Abstract
In the last decade, the configuration of the banking market has seen a steady increase in competition levels, 7 owing to the relaxation of restrictions on banks’ operational independence, to the entry of nonbanking operators into the payments system, and to the banks’ new role in the financial market (Banca d’Italia, 1996).
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We refer to the innovations introduced by EU directives on banking coordination, and, in Italy’s particular case, to the new overall regulation system for the banking industry.
Porter and Millar (1985) in How Information Gives You Competitive Advantages, classify different industrial sectors through an information intensity matrix. The said matrix allows analysis of the needs in terms of information intensity of processes and contents for the products/services typical of each sector. They then give an evaluation of existing and potential information intensities of processes and products, stating that these can determine role and dimension of IT in every industrial sector. Porter and Millar demonstrate how IT is a fundamental variable in all activities carried out by the bank. They do this by focusing on the needs and information contents typical of the banking sector which, in order to fulfil its objectives, uses IT in a consistent and systematic way. They thus conclude by saying the high information contents in products/services and the high information intensity in processes make the banking sector one of the most enthusiastic in the use of IT. On the same issue see also Rossignoli (1997).
it is interesting to remark that artificial intelligence systems, which are now included in data mining systems, are a technology that was already available in the 1950s. However, they partly owing to a natural reaction of resistance to change among management, partly because of the high implementation costs, and partly because of technical factors making it impossible to implement them, such as difficulties in accessing and reusing company data.
On this issue, CeTIF carried out a research study in . Forty banks of different sizes were involved (accounting at the time for 50% of the profits of the entire banking sector). Five different areas were explored: control over several sectors of the financing area, management control, management information system, strategic planning and ALM (Asset and Liability Management). It emerged from the results that while in the first two sectors information systems enjoyed a certain, if not sufficient, diffusion, the third and fourth areas had a limited use for such tools, since many activities were still performed by hand and in the fifth there was an extremely low spread of information systems, because there were few companies implementigg innovations.
Such as data modelling, information engineering, and other approaches.
In this case we refer to data contained in company archives and to data coming from information providers, or from the Internet, or from the banking system in general.
On Line Transaction Processing. It refers to on-line-updated operational systems.
An example is supplied by the data on a certain geographical area, or data from the Risk Centre, or those originating from information providers such as Reuters or Bloomberg.
Sometimes „datamart“ is used to mean a number of decision support systems, isolated from one another and oriented to specific business problems. On other occasions the term is used to refer to server-based, local applications, which are fed by a central DWH. They are also called subsidiary datamarts.
It is possible to have a data mart for the loans area, one for the financial area, and so on.
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© 2003 Springer-Verlag Berlin Heidelberg
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Rajola, F. (2003). The Organization of Data Warehouse Activities. In: Customer Relationship Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24718-0_4
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DOI: https://doi.org/10.1007/978-3-540-24718-0_4
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