The new face of big data: AI, IoT and blockchain

Each new foray into sentiment analytics, mobile technologies and additional integration efforts of unstructured data with structured data further blurs the lines between big data and traditional data.

The advent of big data was arguably the defining moment of the contemporary data management landscape, simply because its deployment and use cases have largely become synonymous with data itself. Each new foray into sentiment analytics, mobile technologies and additional integration efforts of unstructured data with structured data further blurs the lines between big data and traditional data.

Consequently, what was once a niche market within the data sphere has become so implicit to data-driven culture as a whole that its technologies have fragmented and become increasingly specialized—much like the cloud, which is widely regarded as the de facto means of facilitating big data initiatives. Previously, big data technologies represented the vanguard of data management with an alignment of social, mobile and cloud applications. Today, organizations enhance big data’s value while maximizing its monetization with the emergence of a new affiliation of technologies supported by:

  • artificial intelligence—AI’s journey through the data landscape has been well documented. The various manifestations of machine learning, deep learning, neural networks, cognitive computing, image recognition, speech recognition and natural language processing are consistently aiding the enterprise in analytic endeavors associated with big data. In many instances, AI is an immediate solution for the volumes and velocities for which big data is known.
  • the Internet of Things—The IoT is emblematic of so much of big data’s promise, combining the speed and size of those technologies alongside alternative cloud paradigms and the evolution of mobile applications. Touted as one of the primary expressions of big data in the subsequent decade, its emergence should become much more apparent in the coming 12 months largely due to the maturing influence of AI and the cloud itself.
  • blockchain—The growing interest in the blockchain phenomena, defined at a high level as a secure way of leveraging nearly instantaneous transaction activity, is projected to exceed the financial vertical for a profound effect across the data sphere. Its most eminent application could very well be provisioning a prototype for security measures to truly fortify the IoT.
  • augmented reality—AR and virtual reality (VR) will impact the next decade as a more accessible means for organizations to explore their data. The year 2017 will see additional organizations experimenting with ways those capabilities render big data less daunting and perhaps even more enjoyable.

The next year will demonstrate the utility of the synthesis of those technologies to the enterprise—and to each another—in a manner increasingly difficult to disambiguate as organizations continually rely on them. John Rueter, VP of marketing at Cambridge Semantics, says, “All the different technologies that people have been using in their data-intensive environments are now being driven by this big data age of all the things we’re familiar with: different structures, formats, locations, volume. Our argument is the majority of the tools on the market weren’t built in anticipation of the demands of this new data age.”

Those predicated on the following technologies, however, unequivocally are. Full article here: