Artificial intelligence is progressively entering most business processes. In this context, the ability of AI – Artificial Intelligence to analyze data coming in real time from the various applications is decisive, to immediately offer useful answers to support and automate the choices to be made.
Artificial intelligence is experiencing a moment of extraordinary critical and media success, finding itself exercising a leading role also in mainstream applicationswhere billions of people use it every day, often completely unaware.
The surprising results of generative AI, just think of the images of Dall-E and Midjourney, as well as the texts of the infamous ChatGPT, have certainly captured the attention of the general public, making the potential of artificial intelligence tangible both from a creative point of view and as regards the analysis of large numbers of data.
Although characterized by various implementation speeds, artificial intelligence is gradually entering most business processes and one of the most “appreciated” skills by companies concerns the so-called real time analytics, i.e. analysis – using artificial intelligence techniques – of data coming in real time from various applications with different levels of automation of the possible actions and decisions following the information produced by these analyses.
If information is power, responding immediately to a customer’s or an operational need is increasingly synonymous with success. This is demonstrated, at a mainstream level, by some applications that we now take for granted in our daily lives. Consider, for example, ai Amazon and Google shopping suggestions (the so-called recommendation systems), as well as the Netflix’s ability to recommend the right movie in real time to spend the evening. Most users are probably unaware that these services take advantage of the ability of artificial intelligence to analyze their interaction data in real time, but they use it profitably every day.
Initially they were a precious exclusive of large companies. Today, especially thanks to cloud services, AI powered tools are starting to become an available resource for many realities, enabling new business opportunities. Let’s see what applications based on real-time data analysis by artificial intelligence consist of and what are, today, the main use cases in the various business sectors.
Real time analytics: AI and IoT to generate value from data
Real time analytics applications use machine learning algorithms to analyze data acquired from business systems in various application contexts. The machine learning capabilities that characterize this sub-branch of artificial intelligence make it possible to progressively improve the knowledge of the reference context to formulate forecasts useful to support operational choices and strategic decisions. Being able to carry out this process in real time opens up enormous possibilities at a business level.
The sources from which it is possible to acquire the necessary data in real time are extremely varied and largely depend on the type of business and the systems that are used in the respective application contexts. The most common are:
- Interactions with online communication channels
The official website, mobile apps, social media and all the channels that companies use to implement their omnichannel strategies. Users engage in interactions that are captured and collected in CRM and other business systems, where they become available for real-time analytics applications.
The physical data points, including payment systems and other interfaces with which users interface in the various locations where services are provided. Real-time analysis makes it possible, for example, to successfully identify and anticipate fraud attempts.
- Sistemi IoT (Internet of Things)
Typical of home automation applications and industrial systems (Industrial Internet of Things), where special sensors and actuators allow you to control a series of smart devices, whose intelligence lies in the ability to exchange data bidirectionally, in order to respond in real time to operational needs.
The acquisition of data relating to temperature, humidity, pollution or, in an economic environment, to market prices makes it possible to analyze the impacts on the reference context and to make forecasts regarding future trends.
Some real time analytics applications
Technologies capable of acquiring (IoT) and analyzing (AI) data are widely used in various application contexts. Among the most recurring examples of real time analytics we find:
- E-commerce applications to suggest possible purchasing solutions to customers in real time based on their interactions;
- Financial Transaction Monitoring Servicescapable of detecting anomalous patterns which could correspond to fraud attempts;
- Online advertising services (e.g. Google and Facebook) which analyze user behavior in real time to send them relevant commercial announcements;
- Autonomous driving systemswhich use environmental data to guarantee the functioning and safety in navigation;
- Cybersecurity platformscapable of analyzing network traffic in real time, to detect both possible system vulnerabilities and anomalies that could correspond to an IT security attack.
Hybridization and democratization of AI
In most cases, when we talk about real time analytics we are not referring to new technologies. And instead frequent association and hybridization of various artificial intelligence techniques, chosen by data scientists to cope with the variety and complexity of the problem which they are promptly called upon to address.
What is actually changing in predictive data analytics? If until recently machine learning systems capable of enabling this activity were the prerogative of a few tech giants, we are now witnessing a real democratization of technology.
Even start-ups, small and medium-sized businesses can now access cloud applications that allow you to implement real-time data analysis based on artificial intelligence, generating enormous added value to their business. A similar argument obviously applies to data management systems, which are now also available thanks to “as a service” models, which allow any size of company to have what it needs, without facing otherwise unsustainable initial investments and paying only according to the actual needs of their workloads.
The democratization of AI is a phenomenon that follows other similar dynamicswhich we have witnessed throughout the history of technology, just think of what happened with the automation systems of assembly lines, with the birth of the first personal computers and, more recently, with the widespread diffusion of the internet.
Each of these events has proved capable of generating a huge impact on the socio-economic system, revolutionizing the way we work and socialize. The same evolutionary path is currently underway with regard to the growing influence of artificial intelligence in the various applications of daily use.
It is a natural progression, to which the maturation of various AI techniques and methods, from the convergence of which derives the ability to successfully solve problems that until recently were not even addressedas they are deemed overly complex in their nature.
Generative artificial intelligence systems such as Dall-E and ChatGPT, although far from their actual massive implementation, already allow us to appreciate the enormous analytical and creative potential of AI and what is possible thanks to real-time interaction with data made available in each application context.
While awaiting further news on the more visionary and suggestive front of AI, to be expected in the medium and long term, it should be noted that many real time analytics applications already exist, which can be concretely implemented in business processes, to solve the needs of various business fields.
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