Becoming cognitive: A new disruptor remakes the business landscape
In an era of cognitively enriched intelligent machines, among them mobile devices and Internet of Things (IoT) endpoints, cognitive analytics is transforming both life and work. Indeed, growing numbers of organizations across a range of industries are building and deploying cognitive applications, as indicated by an enterprise survey conducted by the IBM Institute for Business Value. During the course of that survey, 39 percent of respondents reported using cognitive analytics—and 61 percent said either that they used cognitive analytics or that they planned to do so in the near term.
Disrupting the modern business environment
Already, cognitive analytics has brought disruptive innovation to the business environment as developers across a range of sectors design a smorgasbord of cognitive applications. For examples of fine-grained personalization of mobile, social, cloud and Internet of Things applications, take a look at this gallery of IBM Watson–enabled apps. Cognitive systems enable natural interactions involving voice and visualization capabilities, and they are becoming increasingly able to understand geospatial and temporal contexts while delivering suitable responses.
For a good strategic overview of trends in cognitive computing, read Andrew Manby’s white paper Becoming a Cognitive Business with IBM Analytics, in which he describes how modern businesses are infusing their every application, interaction and touch point with cognitive capabilities. In such an environment, Manby says, everyone from senior business executives and application developers to IT managers and line-of-business professionals should begin rethinking both job and organization with an eye toward realizing the full strategic potential of cognitive technology:
Essentially, cognitive businesses place a premium on making probabilistic-based decisions with a high degree of confidence. They systematically empower their employees to discover insights, and above all, put those insights to work, every day, leveraging all available data to solve problems, make better decisions, innovate faster, predict the future, and outthink the competition.
Powering a new generation of data scientists
The data scientist is central to this vision of cognitive computing, and a new generation of data scientists is coming of age, its members adept at developing cognitive applications in Apache Spark, Apache Hadoop, R and other open platforms and tools. Moreover, modern data professionals, many of them self-taught and self-directed citizen data scientists, are increasingly combining statistical modeling, subject-matter domain knowledge and programming skills to accomplish projects intended for the public good.
What’s more, cognitive computing is rapidly accelerating the productivity of working data scientists by automating much data analysis that once required manual effort. In particular, many cognitive systems use unsupervised learning to automate sensing and detection of deep patterns and nuances even in the absence of complex data sets. Accordingly, machine learning helps automated systems understand new concepts while training themselves to become experts—capabilities that are fundamental to creating value through machine learning in a world in which both the quantity and the variety of big data seem to have no end.
In addition, if you’re interested in other studies of cognitive business strategies conducted by the IBM Institute for Business Value, learn more by checking out the following resources:
- Analytics: The upside of disruption
- Cloudy with a chance of mishap
- Beyond listening: Shifting focus to the business of social
For strategic guidance that can help you succeed with analytics in the cognitive era, download Manby’s white paper, complete with case studies.