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With the current dynamic and competitive business environment, the employment of big data analytics has turned into a fundamental approach for organizations to not only obtain a competitive advantage but also ascertain insights for the improved decision-making process. As a result, the concept has become a trending practice with numerous companies adopting it with the aim of driving new revenue streams and enhancing operational efficiency. Nevertheless, despite the abundant benefits, the central issue emerges in the data management and processing aspect whereby most firms in spite of possessing vast quantities of data face the challenge of the most appropriate and economical manner to analyze it in a way that brings value to the organization (Sivarajah, Kamal, Irani & Weerakkody, 2017).
Other challenges that businesses may encounter include privacy and security issues, inadequate infrastructure, difficulties in the conversion of unstructured to structured data, maintenance of data quality during storage, and fault tolerance problems (Mishra, Dhote, Prajapati & Shukla, 2015). Moreover, according to Harvey (2017), other common issues related to big data consist of dealing with data growth for the quantity of information stored in the global information technology systems keep doubling after two years, generating insights promptly, and the recruiting and retaining of big data talent.
Determining the most efficient way to extract insights from the stored big data, as mentioned above, is the primary concern for most organizations. This issue arises because there has been a rapid data increase in the digital environment with a speed that is perceived to out-pace the advancement in computing infrastructures. As a result, existing data processing technologies, such as data warehouses, are becoming insufficient and inefficient in the evaluation of the stored data. Due to competitiveness, companies are also expected to analyze the vast amounts of data in a time sensitive and iterative manner. Furthermore, businesses also realize a problem in the selection of the best type of analytical process or tool in the handling of structured, semi-structured and unstructured data to reveal any unknown correlations, uncovered hidden patterns, as well as other information substantial in achieving enhanced decision-making.
One of the most affected industries by the challenges of big data analytics is the healthcare field. The current medical practitioners are expected to successfully incorporate data-driven acumens into their operational and clinical operations (Fatt & Ramadas, 2018). Possession of such knowledge not only requires the medical specialists to be computer literate but also to continually improve their technical expertise. Besides, not all data is legal and useful in the healthcare setting, and hence the healthcare providers have to capture data that is correctly formatted, accurate, complete, clean, and flexible to be used across multiple organizations. On the other hand, healthcare information is subject to a wide array of vulnerabilities ranging from hacking and high-profile breaches to ransomware episodes.
However, with the proper measures in place, the challenges associated with big data analytics can be effectively mitigated. For instance, the issue of having the necessary data processing technologies can be solved through the application of advanced big data analyzing programs such as Hadoop and NoSQL Databases. The latter program can also be used in addressing the issue of handling different data types. On the other hand, security and privacy issues can be resolved through the employment of multi-factor authentication, encryption of sensitive data, setting up firewalls, and maintenance of up-to-date antivirus software. Moreover, if I were a member of a company with big data analytics challenge, I would stress on equipping all employees with the proper skills and knowledge in handling and evaluating big data. Constant education of the workers would also guarantee reduced security issues as well as ensure generation of insights in a timely manner.
Fatt, Q., & Ramadas, A. (2018). The Usefulness and Challenges of Big Data in Healthcare. Journal of Healthcare Communications, 3(2), 1-4.
Harvey, C. (2017). Top Big Data Challenges. Retrieved from https://www.datamation.com/big-data/big-data-challenges.html
Mishra, S., Dhote, V., Prajapati, G., & Shukla, J. (2015). Challenges in Big Data Application: A Review. International Journal of Computer Applications, 121(19), 42-46.
Sivarajah, U., Kamal, M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286.
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