of a data warehouse that is focused on a single subject or functional area. It draws data from a limited number of sources such as sales, finance or marketing, and is often created and controlled by a single department within an organization. Like data warehouses, data marts implement the characteristics of governed, non-volatile, and integrated data, although the static model known to be the “truth” at the time is of a smaller scope than the enterprise scope used in a data warehouse.