Strategic advice to leverage new technologies

Many large enterprises struggle with annual project budgeting cycles; big up-front planning; arduous governance, portfolio, program, and project management and control mechanisms; and other systemic efforts that, while intending to establish more predictability/control and less risk, often do just the opposite. This can cause Agile initiatives to falter or to collapse under the weight of an unrelenting command-and-control mindset.

Cognitive computing is among the major trends in computing today and seems destined to change how business people think about the ways in which computers can be used in business environments. “Cognitive computing” is a vague term used in a myriad of ways. Given the confusion in the market as to the nature of cognitive computing, this Executive Summary and its accompanying Executive Report describe what we mean by cognitive computing by exploring five different perspectives on the topic: (1) rules-based expert systems, (2) big data and data mining, (3) neural networks, (4) IBM’s Watson, and (5) Google’s AlphaGo.

Cognitive computing will revolutionize how organizations use computers. In this Executive Report, we consider the nature of cognitive computing. We look at the topic from five different perspectives, including: (1) rules-based expert systems, (2) big data and data mining, (3) neural networks, (4) IBM’s Watson, and (5) Google’s AlphaGo. We conclude that cognitive computing isn’t a specific new technology, but rather a variety of different technologies and complex architectures used to solve complicated and challenging problems.

It may be fair to say that many of the problems people see with enterprise architecture come from the name that the profession has given itself. “Architecture” implies a responsibility for the overall structure of the organization, a role that involves controlling and approving any changes to how the parts are put together. By analogy, this places the enterprise architect in a role similar to that of a building architect, with an overall responsibility for the structure of the enterprise.

Many have felt that “urban planner” is a better meta­phor, because the urban planner looks at how all the individual buildings fit together, considers how the structure and layout of a city affect the lives of its inhabitants, and helps to realize the overall vision of the city. I agree that this metaphor is better, and I think the evolution of urban planning provides some additional insights.

Emotion recognition platforms are now available that use neural networks and other machine learning algorithms to analyze and measure the facial expressions of subjects appearing in photos and video in order to determine their emotional state. Such analysis may take place on large collections of photo and video files residing in databases. It can also be performed in near real-time on images captured live — for example, for security scenarios or in-store retail applications involving shopper response measurement.

There has been a long-standing debate on what is the right ratio of developers to QA (dev-QA) on software engineering teams. Many managers face this debate on a regular basis. Some people argue that you need to keep a 1-to-1 ratio of dev-QA, whereas others say that you need no QA people on The Team and that developers should be responsible for the code they write.

Although certainly not a complete replacement for the highly customized applications that have characterized most enterprise IoT implementations to date, domain-specific IoT and industry-specific solutions offer end-user organizations a (relatively) less painful way of putting their IoT plans and initiatives into production.

In this issue of The Cutter Edge: Preparing for IoT Disruption | Six Ways to Reduce Tech Debt | New Cutter IT Journal