BackgroundFrom the last decade, data mining techniques, employed in particular in customer relationship management, have assumed a key role in the profitability and operations of companies. To support small and medium companies (SMEs), several innovative and continuously improving tools have been developed that allow SMEs to utilize the internal and external data sources to increase their competitiveness.ObjectivesIn this paper, an analysis of the impact of digitalization, and in particular data mining techniques, in the context of SMEs development is presented.Methods/ApproachA review of various sources has been conducted, with the focus on open source tools, since in the context of the Italian economy they are used by SMEs the most.ResultsFirst, the analysis presents a brief review of the data mining techniques available and shows how they are practically employed in small companies. Second, an economical review of investments in data mining projects in Italy is presented.ConclusionsThe review indicates that data mining techniques can boost a company in the market. However, the awareness of data mining as a company asset is still not strong in Italian SMEs and most investments in Italy are still carried out by large companies.

Large companies and organizations periodically feed their information systems with large data flows. Apart from the classical operational activities, they are called to prepare aggregated reports to send to institutions and rating agencies. Unfortunately, organizations typically suffer for the lack of integrated data and for the lack of a standard data dictionary. The presented approach aims to tackle such a problem by building a "bridge" between employees that need to specify how to generate reports (on the basis of concepts and terms typical of the application domain) and the information system that stores the data to query and aggregate in order to automatically produce reports. The implemented framework, RADAR (Rich Advanced Design Approach for Reporting), moves from the notion of Operational Data Store, and it is posed in the middle between an ontology (of concepts and terms) and the actual operational (and relational) schema of source data. Then, in the defined schema allows for giving a high-level view of such source data, based on concepts described in the ontology for a specific application domain.

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In this chapter, a study about how Italian SMEs understand and use FinTech technologies is presented. The study focuses on FinTech-aided banking services, in particular, due to the fact that these are, at present, the most widely used FinTech technologies available in Italy. The study shows how, despite FinTech entering Italy only in recently, the Italian SMEs market is very active and fruitful for digital companies. In the last years, a continuous growth of investment has seen the development of FinTech technologies in multiple areas, such as mobile networks, big data, trust management, mobile embedded systems, cloud computing, image processing, and data analytic techniques.

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