Organizations are becoming more analytically inclined, automation is rampant, and business users are empowered to accomplish more at a greater scale than they previously could. Nonetheless, there's another side to the pervasive deployment of cognitive computing technologies throughout the data ecosystem, particularly in terms of the mounting ease, accessibility, and utility of advanced analytics. The increasing demand for predictive insight--and the data required to facilitate it--has very real repercussions in terms of data privacy and regulatory compliance which, if not properly addressed, can restrict AI's use for organizations. Many firms are attempting to balance the data demands for AI with what Privacera SVP of Marketing Piet Loubser termed the "let's stay out of trouble side of things. As much as we think externally of regulations from on top, the majority of organizations have much more stringent things going on inside their four walls."

Other than the resurgence of various Artificial Intelligence dimensions, the single most meaningful development in the big data space in the past several years is the burgeoning distribution of data assets. Whereas once those assets were safely confined within the enterprise, the confluence of mobile technologies, the cloud, the Internet of Things, edge computing, containerization, social media, and big data itself has shifted the onus of data management to external, decentralized sources. The ramifications of this reality are manifold. Organizations can now get the diversity of data required for meaningful machine learning results. The overhead of operating in hybrid, multi-cloud environments is less costly.