All the secrets of cognitive computing, the technology that, by imitating the way of thinking about human intelligence, is already today transforming the way of working of different productive sectors. The enormous growth, or perhaps better to say explosion, of the amount of data, is a phenomenon that is certainly changing the traditional IT landscape. A growth induced by the net enrichment of the variety of the sources, starting from the cloud and from the Internet of Things. The challenge facing all organizations today is to make this expanse of data, which is at first only difficult to manage, a primary resource for defining corporate strategies. In this sense, the main road is to rely on modern cognitive computing systems: the latter enhance the abundant availability of digital data to understand the market and customers and to influence the creation or evolution of the business models themselves.
Table of content:
- 1 Cognitive growth in the near future
- 2 How cognitive systems work
- 3 Applications of cognitive computing
- 4 The implications of the cognitive for the social
It is no coincidence that a recent estimate by IDC predicts an increase in investments in these technologies of 59.3% in the current year for a world-wide value of 12.5 billion dollars, but the turnover should rise to quota $ 46 billion by 2020. IDC itself predicts that, by 2018, 75% of companies, as well as software developers, will implement some cognitive computing functions in at least one business application. And again, by 2019, 40% of digital transformation initiatives will be supported by cognitive skills. A true symbol of the rise of this technology is the cognitive computer system of IBM, Watson, which has allowed Big Blue to re-launch itself due to a series of solutions and services.
The big difference compared to the classic big data and analytics or, better, the extra step is that with the term cognitive computing, we mean all those tools that allow the functioning of the human brain broadly, managing to learn and interact naturally with who uses them. In this way, they are able to provide significant elements to make decisions in the face of a very high quantity and heterogeneity of data and variables. Indeed it is possible to say that the more data they have available, the more these systems are able to learn and provide "better" answers.
More technically, we are talking about technological platforms able to learn independently, reason, understand, process, and use the natural language of man. It includes visual and dialectic skills (Nlp - Natural Language Processing), to contextualize information and provide even very detailed insights. In essence, with the cognitive type of computer science, one exits the typical binary mode of "yes or no": what is predicted is not a certain result, but an indication based on scores and correlations.
This particular feature of the cognitive produces clear advantages in all those fields where it is necessary to process large amounts of data. They are often available even in formats that are not homogeneous among them and, therefore, difficult to "digest" by traditional IT applications. For example, cognitive computing makes it possible to automate some complex actions due to the ability to learn from data, process them, and return indications and recommendations. All this makes it possible to reduce the time and resources needed to solve customer problems.
Just think of a sector like the Supply Chain, where greater speed and efficiency can ensure a great competitive advantage. A lesson that was taken up by a giant like Amazon, which has long been on this path but also by many small and large retailers, who, due to the use of cognitive computing tools, have managed to make their operation as efficient as possible, thus succeeding in minimizing logistics costs and improving margins. Predictive maintenance is undoubtedly another very interesting area for cognitive systems because instruments of this type are able to suggest areas of potential improvement or those in which there are greater chances of anomalies occurring, thus minimizing plant shutdowns. No less important is the potential of cognitive in the world of Customer Care, considering that today. Companies are able to accumulate vast amounts of information about their customers from disparate sources, which, however, are rarely adequately exploited, despite the strong consumer demand for quick answers and personalized experience. Cognitive solutions come to the rescue, allowing individuals to quickly identify spending preferences, independently analyzing data from heterogeneous sources (purchases, social media behavior, demographic and economic variables, etc.). In this way, it is possible to set the relationship with the customer in a more direct and natural way, with proposals and messages packaged on the basis of the actual tastes and expectations of each consumer.
More generally, according to a recent study by SB Italia, cognitive computing can provide concrete answers to today's most important business needs. In this way, a commercial company can use a cognitive system to standardize customer behavior, develop proposals and offers tailored to consumers' tastes, and simulate sales trends to optimize warehouse flows and make customer care tailored to the individual user commercial leverage. In the same way, a production company can do predictive maintenance on its plants, inject intelligence into the supply chain, preventing inefficiencies, and concretize their time-to-market wishes by being able to make decisions based on greater certainty.
In addition to companies, the personal digital assistants we have on our phones and computers (above all Siri) cannot be classified as real cognitive computing systems. Basically, they are software that has a pre-programmed set of answers and can only respond to a default number of requests. But in the near future, these systems (who know onboard which devices) will be able to learn and provide really intelligent answers, evolving in the direction of the cognitive.
Jan 10, 2020