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Big Data and Cognitive Computing – Part 4

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In Part 3 of this series, we took up the important issues that come up on the level of functional description as terms are fired off to delineate (or not) big data and cognitive computing. We found that confusion tends to arise immediately from the many statements we can read which appear to wrap big data and cognitive computing into the same phenomenon.

We also found that conversations around big data analytics often seem to ignore the reality that an application using cognitive computing approaches and technologies must be developed as a project distinct and largely independent of the “big data-ness” of the analytics environment.

We also found another consideration generating confusion around the functions of the two trends: the many statements that conveniently ignore the relative maturity of each. Big data has well over a decade of development so far and is already seeing the adoption of its second generation of tools and techniques. Cognitive computing, on the other hand, is in its earliest stages, with very few products even ready for the market and many more promises in the air than tangible results on the ground.

In this final part of the series, we turn to considering how the terms “big data” and “cognitive computing” are working on the symbolic level. We need to recognize first that when terms or phrases become buzz labels for entire new phases in the development of the computing business and the information economy, they take on something of a life of their own. This new life in the symbolic atmosphere has proven over the years to turn buzz terms into potent sources of confusion and obfuscation rather than guideposts toward understanding. This is the situation today with big data and cognitive computing.

For example, simply by looking at the impact of the phenomenon of big data on the marketplace, we see a remarkable difference between the attitude and language of companies who actually run their businesses on big data and those companies who are using these terms but are primarily worried about being left behind.

Google, Facebook, Yahoo, Amazon—these are the firms who first encountered big data and learned how to harness it into web advertising, social media, digital publishing, and online commerce and web services, respectively. But these firms rarely make a big noise about the term “big data.” They view data as being simply raw material for business propositions that drive well beyond the narrow view of the technologies that underpin them. (An exception, of course, is Amazon’s cloud services business, which offers its expertise and resources directly to others as a service.) These firms don’t view themselves as being in the computing business, despite the fact that they have invented the state-of-the-art tools and practices required to cope intelligently with massive volumes of flowing data.

On the other hand, the legacy enterprise software vendors who dominated the computing markets in the recent past have been trumpeting about “big data” and how they can save their corporate customers from its perils while enabling them to reap giant gains from its opportunities. IBM, Oracle, HP, Microsoft, SAP, EMC—the list goes on. All these firms missed the industry turn to cloud computing, software-as-a-service, and big data, and now are re-engineering their product lines and marketing approaches around the new terminology.

At this stage of the maturing of the big data marketplace, most analysts are predicting a slowing of innovation, a period of consolidation, and a converging of big data offerings on a broadly comparable suite of products and services from a smaller number of vendors. In this environment, all the legacy vendors need to differentiate their big data value. This is where the emerging ideas and terminology around cognitive computing come into play.

IBM deserves special mention in this discussion of the symbolic importance of the cognitive computing term, since the company has been years ahead of competitors in uniting their R&D in cognitive technologies with their event marketing prowess (Watson playing Jeopardy) and with a serious, long-term, multi-layered business and investment initiative. They deserve recognition for proposing and successfully establishing the term in the first place. That said, IBM recognizes the value of cognitive computing in the short term in giving them symbolic differentiation from their competitors in the trenches of the big data, cloud, and analytics marketing wars.

We have left the consumer out of this discussion altogether, but we can’t close without noting that cognitive computing is now operating, for better and for worse, in your smartphone homescreen, and Apple and Google and Microsoft and Amazon are betting that their nascent digital assistants will be taking over a greater and greater part of the operating interfaces of these devices. IBM and Apple have announced alliances around these emerging capabilities, and we will certainly see another round of consumer-driven IT arrive in the near term, with smarter “cognitive” applications delivered in mobile form factors available at work as well as in civilian life. And leveraging big data to boot!

In closing, we hope that we have offered a series of perspectives on big data and cognitive computing that provide food for thought, a degree of clarity about similarities and differences, and a framework for looking at these fascinating and emerging technology trends with confidence and a level of immunity from industry hype.

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About the Author:

Hadley Reynolds is Co-founder and Managing Director at the Cognitive Computing Consortium. He is a leading analyst of the search, content management, and knowledge management industries, researching, speaking, and writing on emerging trends in these technologies and their impact on business practice. He currently leads the publications program at the Consortium.
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