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With its ability to learn, draw conclusions, and identify obscure associations in real time, an emerging breed of powerful analytics transcends the limitations of traditional data management and analysis.

For the first time in computing history, it is possible for machines to learn from experience and penetrate the complexity of data to identify associations. Known collectively as cognitive analytics, these innovations are inspired by the way the human brain processes information, draws conclusions, and codifies instincts and experience into learning.

Rather than depending on predefined rules and structured queries to uncover answers, cognitive analytics relies on systems that draw from a wide variety of potentially relevant information and connections to generate hypotheses. It expresses possible answers as recommendations, and includes the system’s own ranking of how confident it is in their accuracy. This process differs markedly from traditional analysis in that as more data is fed into a machine learning system, the system learns, which results in higher-quality insights and more accurate hypotheses.

The real value of cognitive analytics lies in its ability to process and understand exploding volumes of data in real time—including data that may contain wide variations of format, structure, and quality. It forms context-based hypotheses by exploring massive numbers of permutations of potential relationships, of influence, and causality, which generates conclusions unconstrained by organizational biases. In academia, these same techniques have been applied in studies of reading, learning, and language development, such as the Never-Ending Language Learning¹ projects. In the consumer world, pieces of cognitive analytics form the core of artificial personal assistant technologies that are frequently found in mobile phones and other consumer devices.

Even more interesting uses can be found in the commercial realm. For example, the health care industry is deploying cognitive analytics capabilities to improve the quality of patient outcomes. By coupling a wide range of structured inputs—such as claims records, patient files, and disease outbreak statistics—with unstructured inputs like medical journals and textbooks, clinician notes, and social media feeds, doctors are able to create more detailed, accurate diagnoses that draw upon the latest medical research, statistics, and individual patient histories.

The financial services sector is using cognitive analytics to advise clients, execute trades, and detect fraud, among other activities. Retailers are beginning to use cognitive systems as virtual customer service agents and digital store clerks that can provide answers to customers’ questions about products, trends, recommendations, and support. More broadly, global organizations are deploying cognitive analytics to fine-tune complex supply chains and cloud networks.

The Nuts and Bolts

Cognitive analytics is an extension of cognitive computing, which comprises three main components: machine learning, natural language processing, and advancements in enabling infrastructure.

Machine learning. Machine learning, and its latest incarnation, deep learning², are artificial intelligence³ techniques, some of which are modeled after characteristics of the human brain. A machine learning system explores many divergent concepts for possible connections, expresses potential new ideas with relative confidence or certainty in their “correctness,” and adjusts the strength of heuristics, intuition, or decision frameworks based on direct feedback to those ideas. Many of today’s implementations represent supervised learning, where the machine must be trained or taught by humans. User feedback is given on the quality of the conclusions, which the system uses to tune its “thought process” and refine future hypotheses.

Natural language processing. Natural language processing (NLP), or the ability to parse and understand unstructured data and conversational requests, is another important component of cognitive computing. NLP makes it possible to include large volumes of raw data—including handwritten content, emails, blog posts, and even voice transcriptions—from multiple sources in an analysis. This is essential given that, by some estimates, the volume of unstructured data is growing by 62 percent each year.4 Instead of demanding that all information be scrubbed, interpreted, and translated into a common format, the hypothesis and confidence engines actively learn associations between and the relative merits of various sources.

NLP can also simplify human interaction with cognitive systems. Rather than forcing end users to learn querying or programming languages, it allows spoken, natural exploration. For example, users can ask, “What are the sales projections for this quarter?” instead of being forced to write complicated lookups and joins against databases and schemata.

Big data. In data analysis, unstructured data that does not fit into traditional tables and sets often creates bottlenecks that slow progress and make it difficult to extract useful business insights. Unfortunately, much of the consumer information found on the Internet—e.g. photos, videos and blog posts, among others—is unstructured.

Increasingly, companies are using cognitive analytics to break the bottleneck. For example, Google has developed software that can recognize distinct objects within thousands of YouTube videos without previous instruction. Facebook has a deep learning team that specializes in using cognitive analytics to illuminate the human emotions that underlie posts on its website and app. Potentially, approaches like these will help companies gain greater insight into trends, and predict consumer behavior with greater accuracy.

Enabling infrastructure. Finally, continuously collecting, storing, and analyzing massive amounts of data requires increased processing power and storage networks delivered at low cost. To support cognitive computing systems and processes, CIOs often leverage the cloud, large appliances and high-end servers, and distributed architectures to create needed efficiencies and lower costs.

Well-Timed Progress

Cognitive analytics is the application of cognitive computing technologies and processes in ways that enhance human decision-making. It takes advantage of cognitive computing’s vast data processing power and adds channels for data collection (such as sensing applications) and environmental context to provide practical business insights. If cognitive computing has changed the way in which information is processed, cognitive analytics is changing the way information is applied.

This breakthrough could not have come at a better time. As more human activity is expressed digitally, data forms continue to evolve. Though highly structured financial and transactional data remain at the forefront of many business applications, the rise of unstructured information in voice, images, social channels, and video has created new opportunities for businesses to understand the world around them.

Cognitive analytics is still in its early stages, and is by no means a replacement for traditional information and analytics programs. Yet industries wrestling with massive amounts of unstructured data or struggling to meet growing demand for real-time visibility may find it worthwhile to begin exploring this powerful new approach in the analytics arsenal.

—Rajeev Ronanki, principal, Deloitte Consulting LLP and David Steier, director, Deloitte Consulting LLP