Free Essay Example. Big Data Hype or Mining

Published: 2023-04-10
Free Essay Example. Big Data Hype or Mining
Type of paper:  Essay
Categories:  Data analysis Information technologies Business strategy
Pages: 6
Wordcount: 1578 words
14 min read

The modern epoch is characteristically immersed in a pool of immeasurable technological advancement. Notably, technology is radicalizing all human existential spheres that is the political, economic, social and cultural domains. Consequently, for both profit and non-profit organizations, any attempt to remain technologically rigid is undeniably a catalyst for calamitous repercussions (Karpurapu & Jololian, 2017). For businesses with an insatiable craving for the accruement of profitability, which is very gravitas for organizational functionality and operatory scope expansion, the pursuit and adoption of new business and management technologies is not an option but an inevitable prerequisite. One such pivotal business technology is big data. Essentially, big data refers to vast volumes of data which can either be structured or unstructured and floods businesses daily (Pyne et al., 2016). However, the fundamentality of the data is not its quantity but what an organization uses the data to accomplish. Therefore, big data can be analyzed to obtain gainful insights that are very critical in making apropos business decisions and strategic moves.

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The Pros of Big Data

Summarily as described above, big data denotes data, which is very vast, complex and fast making it very hard or even not possible to process through the application of traditional methods (Sharma et al., 2017). However, one of the prime focal points of big data is breaking down or easing the process of storing as well as accessing vast quantities of information. More so, the utilization of big data in business operations is characterized by several pros spread across technological, ethical and managerial scopes. Hence, some of the pros of big data include:

Better Decision Making

The leading advantage of the use of big data analytics is its knack to improve the process of making pertinent decisions in an organization. Therefore, the previous reliance on anonymous bases when making decisions and deductions by organizations has been replaced by big data analytics bases (Pyne et al., 2016). More so, when making decisions through the application of big data analytics, vital customer-centered factors are considered. Some of these customer-centered factors include customer needs, solutions to customer needs problems, and the analysis of the needs as per the current market trends.

Promotes Innovation

The contemporary world is undeniably inclined towards and supportive of innovation trends. Notably, innovation is a very vital necessity for the prosperity of organizations. However, data is an inevitable raw material for innovation. Hence, big data is a prime source for the vast array of data needed in innovation enabling individuals to make unthinkable innovation achievements (Sharma et al., 2017).

Several companies heavily rely on big data analytics in the creation of new products and services for their clients. Through big data, companies are able to analyze several customer opinions regarding their products as well as the perception of their products in the market by customers. Hence, big data acts as a source of very vital information regarding what an organization's product or service is lacking and the fundamental prerequisites to be observed when developing future products (Hussain & Roy, 2016). Companies are therefore in a position to develop products which meet customer needs hence rubberstamping the verity that big data provides the ability to think beyond the ordinary level.

Increases Sales and Loyalty

Big data is a great deal in revealing critical digital footprints left behind which is important in providing accurate information about customer shopping beliefs, and preferential trends among other aspects. The data enables a company to tailor its products and services to the precise needs of clients (Pyne et al., 2016). More so, a digital footprint is left for reservation or observation when clients browse online and then post on social media platforms online hence increasing overall sales and loyalty for example through online reviews.

Nevertheless, briefly, other pros of big data include time reductions, saving on costs, the development of new products, control over online reputation, ability to understand the conditions of the markets, improved efficiency, improved capacity to compete large businesses among other advantages (Karpurapu & Jololian, 2017).

The Cons of Big Data

Susceptible to Cyber Security Threats

The storage or reservation of big data especially data that is very sensitive exposes organizations as attractive targets of cyber-attacks (Sharma et al., 2017). Hence, to avoid cyber-attacks, it is critical for companies to invest in cyber security which may be expensive.


Compliance with government rules and regulations is another thorny challenge facing big data analytics. The laxity by some organizations in complying with the government is mainly due to their data reservoirs including very sensitive and private information. Therefore, companies are forced to meet government requirements and industry standards in the handling as well as storage of data.

The Need for Data Talent

The handling of big data demands experienced and qualified data scientists. However, data experts and scientists are highly paid since they are highly coted. Consequently, a move by an organization, to train or hire its staff will automatically be an added and costly expenditure coupled by the verity that the process of data skills acquisition might be very lengthy (Pyne et al., 2016). Also, other big data cons include high costs, hardware needs, rapid changes, the need for cultural change, the quality of data and the challenges of integrating legacy systems.

Impact of Big Data on Stakeholders

The prevalent use of big data present both positive and negative impacts to its major stakeholders. Importantly, big data key stakeholders include shareholders, partners, employees and customers.


The positive impact of big data on employees is its ability to recruit as well as retain top talent hence enabling an organization to get the best from its employees (Sharma et al., 2017). However, for employees to work to their level best, it is important that a company provides relevant tools and applications that is essentially reliant on the available reliable and useful data. However, the negative impact is that with bad data, employees will be slowed, they will feel their performance is jeopardized, less job security and morale thus culminating into a decrease in their productivity.


As a positive impact, transparent big data of a company acts as its magnifying lens concerning business operations which in turn attracts more shareholders. Also, by ensuring more protection and security of sensitive data as well as truthful reportage of a company by ensuring audibility, trust and transparency, big data helps to attract more shareholders willing to invest. However, for a company's big data with gaps on quality, masking and management, the resultant negative impact is high costs of operations and high risks of government or regulatory fines (Karpurapu & Jololian, 2017).


Pertinent and strategic big data on distribution, resale and supply chain elevates an organization's reputation, improve its market share and ultimately the prosperity of the organization (Pyne et al., 2016). However, for example, the negative impact of poor-quality big data supply is inappropriately negotiated business contracts resulting into costly breaches along the supply chain.


Big data application impacts positively on companies by guaranteeing customer loyalty. Also, another positive impact of big data on customers is the delivery of a great and exceptional customer experience. However, on the negative side of big data, if the data is bad, the resultant impacts is bad sales, marketing, service experience, fulfillment and support.


Big data usage is not principally a reserve for big businesses. Small businesses also have the ability to utilize big data in the making of data-centric decisions that will enable them to reap several benefits. Small businesses should therefore analyze the entirety of surrounding offline as well as online information which will help to project them towards growth. Essentially, small business ought to maximum benefit from the arrival of big data revolution prevalent in wireless networks, the internet, the social media, and smartphones among other technologies.

Also, since data powers almost everything if not all, companies have essentially just begun unraveling the potentiality of big data (Sharma et al., 2017). However, regardless, of the company, business, organization, or government's need for big data, it is very critical for operations in big data to develop pertinent practices with the real-time capacity to ensure that the protection of users' private information (Pyne et al., 2016).


The modern epoch is undeniably characterized by the creation of vast quantities of data whose worth cannot be assumed. More so, these data should be taken with the much critical significance it deserves. Notably, if properly exploited, big data analytics is very beneficial in the production of incredible outcomes. Primarily, big data provides the much-needed power for achieving that which is deemed unachievable. Therefore, it is not an option but an inevitable prerequisite for both small and large organizations and businesses to embrace big data before it is too late.


Dollah, R., & Aris, H. (2018). A Big Data Analytics Model for Household Electricity Consumption Tracking and Monitoring. 2018 IEEE Conference on Big Data and Analytics (ICBDA).

Hussain, A., & Roy, A. (2016). The emerging era of Big Data Analytics. Big Data Analytics, 1(1).

Karpurapu, B. S., & Jololian, L. (2017). A Framework for Social Network Sentiment Analysis Using Big Data Analytics. Big Data and Visual Analytics, 203-217.

Pyne, S., Prakasa Rao, B. L., & Rao, S. B. (2016). Big Data Analytics: Views from Statistical and Computational Perspectives. Big Data Analytics, 1-10.

Sharma, N., Sawai, D., & Surve, G. (2017). Big data analytics: Impacting business in big way. 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI).

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