The movement of the information axis to different platforms has enabled the creation of an access, even on the part of companies, across different channels, establishing a process of data transformation. The ability to achieve segmented access, also from private companies, has provided greater integrity of information. For the business arena, this milestone is an important outcome, in order to make the business more efficient, foster growth and increase productivity.
The preciousness of data is a key aspect but, in most cases, it is not easy to access specific knowledge: one of the main problems concerns precisely obtaining centralized access given that information silos are generally decentralized.
Data integration has brought down these barriers, bringing disparate sources to a complete unified view-from assimilating, cleansing, mapping and transforming data, to processing intelligence that is more easily usable by those who access it. It is significant for companies today to implement data integration initiatives to analyze and use information more effectively, particularly with the help of new Cloud and Big Data management technologies. Data Integration is a necessity for the modern company, indispensable for improving decision-making processes and increasing the competitive edge and profitability.
Real-time access is useful for enhancing processes, lowering costs for various departments and increasing numbers. As a result, information from multiple sources is pooled all in different locations, facilitating access and, in general, productivity. Another aspect to consider when discussing data integration is the real possibility of anticipating customer demand thanks to sales histories and a direct conduct of data. In the banking field, the approach just considered is beneficial in outlining the best of strategies useful for customer retention and new, custom-built services.
Given the significant amount of data financial institutions have at their disposal, it has become of paramount importance to have tools available for their analysis. Operations based on Data Mining and Machine Learning algorithms allow data to be studied in such detail that targeted market strategies can be easily identified and implemented. Evolved data analysis empowers the bank to unfold new performance models and new roles in the value chain.
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