1. OIM7502-B Business Data Analytics
Answer:
Introduction
Data analytics has proven to be one of the fields that is very crucial in the current century. It
is one of the emerging technology that is being adopted by almost every field. This is
because they have been a massive generation of data sets from the recent technological
improvements. Companies such as oil and gas exploration and micro-finance have massive
data sets and managing them have been one of the major concern. A study conducted by Bi
& Cochran (2014) shows that a lot of energy is taken into collecting data and it becomes a
challenge to manage the data. This is where the concept of data analytics come in place to
assist. Data analytics is a new technology that enable the data handling and processing.
If data is well managed, it can provide insight for business that can enable effective decision
making (Jeble et al. 2018). For a company to succeed, it should employ the concept of data
analytics to be part of their top priorities. Data analytics will help them make great and
effective marketing strategies. Data analytics will Certainly Gain Momentum for the
foreseeable future and will one of the new technology solutions.
Present Role Of Data Analytics
Currently, companies are using data analytics to be relevant in the market. This is because
they have been an increasing competitive pressure to acquire customers and also to
maintain them. The need for the business to understand their customers and improve the
customer relationship is one of the current boost of data analytics. Understanding the
client’s needs and experiences reduces the churn rate and thus increases the portfolio of
any business. One of the factors that hinders business growth is churn rate. The challenge of
churn rate can be reduced by the application of data analytics. Data analytics can be used to
analyze the customers’ response towards the services rendered by the business. As a result,
the company is able to work on their loopholes.
Firms are using data analytics to guide their business into making effective decisions and
this go in line with the minimization of financial losses and risks. Predictive analytics, one of
the techniques of data analytics, suggests what could happen in response to changes of the
2. operations of the business while prescriptive analytics is being used to indicate how the
business should react to these changes. This is done by using different statistical models
and techniques such as regression analysis, time series, A/B test etc. Business are also
adopting the concept of visualizing their performance based on their targets using data
visualization tools. This help them to make decision based on their previous performances.
One of the channels where businesses use a lot of money is advertisement. Previously,
companies were just advertising for the sake. As a results, companies would use a lot of
money to advertise without any substantial results. Therefore, companies need to use the
concept of target advertising and marketing. Through data analytics, companies are now
able to have a targeted advertisement and marketing. This is because data analytics will
enable the business to understand their customer and thus, they will know which
customers to target. Insights obtained from data analytics allow companies to create
successful and target marketing (Maheshwari et al. 2021; Rizwan et al. 2018 )
Future Role Of Data Analytics
One of the factors that will delay the use of data analytics is the problems associated with
the data such as data quality issues. However, it is expected that in the next 20-30 years,
businesses will greatly reap from data analytics. The development of data analytics will
enable it to solve the current issues and thus, some current professionals will be rendered
obsolete. The impossible will eventual become possible. The use of data analytics is
expected to change the way of living and the ways businesses will be carried out in the
future. Currently, we are in the early stage of data era. Companies are now investing in data
analytics capabilities to keep up with known and unknown competitions and developments.
Currently, the known data analytics development cycles is grouped into different stages
such as descriptive, which described what happened to diagnostic which descried why it
happened, to discovery, described what can be learnt from the results, to predictive which
describes what is likely to happen to prescriptive analytics which gives an action plan.
Companies find themselves in the diagnostic and discovery stages.
In the future, the process of decision making will be automated and thus giving better
results. This will be made possible by the use of artificial intelligence. For instance, we have
seen autopilot updated in Tesla model S cars and we except much of automation in the
future.
Data analytics will change the way we live. Several activities will be automated. For
instance, driving cars will be automated. We will not need to individually drive our cars.
Cooking will be automated etc. This kind of lifestyle will be normal in the future. Some jobs
will be rendered obsolete in the future. Some of the people will be rendered jobless. For
instance, those working as accountants will be rendered jobless in the next 20 years. This is
because 94 % of their work will be computerized. On the same hand, new jobs will arise and
jobs will change. The jobs will be skewed towards the development of machine learning.
3. Challenges Of Data Analytics
One of the challenges of data analytics is the issue of data quality. Data quality affects the
results obtained from the analysis and as a result affects the decision making. Wrong data
will not help any business and in fact, it will mislead it. Therefore, data need to be cleaned
first before conducting any analysis. The process of data cleaning is time consuming. Data
cleaning takes about 80 % of the time taken to do the analysis (Hariri et al. 2019).
So much data is available and it is difficult to dig down and access the insights that are
needed most. Employees might be overwhelmed with the amount of data and therefore,
they may fail to fully analyze the data. The may focus on certain measures that are easiest to
collect instead of those that are valuable to the company (Vassakis, Petrakis & Kopanakis,
2018).
The process of presenting the data is time consuming and difficult. For instance, the process
of data visualization needs an analysis to collect information from different areas and put
them together in order to come up with meaningful visualization. The whole process is time
consuming (Najafabadi et al. 2015).
Opportunities Of Data Analytics To Business
Data analytics enable businesses to personalize the customer experience. Businesses collect
data from the customers and when this data is used well, the business can obtain insights
into customer behavior and thus help the business into decision making.
Enterprises can use data analytics to guide the business into making effective decision and
also to minimize financial losses. The use of predictive analytics can guide business to know
what could happen when there is changes to the business. On the other hand, prescriptive
analytics indicates how business can react to such changes (Schneider et al. 2015).
Data analytics can be used to mitigate risks and handle setbacks. There exit risks in every
business. The risks ranges from customer or employee theft, employee safety, legal liability
etc. Through data analytics, the business can understand risks and take preventive
measures. Statistical models can be used to predict future risks and these help businesses to
plan ahead (Sedkaoui, 2018).
References
Bi, Z., & Cochran, D. (2014). Big data analytics with applications. Journal of Management
Analytics, 1(4), 249-265.
Jeble, S., Dubey, R., Childe, S. J., Papadopoulos, T., Roubaud, D., & Prakash, A. (2018). Impact
4. of big data and predictive analytics capability on supply chain sustainability. The
International Journal of Logistics Management.
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), 1-16.
Maheshwari, S., Gautam, P., & Jaggi, C. K. (2021). Role of Big Data Analytics in supply chain
management: current trends and future perspectives. International Journal of Production
Research, 59(6), 1875-1900.
Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E.
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perspective. IEEE Access, 6, 41903-41920.
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Sedkaoui, S. (2018). How data analytics is changing entrepreneurial
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