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4.1 DATA COLLECTION The data that is stored today does not arise on itself. It is generated through some or other activity or happening. There are multiple sources for a data to be storable. Take the example of storing the data of Indian Citizen for the project of UID(Unique Identification), that is gathered for the project of issuing of Aadhar Cards to all the residents of India. For that single project, the data recorded would be billions of bytes, as the data gathered for individual comprises of three things, Iris image, Headshot and finger prints. This can easily produce an ample of data which will be difficult……really difficult to manage. Further, all of the data gathered is not always helpful. There has to be a filter at the very start point of the data gathering process. Otherwise there are very much chances that irrelevant data is collected by the system, in a scenario where data storage itself is causing problem. In the same case of data storing for Aadhar cards, we have to be sure that the correct & relevant data is being gathered. See for example, if one of the censors of the machine taking Iris images is faulty then it is bound to affect the quality of data gathered. And in that case although ample of time and money is being spent on collection of data, but the data collected is wrong. While making sure that we are not storing a type of data which is irrelevant in nature, we have to also keep in mind that we are not discarding the useful data. Along with it, we have to be careful enough to analyse the data at every possible points to ensure that we are collecting right data, because we cannot afford to collect the wrong data, store it for some time & then discard it after the knowledge of it being wrong. Another import... ... middle of paper ... ...articular desired way, but also enabling them to use the same data to have another set of interpretations using different methods of analogy, their own assumptions, different parameters etc. It is important for the presenter of the data to present it in a manner that the business i.e. ultimate user is comfortable with. Where early systems were presented in tables, today it has to be more visual. The data should be user friendly. The user should be able to reach its desired part of the data within a few clicks. Users should not only understand the results but also how the results were arrived at. Sometimes the analysis can be too technical for some users to understand. One alternative can be to enable user to make changes to say e.g. parameters. The user can then see the modified view. Because of this the user can relate itself to the data & use it more conveniently.

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