The new face of big data: AI, IoT and blockchain
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.
AI’s utility to the big data age is manifold. Its dynamic algorithms are tasked with implementing timely analytics on immense data quantities. That functionality is particularly useful with unstructured data, which frequently becomes too time-consuming with conventional business intelligence methods. Thus, AI adds to the variety of sources incorporated in analytics, such as the inclusion of video and image data for sentiment analysis. In finance, AI technologies parse through massive quantities of unstructured data—reports, investment news, presentations—on which analysts depend. They also play an invaluable role in integrating data (a considerable chore when involving unstructured data) by discerning which data are most relevant for specific use cases based on established precedents and intelligent, semantic inferencing. Additionally, cognitive solutions leverage AI for recommendations—replete with explanations—for future courses of actions since they are inherently predictive in nature.
“Our argument is the majority of the tools on the market weren’t built in anticipation of the demands of this new data age.”
Most importantly, AI has added a pivotal fourth dimension to big data analytics termed by Franz CEO Jans Aasman as “learning.” According to Aasman, the first dimension of big data is structured data, the second is unstructured data and the third is knowledge facilitated by taxonomies and standardized ontologies. AI technologies not only assist in the integration of unstructured and structured data, but also in the profundity begat by the fourth dimension of big data analytics in which “you can put the output of analytics back into the graph so the database learns from it,” Aasman says. That iterative process is one of the fundamental tenets of machine learning.
The fourth dimension of big data expands that capability exponentially by enabling organizations to effectively iterate not just on individual algorithms, but on the results of comprehensive, integrated analytics throughout the enterprise—which is one of the more incisive capabilities Aasman bestowed on an exhaustive Semantic Data Lake for Healthcare.
The fourth dimension capabilities of AI could well be its most vital because they democratize big data by making it accessible to those who need it most—the business users who daily depend on data for their jobs. According to Forbes, 2017 AI predictions from Forrester reveal: “The combination of AI, big data and IoT technologies will enable businesses investing in them ... to overcome barriers to data access and to mining useful insights. In 2017, these technologies will increase businesses’ access to data, broaden the types of data that can be analyzed and raise the level of sophistication of the resulting insight.”