Today it is possible to obtain an enormous amount of data in many ways: in streaming, thanks to IoT (Internet of Things) systems and their interconnected IT devices, which collect and process a continuous flow of data in real-time. Equally decisive is the role of the internet and social media, where people now spend a large amount of their time exchanging data in text, images, video, voice, audio and the same length of time in carrying out a given experience Digital. There is also a series of data relating to the interaction between a user and a service/product. In the case of a company, this concept becomes central to CRM strategies and applications.
The data marketplaces the issue of privacy as a highly topical element at the basis of regulations such as the GDPR, which will necessarily have to evolve their provisions to ensure consistency with the changing framework they are trying to regulate. The first years of the GDPR are, in any case, more than sufficient to give us a perception of the extent of the problem and of how many implications the acquisition and storage of personal data must be considered even in the theoretically most trivial operations. The data market is of enormous value, and its growth prospects seem to know no bounds. And the same can be said for how Big Data management can create added value, at least in the following business areas.
The use of predictive models based on machine learning can manage the Big Data coming from the sensors of a plant to fine-tune a predictive maintenance strategy, capable of predicting the failures and harmful downtime that would result. This approach also allows considerable savings compared to preventive maintenance, as it allows only the interventions that are useful to keep the system in the best functional conditions. Care is only one of the aspects in which Industry 4.0 can benefit from the contribution of Big Data, but the application possibilities are many. Think only of the ability to optimize a supply chain rather than making the life cycle of a product more efficient.
The real-time availability of customer interaction data with the store and the brand’s communication channels can automatically generate purchase incentives, such as the generation of personalized promotional coupons, thanks to predicting a preference based on a history behavior. Real-time marketing is essential for re-functionalizing retail in times when purchasing tends to become an increasingly less exclusive factor, to the advantage of the supply of services, whose contribution to the cause must at least justify the costs, far from negligible, of the premises and the staff employed to manage it.
Digital Marketing And Sales
The marketing and sales sectors, and to a greater extent also the departments dedicated to customer care, can benefit enormously from data analysis to build tailor-made strategies for each customer to acquire more customers, make them more profitable and retain them in the long run. The information coming from the customer relationship data is usually managed thanks to CRM (Client Relationship Management) software. It constitutes a valuable basis for making a wide range of processes more efficient, including improving the offer’s products and services.
In the future, we will be less and less involved in driving vehicles while we travel, and we will have more and more time to live in the cockpit, also performing other functions, including work and social relations. All this is possible thanks to the vehicle’s ability to learn and process a vast amount of environmental data in real-time.
Finance And Investments
Fintech is a privileged area of action of Big Data Analytics, as predictive analysis of stocks can optimize the management of an investment portfolio. This manifests itself according to various dynamics, ranging from the ability to predict the performance of a security based on the ability to assess the performance of its reference market in real-time and all the specific conditions of the companies to which it refers. This allows banks and investment companies to offer a range of solutions and financial products capable of adapting more and more to the needs of savers and investors who turn to them.
Banking Transaction Security
Thanks to artificial intelligence systems, payment management systems can reveal anomalies for routine procedures in real-time, which could correspond to attempted fraud by an attacker. These systems are based on analyzing a data stream detected by the POS. In case of anomalies, they can automatically activate the anti-fraud procedures, which can proceed with the requested operation in a practically immediate condition or stop it in advance.
In addition to banking applications, insurance also represents a historically confident sector with data analysis, especially risk assessment, a fundamental parameter for generating the conditions and premiums of policies. Adopting a complete forecasting model capable of describing and predicting based on a large volume and a large variety of customer-specific data allows us to offer customized products.
Thanks to the data enrichment of traditional processes, insurance companies can: interact more transparently with customers, learn more about their habits and their needs, suggesting ad hoc solutions to optimize final premiums. In the same way, it is possible to exploit the knowledge that derives from the data to create new distribution models and new marketing tools and innovate the business model in an ever more profound way (peer-to-peer policies, on-demand, etc.)
It is evident in the insurance context that the branch of health policies can constitute one of the applicative examples of Big Data in the health sector, which go far beyond the protection aspects. Big Data is fundamental in the diagnostic and forecasting processes of disease risk. Thanks to wearable devices, IT systems can monitor the main vital parameters of users, generating possible alarm bells if the predictive models become aware of possible dangerous combinations deriving from the detected variables. Health monitoring is just one of the many fields where data analytics are helping to improve the efficiency of healthcare applications today.
Big Data is also used to learn and do it better and better. An example is the dating and digitization processes, according to the report formulated by Ben Williamson in 2017. Dating expresses the transformation of education into digital data, translating into structured data of tests, essays, streaming of online courses, etc.. . to produce analysis and cognitive results organized in practical diagrams, graphs and tables.
Digitization is more specifically concerned with coding educational policies into algorithms and applications. More excellent knowledge of the educational processes in place constitutes the basis for continuous improvement and the implementation of profound innovation strategies based on new learning methods capable of increasingly fully exploiting the potential of digital.
Logistics And Distribution
Data analysis is the basis for improving the efficiency of the supply chains of industry and large retailers. A tangible example is an application to optimize supermarket supplies, minimizing warehouse requirements for product storage and the risks of overestimating and underestimating reserves. The forecast models are increasingly accurate as they are based on the historical consumption data about many variables, capable of also considering exceptional conditions, such as consumption close to holidays or particular weather conditions.
Big Data at the citizen’s service would deserve a separate book, many and what are their implications. Data analysis is, in fact, a fundamental enabling factor of the smart city paradigm, which is based on a system of interconnected and monitored services with IoT logic. Services such as automatic parking, monitoring of environmental parameters, and management of networks generate vast data flows, whose analysis depends on the progressive improvement of the urban ecosystem.