Problems and Opportunities created by having too much data, and what to do about them
When you’ve had a chance to read these articles, anything from the Background that is helpful to you, or anything else you may have come across, please write a 3- to 5-page paper discussing the question: Problems and Opportunities created by having too much data, and what to do about them Your paper should be between three and five pages. Take a definite stand on the issues, and develop your supporting argument carefully. Using material from the background information and any other sources you can find to support specific points in your argument is highly recommended; avoid making assertions for which you can find no support other than your own opinion. Your paper is to be structured as a point/counterpoint argument, in the following manner. Begin this paper by stating your position on this question clearly and concisely Citing appropriate sources, present the reasons why you take this position. Be sure to make the most effective case you can. Then present the best evidence you can, again cite appropriate sources, against your position — that is, establish what counterarguments can be made to your original position. Finally, review your original position in light of the counterarguments, showing how they are inadequate to rebut your original statement. By the end of your paper, you should be able to unequivocally re-affirm your original position. The case for this module revolves around the question of large-scale data and the implications of database capabilities for organizational data management. As we’ve said, the change from data as a scarce resource to data as overabundance is still a major concern for organizations. Here, you’ll have a chance to consider the value of data and information. Data storage and management was seldom considered a particularly exciting topic; however, when data is used for making better decisions and/or enhancing organizational performance, it is amazing how quickly organizational (and personal) interest can be created. The new state of having rather too much data to fit into the established databases is increasingly called “big data.” Big data arises from a combination of cheap storage, multiple data input streams, and a general sense that with all of this, there ought to be valuable data in there somewhere. Here are a couple of sources that begin to discuss these issues; you can undoubtedly find more: Challenges and Opportunities with Big Data (2013). Mehrotra, P., Pryor, L., Bailey, F. and Cotnoir, M. (2014). Supporting “Big Data” Analysis and Analytiics at the NSAS Supercomputing Facility. NAS Technical Report: NAS-2014-02. Kaisler, S., Armour, F., Espinosa, J.A., and Money, W. (2013). Big data: Issues and challenge moving forward. 46th Hawaii International Conference on Systems Sciences, 1-10. The trick to coping with “big data” is, of course, better data analytics — that is, that set of statistical mining, and related analytical tools that can be used to identify patterns in the data, assess a variety of associations, and generally illuminate the knowledge that might otherwise be buried in the mounds of numbers. Today, analytics is a rapidly expanding field. Raghupathi, W. and Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health and Information Science and Systems, 2(3), 1-10. ISO/IEC JTC 1 Information Technology Big Data, Preliminary Report, 1-36. Fujitsu (2015). The White Book of Big Data: The definitive guide to the revolution in business analytics. So the case for this module revolves around the challenges of “big data” — that is, how to manage it, create reasonable analysis strategies, and at the same time avoid becoming totally dependent on it. Data makes a very good servant, but not a very attractive master.