SWOT Analysis - Business Analytics

This article provides a SWOT analysis of business analytics, discussing its strengths, weaknesses, opportunities, and threats. It highlights the importance of data analytics in improving decision-making and offers recommendations for implementing data analytics in businesses.

SWOT Analysis – Business Analytics
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Table of Contents
Analysis...........................................................................................................................................2
Findings...........................................................................................................................................3
Strengths.......................................................................................................................................3
Weaknesses..................................................................................................................................3
Opportunities................................................................................................................................3
Threats..........................................................................................................................................4
Recommendations............................................................................................................................4
References........................................................................................................................................6

Analysis
The urgency for efficiency and precision in business dealings has prompted businesses to
employ technology in all areas of operation to be on par with the market demands. From
operational to transactional business systems, from management and scanning facilities; from
outbound and inbound customer contact points and so on. Information received in businesses is
so immense that it requires equivalent ways of dealing with it. Data analytics is the only answer
to this problem, and therefore companies must purpose to invest heavily in data analytics if they
are to maintain a high level of market competitiveness and improve efficiency in their
operations.
Data analytics is usually extensively employed in areas of business like enterprise
decision-making, market optimization, price and promotion modeling, credit risk analysis, store-
keeping unit optimization, and many others. At this point market optimization would be a
worthwhile area for the application of data analytics.
Big data analysts normally want the knowledge that emanates from the analysis of the
data under consideration (Wilcox et al., 2019). Business organizations are continually looking
for methods of finding actionable insight and knowledge in the data under their possession.
Furthermore, most enormous data projects normally originate from the desire to answering
specific questions relating to business operations. These questions might include how to increase
sales, effectively manage the organization's human resources, and methods of cutting
organizational costs. With the right platform of big data analytics, an organization will gain the
capacity of increasing efficiency, boosting sales, improving operations, risk management, and
customer service (Amankwah & Adomako, 2019). Data analytics usually relies on a
simultaneous application of computer programming, operational research, and statistics to help

quantify the performance of data. It should be noted that data analytics employs extensive
computer programming and therefore the algorithms and software used in analytics are the most
current methods in computer sciences, mathematics, and statistics.
Findings
This section will be analyzed using the Strengths, Weaknesses, Opportunities, and Threats
(SWOT) model.
Strengths
(i) Massive cost reduction in business operations. Big data technologies like the Hadoop and
cloud-based analytics are critical in providing significant cost advantages.
(ii) Rather than the mere reason for processing and storage of data, data analytics look to
augment the old data architectures. The long-term goal of augmentation of the two is the
reduction of costs incurred (Ghasemaghaei, 2019).
(iii) Data analytics has always attempted to improve decision-making. Businesses are
looking for faster and more efficient ways of making decisions with big data, and this has
been realized by the used of analytics.
Weaknesses
(i) Lack of a comprehensive approach to big data is a challenge that must be addressed well.
(ii) Moreover, getting the right information to the decision makers should be a priority to
avoid companies from sinking in the humongous amount of information.
(iii) Businesses do not have effective ways of turning big data into effective big
insights.
Opportunities
(i) Information obtained through data analytics should be well documented, with the most
relevant information properly stored?

(ii) Make the information derived from Big Data Analytics available to all departments and
stakeholders (Kumari et al., 2019).
(iii) Ensure that the authenticity of information emanating from the Big Data Analytic
procedure is well maintained.
(iv)A protective mechanism should be adopted to ensure that information derived from Big
Data Analytics is protected from third parties and any unauthorized access.
Threats
Privacy and security concerns: Big Data Analytics concerns itself with analyzing
enormous volumes of data, and this becomes a security issue, and thus experts have to find ways
in which this system does not compromise the privacy and security of the information that is
being analyzed (Côrte et al., 2019).
Multi-national corporations are able to acquire the best data analytics technology in the
market giving them a competitive edge over smaller businesses that cannot acquire such
technologies due to cost.
Recommendations
Data analytics enable the business to make a rational conclusion on some of the data that
are not tapped by the conventional businesses intelligence systems that have been used to help in
making a business decision. Without the use of data analytics in the business, huge volumes of
data that relates to the business can go unattended to given the fact that traditional business
intelligence analysis cannot process them so that important inferences can be drawn from them.
Information such as those from Web servers, activities drawn from the social media reports,
details of the mobile phone calls, information extracted from sensors, and the information from
the internet clickstream is useful when processed using data analytics to check for trends and
patterns in the data (Mikalef et al., 2019).

Big data and big data analytics are able to use this unconventional data to allow business
make informed choices on how to go about their businesses in terms of making steps ahead of
their competitors and increasing their revenue base over time. It is thus advisable for companies
that have long-term strategies in business to go the IT way in most of their activities. This is in
conformity with the fact that timely information in business, more so those that are in a highly
competitive landscape is an essential tool in shielding the competitors off the game. However,
the most important advantage of data analytics is the propensity to bring up new products and
services to customers (Zhang & Xiao, 2019).
Through the use of big data analytics, the company is also able to establish a strong
network of loyal customers that will be attached to their products. Through the system, the
company can list and capture the details of their esteem customer who order or make inquiries
about their products. They will also have an opportunity to treat them like a family in a bid to
retain them in the business for future sales. From the analysed data, they can notice areas where
the products are not popular with the people either because they have not done proper marketing
or because of the strong presence of the competitors. In return, they will be able to make a proper
decision based on their findings.

References
Amankwah, J., & Adomako, S. (2019). Big data analytics and business failures in data-Rich
environments: An organizing framework. Computers in Industry, 105, 204–212.
https://doi.org/10.1016/j.compind.2018.12.015
Côrte, N., Ruivo, P., Oliveira, T., & Popovič, A. (2019). Unlocking the drivers of big data
analytics value in firms. Journal of Business Research, 97, 160–173.
https://doi.org/10.1016/j.jbusres.2018.12.072
Ghasemaghaei, M. (2019). Does data analytics use improve firm decision making quality? The
role of knowledge sharing and data analytics competency. Decision Support Systems,
120, 14–24. https://doi.org/10.1016/j.dss.2019.03.004
Kumari, A., Tanwar, S., Tyagi, S., Kumar, N., Parizi, R. M., & Choo, K.-K. R. (2019). Fog data
analytics: A taxonomy and process model. Journal of Network and Computer
Applications, 128, 90–104. https://doi.org/10.1016/j.jnca.2018.12.013
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm
performance: Findings from a mixed-method approach. Journal of Business Research,
98, 261–276. https://doi.org/10.1016/j.jbusres.2019.01.044
Wilcox, T., Jin, N., Flach, P., & Thumim, J. (2019). A Big Data platform for smart meter data
analytics. Computers in Industry, 105, 250–259.
https://doi.org/10.1016/j.compind.2018.12.010
Zhang, H., & Xiao, Y. (2019). Customer involvement in big data analytics and its impact on B2B
innovation. Industrial Marketing Management.
https://doi.org/10.1016/j.indmarman.2019.02.020