Q6 Essay Example | Topics and Well Written Essays - 1000 words

Q6 - Essay Exampleon derivatives into gigantic information warehouses is concerned, the results have been disastrous, because the operational realities make the work of efficient and optium data storage a messy and complex one.In monetary organizations such as banks for example, some of the problems arise because they start to build their data warehouses before figuring verboten what they want in it. To botheiate this problem, effective preparation is necessary a specific purpose must be formulated for the data warehouse.(Gronfeldt, 1998).Experts recommend that common denominators be set up for the data, which are accessible to all departments and extensions be created for oother departments to link to. Creating extensions to data t satisfactorys can make specific relevant informaiotn available to specific departments.An ESRI survey identifies how data warehousing is used in hospitals to enhance Online Analytical processing including a spatial data model can also facilitate p atient profiling and physician profiling.(www.esri.com). Structuring the data warehouse so that it facilitates the aggregation of data and data linking would be helpful in developing suhc profiles.2. Human beings are able to convert data into information through a process of association using external stimuli as well as internal ones such as memory cues. A similar process occurs in Online Analytical processing of data, where data from different sources are associated or linked together in baffle to assess, discover and evaluate existing trends wihtin it. Data is associated with additional streams of data available from other sources and a process of elaboration of the exsiting data can be initiated through the process of Online Analytical processing, to generate hypotheses about the data and assess the consequences of those hypotheses.(www.edc.ncl.ac.uk). Alternatively, patterns existing within streams of data can also be evaluated in this manner in order to generate usable informa tion. For example, data on large numbers