Big Data Challenge
In the contemporary world, owing to the fast development in the use of the internet and its linked tools, every day, a massive amount of data is being generated. The notion of big data turns up when we were incapable of controlling huge data with conventional techniques. Big data is a system of capturing, keeping, and scrutinizing the big datasets and besides a plan of extracting some value from it. It is extremely handful while establishing the main causes of issues, defects, and failures in almost-real time, generating coupons and additional sales offers in line with the consumers’ patterns of shopping, discovering any apprehensive and deceptive activities immediately (Nasser & Tariq, 2015).
However, as it is very helpful, as well as numerous challenges, one major challenge is data integration and organization. Today, big data develops continually, and institutions regularly fail to obtain prospects and extort actionable data. Organizations frequently fail to establish where they ought to apportion their resources. This failure in distributing the resources bring about not making the majority of the information. Despite that, institutions regularly end up with talents that do not know how they must use big data analytics. Scarcity of qualified workers who can draw out information results in an organization not making the majority of the information held by them (Webb, 2020). Moreover, while extracting cognizance from the big data held by the organization, they fail to discover the precise purpose and wind up with cognizance that is significant to their development.
Another challenge is data security. According to Nasser & Tariq (2015), when companies store bulky amounts of data sets, these sets comprise of nearly all sort of data or information that is still minutely vital for the organization. Consequently, when management fails to establish appropriate measures of security, they are vulnerable to data theft threats. Data theft means that an organization is missing out on important information. Additionally, data theft can further divulge confidential data or information that the organization has concealed for a very long time. Therefore, this could connote a fatal impact on the reputation of the business. Client information is the main target for most attackers. With stealing significant information like that from an institution, attackers can then sell it to other organizations for financial gains.
Data quality is also a big challenge for big data. Undoubtedly, erroneous data can possibly result in defective insights. When data warehouse/lakes data, try to merge inconsistent and unstructured data from varied sources, it meets errors. Missing data, incompatible information, logic variance, and data duplicate all effects in information quality challenges (Hariri et al., 2015).
Timely insights are another major problem. As organizations move to data-driven decisions, it is crucial to have accurate data at the accurate time. However, big data analytics are important to companies only if they can extricate insights from their specific big data and then act in response to those insights swiftly.
To conclude, these challenges are widespread across a huge range of application domains, and hence not-cost effective to deal with them from the perspective of one domain. Moreover, these challenges necessitate transformative resolutions, and therefore, basic research towards these challenges should be supported and encouraged in order to attain the anticipated advantages of Big Data. Lastly, challenges with big data can be avoidable with appropriate solutions. Companies should try their best to implement procedures that will secure their big data. Basic adjustments in the infrastructure might be necessary to guarantee that the data remains secure and utilizable.
References
Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019). Uncertainty in big data analytics: survey, opportunities, and challenges. Journal of Big Data, 6(1), 44.
Nasser, T., & Tariq, R. S. (2015). Big data challenges. J Comput Eng Inf Technol 4: 3. doi: http://dx. doi. org/10.4172/2324, 9307(2).
Webb, R. (2020). 12 Challenges of Data Analytics and How to Fix Them. Retrieved 2 July 2020, from https://www.clearrisk.com/risk-management-blog/challenges-of-data-analytics