Big Data Problem Causes and Exposition - Essay Example

? Big Data Problem Big data is a collection of sets of data that are very large and compound which are challenging to capture, store, search, transfer, analyse or visualise using simple method and using the usual database that analyse small data. In the past data was captured, stored in units of bytes or megabytes or even gigabytes and these units were considered very big and posed substantial challenges in handling and synthesising. With advancement in technology, the units of measurement of data increased to terabyte and later to petabyte which was replaced by the latest units of exabytes. With these units being used, people faced trouble to get part of the data or use it to develop a trend of events or occurrences in an environment which can be used to ensure it becomes useful to its owner (Sathi, 2012, p4). Some of the organisations that use big data include meteorological organisations, biological and environmental research complex physics simulations among others. These find it difficult to establish amicable to capture, store transmit and analyse some of their data that are collected from different events and analyse them to have necessary deductions. Units for measuring the amounts of data have continued to be invented with time and they are moving from simple to extremely complex and large figures that require large capacity to store. To understand the problem of big data, it is good to focus on the analysis of the issues of data analysis as could be realised in different organisations within the world (O'Reilly Radar Team et al, 2012, p8). Big data problem causes and exposition In the past, people used to measure data in megabytes and in those times, 100 megabytes of data were considered a very large. With time, there data increased and gigabytes were used to measure data, this paved way for terabyte, petabytes and the latest invention of the units of measurement of data is the Exabyte. Exabyte is the largest possible unit of data that can be processed through a system in a considerably time, reasonable enough to be considered effective (Mayer-Scho?nberger and Cukier, 2013, p6-8). In attempt to analyse big data various problems are witnessed in some departments which carry the information for the world data or other information large enough to be contained in traditional database. The big data has not gone without considerable problems in the way it ought to be handled and processed to explain phenomenon and trends in business or in any other organisation. Some problems experienced in handling big data include collecting, processing, analysing and storing of the meaningful data for future use (Ohlhorst, 2013, p11). However, in order to expound the problems of big data, analysis of the same offers an insight into the difficulty of handling that kind of data and the risks thereof. Why analysis of big data is a problem Analysis of big data is problematic because there are limited methods, which can be used to store large amount of data in the same place and process desirable results using it. For this reason, analysis is one of the problems of big data in that it is difficult to combine the different bits that are making the big data. As a result, analysis of such data of great magnitude becomes difficult because there are few devices that accommodate data to a certain capacity and is type. It is tasking to engage in analysing big data because in their unstructured form, they indicate that people have taken part in an event but the information is decentralized into a common place for effective analysis (Best, 2008, p63). How big data is analysed To enable ease of analysis, scientist start by classifying the data into groups and categories, this can be easily analysed to give the required information. The method of analysing big data by structuring is a scientific invention that is designed to ensure that organisations do not have to delete their data from their database. Companies categorise their information into clusters that are representing data for different events and subject it to the system of analysis that processes the data to give trends according to the various factors that are represented (Smolan and Erwitt, 2012, p33). Importance of big data analysis Analysis of big data is of essence to large organisations as it allows storage of large sized data in its synthesised form without suffering the consequences of deleting the data and making it difficult to retrieve in future when it is required. Through the developed system of analysis of large data, it is possible for an organisation to store large amount of data according to different structures of the data. On the other hand, analysis of big data helps organisations to track their progress from different times and assists them to trace failures if they occur in the course of their operation (Franks, 2012, p15). Challenges of big data analysis In the analysis of big data, there are challenges that are encountered which are related to the security of the data that is put in the system. This is because if unauthorized people get to the data, they can manipulate it destroying the records of operations of an organisation (Smolan and Erwitt, 2012, p53). Another problem that threatens the data analysed in the big data system is that if scientists do not write an algorithm for filtering some information, if data is subjected to the system of analysis, it is lost completely with no chances of retrieval (Dasgupta, Papadimitriou and Vazirani, 2008, p5). This means that an organisation may lose its data through a slight mistake in making a system of analysis, meaning that even the trends that come from such systems cannot be relied upon (Srinivasa and Bhatnagar, 2012, p122-131). Methods of analysing big data In order to analyse big data, people have opportunities to choose from the different systems that are used which give the required results within the stipulated time. For instance, an individual or an organization may opt for the cloud systems, which combine different servers, which have stored bits of the data or vCore model, which optimizes the different subsections of the data, and subject it to proper analysis (Fiore and Aloisio, 2011, p12). Once the information is fed on a big data analysing system, different people who have access to that system can potentially access it. Another issue that affects the data when it is subjected to analysis by any of the models is that a mistake in writing an algorithm may lead to total loss of data (Franks, 2012, p33-37). Conclusion In the process of development, organisations are always taking parts in operation in which they produce substantially large sets data. Scientists have come up with different methods of handling it in different processes and stages and analysis, data is dealt with using two major models i.e. cloud database and vCore methods which have the potential to handle big data and prevent companies from losing data. These models are, however, not totally perfect for they risk loss of data in case there data is mishandled or improper algorithm is done in analysis. References Best, J. (2008). Stat-spotting: A field guide to identifying dubious data. Berkeley: University of California Press. Dasgupta, S., Papadimitriou, C. H., & Vazirani, U. V. (2008). Algorithms. Boston: McGraw-Hill Higher Education. Fiore, S., & Aloisio, G. (2011). Grid and cloud database management. Berlin: Springer. Franks, B. (2012). Taming the big data tidal wave: Finding opportunities in huge data streams with advanced analytics. Hoboken, New Jersey: John Wiley & Sons, Inc Mayer-Scho?nberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Boston: Houghton Mifflin Harcourt. Ohlhorst, F. (2013). Big data analytics: Turning big data into big money. Hoboken, N.J: Wiley. O'Reilly Radar Team., Dumbill, E., Croll, A., Steele, J., & Loukides, M. K. (2012). Planning for big data. Beijing: O'Reilly Media. Sathi, A. (2012). Big data analytics. Boise: MC Press. Smolan, R., & Erwitt, J. (2012). The human face of big data. Sausalito, Calif: Against All Odds Productions. Srinivasa, S., & Bhatnagar, V. (2012). Big data analytics: First international conference, BDA 2012, New Delhi, India, December 24-26, 2012 : proceedings. Heiderberg: Springer. Read More