Discussions of cognitive computing almost always include a reference to “big data.” Discussions of big data occasionally, but infrequently, reference “cognitive computing.” But are we truly confident that we know which is which and why that is so?
We felt that finding a way to describe these two trends in simple terms—and differentiate among them and define their relationship to each other—could help lower the level of hype and confusion in this active corner of the technology landscape. If we can achieve a new kind of clarity in this conversation, we can get on with the business of talking about cognitive computing in a much crisper and more intelligent manner than we’ve typically experienced to date.
It’s important first to pull apart the various levels that these terms operate on in our broader public conversation. To cut to the chase, I’m proposing that there are four important levels or “meanings” that these terms are operating on. We need to get better at understanding and differentiating these meanings. We need to be more accurate as we throw these terms around. The four levels are:
1) The mission or purpose of big data vs. that of cognitive computing
2) The foundation technologies of each
3) The functional description of what these trends and their technologies actually do for people
4) The symbolic level, where our public conversation has already transformed these terms into labels for various business strategies, worldviews, and hype campaigns.
In this “Part 1” post, my goal is to start to deconstruct and clarify these levels, starting with the level of mission or purpose. I want to identify the important pieces and offer a view of how they relate and how they diverge. In subsequent posts, we’ll address the important issues that come up in the levels of foundation technologies, functional description, and symbolic communications.
First, I want to suggest that big data and cognitive computing are highly distinct in their purpose or mission. To put it succinctly, the mission of big data is best understood as the next generation of the traditional IT function of storage and organization of machine-based enterprise information—now extended to include different types of data handled in new ways.
Cognitive computing, on the other hand, seeks the meaning in the data. Cognitive computing is best understood as an innovation in methodology for the field of analytics. Cognitive computing seeks to break through the constraints of analytics based on backward facing numerical calculations and static presentations of results for human review. Instead, cognitive computing represents a unique form of computing combining analytics, problem solving, and communication with human decision makers. It uses big data if necessary to answer ambiguous questions and solve problems. But its key contributions go well beyond the charter of analytics as understood today. Cognitive computing looks within and across disparate data sets including rich media and text, identifies conflicting data, uncovers surprises, finds patterns, understands context, offers suggestions, requests clarifications, and provides ranked solution alternatives. Cognitive computing offers a new approach to uncover the potential in data – and capture value whether the data is big or small.
As its purpose, big data remodels the data center, the database, and the data warehouse to accommodate today’s transformed digital environment. As its purpose, cognitive computing leverages a broad suite of evolving discovery, analysis, human interaction, and solution development technologies to offer a new kind of digital assistance that operates in near-human terms.
In my next post, I will look more closely at the foundation technologies of big data and cognitive computing—how they differ, how they overlap, and how to understand their contributions and boundaries. Read part two of the series.Share