Big Data-Generation
Data-Generation
Data can be generated from sources like document, organization, electronic media, and human participation, it is from these sources researchers sample their data collection from. The same sources are used as data collection machines. The collection of data is essential in an organization; it enables the organization to run its operation smoothly. If an organization can control its data, it is categorized as a successful business. Data generation samples extensive data, which is hard to handle if they are not well managed; they can leak and land on the wrong hand (Mehmood, 2016). Information collected by the organization needs to be protected. The significant risk is seen when even deleted information does not get eliminated; the same deleted data can still land on the wrong hands.
Big data means large quantities of data, which require new reasoning and what tools are needed to manage the original rationale. Big data is essential in an organization because it reduces costs in an organization and saves time. This data can be easily leaked due to its large amount. A new management system is needed to be employed to manage extensive data. Big data poses a considerable risk to an organization. One of the significant risks is managing the large number of data the organization has sampled, how to organize and analyze astronomical data is not easy. Securing significant information is another challenge; large data are easily stolen from the system since one cannot track all the data. Privacy is also jeopardized. Complying with the new rules and regulations set for big data is also another risk. Analyzing big data that you cannot understand might lead you to misinterpret the data, this produces unreliable results.
Great benefits are seen when one uses big data in a clinical system due to its efficiency in data collection. Big data is fast since it reduces the traditional sampling of data, which is slow. Cost reduction is also achieved with big data since all data are collected under one system. Big data can be used to detect fraud in the order when the data is well organized; it can also be used to recalculate risk and avoid the risk through data analyzing.
Using big data also has its risks and challenges. Managing big data is not easy; these data needs better organization and competency of the data researcher. Hackers easily hack the same extensive data due to its attraction of enormous size; this data can land in the wrong hands easily since one cannot keep track of all the data at once. Handling big data has rules and regulations set by prominent data managers; complying with these rules and regulations is not that smooth due to strict procedures to be followed before handling the big data. Unreliable results can also be produced when the data manager does not understand the big data. The clinic is a sampling.
The risk posed by the big data can be solved if the organization can separate essential and vital data from all the other data, here the vital data can be retained and stored creating easy management of the data since the volume of the data is reduced (Rathore, 2018). Cheaper options should be made if the organization does not follow the rules and regulations stated for big data. Lastly, hiring a competent data manager reduces the chance of the organization producing unrealistic results that are produced due to a misunderstanding of the data.
To conclude, big data has its advantages and disadvantages to an organization. Efficiency, cost-saving, reduction in time, and fraud are some of the benefits of big data. These advantages may look appealing to the organization, but the organization has to know that managing big data is not easy at all. Privacy and security of the data sampled are also at stake in managing big data.
References
Mehmood, A., Natgunanathan, I., Xiang, Y., Hua, G., & Guo, S. (2016). Protection of big data privacy. IEEE access, 4, 1821-1834.
Rathore, M. M., Paul, A., Hong, W. H., Seo, H., Awan, I., & Saeed, S. (2018). Exploiting IoT and big data analytics: Defining smart digital city using real-time urban data. Sustainable cities and society, 40, 600-610.