One result of today’s technologically-connected world is a historic and exponential explosion in data – what is commonly referred to as Big Data.

Big Data can be empowering and transformative. Individuals, corporations, and governments all over the globe are generating zettabytes of data every year as they connect to networks on their computers and cell phones. People all over the world can search for and purchase consumer products; make dinner reservations at their favorite restaurants; perform research; conduct banking transactions across country boundaries; interact socially with their friends; perform activities associated with their careers; and deepen the interactions of their lives.

However, Big Data presents challenges for today’s networks and computing systems. One major challenge is that data comes from everywhere and in many shapes and forms. Historically, it has come from traditional structured data sources, such as corporate and governmental computer systems. Today, however, it increasingly comes from unstructured data sources such as the Internet, mobile devices, social media interactions, GPS location information, weather models, RFID, transportation and logistics scans that do not reside neatly within the tables and columns of traditional uniform databases and computer systems. What this means is that Big Data is too “Big” and too “Unstructured” to be currently leveraged by most organizations.

Even if the data could be centralized, today’s computing systems still have difficulty making sense of the data (i.e., understanding and learning) from the interactions between both related and seemingly unrelated data elements. Using current analytic techniques, most decision-making frameworks are challenged to process and understand volumes of data and then instigate actions that foster desired outcomes within timely decision cycles.

That is why it is essential in today’s business environment to employ technologies that process and analyze Big Data in a way much like the human mind senses its environment and processes data. For example, an individual assesses the risks of crossing a street when a car is approaching. The mind processes variables like car speed, distance, obstacles, motor skills, and so forth before making the decision to cross or wait. Like human thought processes, cognitive reasoning ingests and transforms data into information; creates rich referential connections between data elements; enables understanding and learning; and is then presented as actionable intelligence (within relevant timeframes). This kind of cognitive reasoning can be used by businesses to take on some of today’s most vexing challenges.

The Enterra Enterprise Cognitive System™ is the leading-edge technology solution to the challenge many businesses face, that Big Data is too big and too unstructured to deploy sufficient human or traditional quantitative resources to analyze and make sense of it within increasingly shorter decision cycles.