The Role of Big Data Analytics in the Private Firms in Nepal

The ideology behind big data has existed for several years from now, and most organizations these days have now understood the importance of capturing it into the data that streams into their quotidian business process. It is also peculiarly true when we happen to use artificial intelligence which is also termed a sophisticated technique. Despite anyone uttering the term “big data in the 1950s, probably a decade ago, businesses still were using basic analytics like numbers in a spreadsheet in order to uncover the company’s trends and insights. In addition, the benefits of big data analytics are considered efficiency and speed (Elgendy & Elragal, 2014). But in today’s context, businesses collect data in real time that helps them to analyze big data, and make better, immediate, and informed decisions. This leads the company to work faster, stay agile, and thereby offers businesses to gain a competitive edge they didn’t have before. Big data analytics is a comprehensive process of identifying big data to unleash information such as correlations, hidden patterns, customer preferences, and market trends that boost the organization’s productivity and make informed business decisions (Russom, 2011). 

Likewise, I definitely believe that the private businesses in Nepal can benefit from big data analytics as it helps in cost reduction, improved data, faster and better decision making, and many more. Not only that the government in Nepal has also been leveraging big data analytics in order to identify and predict potential threats to the country including illegal activities and natural disasters that allow the government to better resource mobilization and planning (George et al., 2014). To concentrate in the private firms, the incorporation of big data analytics into their organizational strategy has instigated highly innovative changes with the abundance of resources and ideas that can turn into knowledge management and be used by further generations. So, let’s have a glimpse of some of the private sectors that benefited from big data analytics.

Manufacturing Sector

This sector is benefited as big data analytics helps them to recognize the pattern so as to improve their entire process of manufacturing and modify variables that help in increasing the efficiency of the supply chain (Dubey et al., 2016). In addition, big data can also be useful in this sector to conduct predictive quality, anomaly detection, predictive maintenance, tool life-cycle optimization, and others. 

Banking Sector

Some of the banks like Nabil, Prabhu, Standard Chartered, and many more have been increasingly using big data analytics for fraud detection and evaluating customers’ spending patterns so that they can identify unusual activities. Banks have also leveraged big data analytics for monitoring their investments and conducting decisions relating to loan screening and mortgage evaluation (Jiang et al., 2014). Well, we can also refer to the Bank of America where they relentlessly use analytics to assess the sentiments of customers in order to retain them for the long- term.

Healthcare

Some of the eminent hospitals like Vayodha, Grande, and Mediciti in Nepal have been facilitated by analytics with groundbreaking research on the treatment and diagnostics. Meanwhile, medical researchers are also using such information to facilitate tumor samples in the databases and identify the reason behind the mutations and the interaction of cancer proteins with several treatments.

Retail and Trade

In the retail and trade industry in Nepal, the role of big data analytics would be leveraging the retail pricing optimization, and shopping patterns, and thus forecasting the demand by referring to website and social media handling. Well, private retail companies in Nepal can refer to giant companies like Zara which is best suitable to analyze a business model that is utilized suavely. In the context of Nepal, e-commerce firms like Daraz have been using these analytics to improve their SEO and flash the correct item in the website and app as per the demand of customers and optimize those pages so that it appears on the top of the search results (Painuly et al., 2021).
So, to discuss further the big data technologies available in the local market would be Apache spark, python programming, Hadoop ecosystems, artificial intelligence, NoSQL,  big data processing computers, and predictive & prescriptive analytics. Even though Nepal lack behind these technologies, a few companies in Nepal have already commenced their initiatives, starting with Nepal Telecom which has invited a particular tender for the cloud platform, big data warehouse, and other technologies relating to big data (Landset et al., 2015). In addition, in many private commercials, A-class banks have been training their staff on this analytics and have been significantly adopting big data technologies. For instance, one of the companies that have recently commenced with its specialization in big data analytics services and warehousing is BSAI techno Sales Pvt. Ltd.
Also, to elaborate on the cost of big data projects, there are several costs related to it. But there is no standard research available that estimates the cost of these alarming technologies in Nepal. However, based upon the common aspects of the costs of the project, the following are the cost associated with the establishment of the project;

  • The upfront infrastructure cost is related to the initiation of the data warehouse, high-speed bandwidth, servers, high capable processing computers, and other big data platforms such as Spart, and Hadoop. Nevertheless, the estimated cost of $1,000 required only for the establishment of a cost of data warehouse could be laid off by using a third party such as Google, Amazon, and Microsoft. Whereas, the private business operating in Nepal can purchase Google BigQuery which is more easily affordable than Hadoop (Chan et al., 2022). 
  • Human resource cost is basically assumed to become one of the challenges faced by the big data technologies that are established in Nepal. Nonetheless, there is a scarcity of capable big data experts that could handle all the emerging complexities such as data scientists, analytics experts, and data engineers. 
  • Maintenance and management costs could be one of the segregation of costs of big data projects that requires setup cost, process cost, and maintenance cost, which also requires a whole sum of the annual payment for the high-speed internet, storage capacity, and processing additional cost. 

Meanwhile, there are several other potential measurable benefits of these initiatives that help the private firms in Nepal facilitate the decision-making process and encompass advanced analytical insights and business intelligence. Some of the benefits include new job creation for the talented pool of Nepalese people, bringing competitive advantage based on the companies that aim to develop new services and products. Also, there comes benefits related to the finances with big data technologies which thereby benefit companies to increase sales lead and reach sales target, and lastly, the return on investment of the company gets higher and faster. Followed by cost cutting, increasing efficiency, improving the company’s pricing, allowing to focus on local preferences, hiring the right employees at the right place, competing in the larger market, and building loyalty among customers (Ogrean, 2018). Ergo, with a still lower rate of digital adoption of big data analytics in both private and public firms in Nepal, it is highly stimulated to gain larger investments in the computational infrastructure of the firm that can maintain the necessary laws abiding data sharing and privacy. 

References

Chan, E. M. H., Ho, D. C. K., Lee, L. W. Y., Yip, T. L., & Gunasekaran, A. (2022). A Big Data Analysis of Perceived Image of the Belt and Road Initiative. In Megatrends in International Business (pp. 105-118). Palgrave Macmillan, Cham.

Dubey, R., Gunasekaran, A., Childe, S. J., Wamba, S. F., & Papadopoulos, T. (2016). The impact of big data on world-class sustainable manufacturing. The International Journal of Advanced Manufacturing Technology, 84(1), 631-645.

Elgendy, N., & Elragal, A. (2014, July). Big data analytics: a literature review paper. In Industrial conference on data mining (pp. 214-227). Springer, cham.

George, G., Haas, M. R., & Pentland, A. (2014). Big data and management. Academy of management Journal, 57(2), 321-326.

Jiang, C., Ding, Z., Wang, J., & Yan, C. (2014). Big data resource service platform for the internet financial industry. Chinese Science Bulletin, 59(35), 5051-5058.

Landset, S., Khoshgoftaar, T. M., Richter, A. N., & Hasanin, T. (2015). A survey of open source tools for machine learning with big data in the Hadoop ecosystem. Journal of Big Data, 2(1), 1-36.

Ogrean, C. (2018). Relevance of big data for business and management. Exploratory insights (Part I). Studies in Business and Economics, 13(2), 153-163.

Painuly, S., Sharma, S., & Matta, P. (2021, July). Big Data Driven E-Commerce Application Management System. In 2021 6th International Conference on Communication and Electronics Systems (ICCES) (pp. 1-5). IEEE.

Russom, P. (2011). Big data analytics. TDWI best practices report, fourth quarter, 19(4), 1-34.

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