NPU Data Analytics Discussion Reply

Respond to these posts in any of the following ways: 200-300 minimum words each

• Build on something your peer said.
• Explain why and how you see things differently.
• Ask a probing or clarifying question.
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• Offer and support an opinion.
• Validate an idea with your own experience.
• Expand on your peers’ postings.
• Ask for evidence that supports the post.

Discussion : 1
Data Analytics is a set of practices and activities used to derive meaning
from existing data to predict future trends, create new data or improve
existing operations. Data Analytics can be applied to the three different
levels of data; descriptive analytics, predictive analytics, and
prescriptive analytics. The critical difference between the three levels is
how much data is used in analytics. The descriptive level of analytics is
usually based on data that is easily accessible such as sales and
marketing data (Hassan et al., 2022). Predictive analytics is used to
identify the characteristics of individuals, communities, or groups. It
may use historical and current data for predictive analysis. Prescriptive
analytics usually focuses on optimizing the most efficient use of a
resource to make the best return on investment. It is based on the past
and predicts the future. Predictive analytics is usually used in a
predictive model. The best example of Predictive Analytics is the use of
credit cards for predictive modelling (Mariani & Nambisan, 2021).
Data analytics is an enterprise data mining and visualization tool that
enables companies better to understand their customers, products, and
processes. It helps them in business intelligence, planning, forecasting,
and other vital areas. It also allows them to analyse historical and realtime data to spot issues that could lead to inefficiencies, reduce or
eliminate any waste in resources, and improve customer experience. The
key driver behind the growth of data analytics is the growing usage of
data to drive new business insights (Mariani & Nambisan, 2021).
Enterprises are investing in data analytics to deliver business value in
almost every aspect of their business. Data analytics can help them
improve the services they provide to their customers and enhance
customer relationships. This enhances the customer experience and helps
retain existing and attract new customers. Companies have started using
various data analytics tools, such as business intelligence (BI) solutions,
for multiple purposes. For example, BI software helps companies use
data to analyse different customer behaviours and interactions. In
addition to the benefits of improved decision-making and customer
relationships, the software also helps organizations make essential
business operations more efficient and less costly (Hassan et al., 2022).
There is a need to analyse and extract some of their data for most
organizations. Typically, multiple sources of data may need to be
collated and analysed. Data Analysis is a relatively new field of data
science, so organizations have a lot to learn and understand before
making the best use of data analytics. The Data Science approach to
Data Analytics involves the use of multiple techniques, methodologies,
and tools to create the process of Data Analytics (Hassan et al., 2022).
One of the main benefits of data analytics is identifying new
opportunities or patterns that may go unnoticed, thereby improving an
organization’s ability to act upon these opportunities. Organizations can
benefit from this type of data analysis, including enhancing their ability
to understand customer behaviour, their performance, their products,
their marketing campaigns, or even the efficiency of their production
lines. Data analytics techniques for data analysis are becoming
increasingly popular as companies strive to extract more insight from
their data and thus improve the performance of their businesses (Mariani
& Nambisan, 2021).
References
Hassan, M., Awan, F. M., Naz, A., deAndrés-Galiana, E. J., Alvarez, O.,
Cernea, A., … & Kloczkowski, A. (2022). Innovations in Genomics and
Big Data Analytics for Personalized Medicine and Health Care: A
Review. International Journal of Molecular Sciences, 23(9), 4645.
Mariani, M. M., & Nambisan, S. (2021). Innovation analytics and digital
innovation experimentation: the rise of research-driven online review
platforms. Technological Forecasting and Social Change, 172, 121009.
Discussion 2:
In today’s data-driven world, businesses need to leverage big data
analytics to stay competitive. Walmart Company is a retail giant that has
long recognized the importance of this technology and has invested
heavily in it over the years. Data analytics allows Walmart to analyze
vast amounts of customer information, inventory details, employee
performance metrics, and more. One significant benefit of utilizing
advanced data analysis techniques at Walmart is improved decisionmaking capabilities across all aspects of their business operations. With
access to real-time insights gleaned from analyzing sales trends or
predicting future demand for specific products; management can make
informed decisions about everything from marketing strategies down
through supply chain optimization (Cao, 2021).
Another major advantage resulting from collecting large volumes
namely variety as well as structured/unstructured datasets together with
using machine learning algorithms will be enhanced operational
efficiency by streamlining processes such as optimizing staffing levels
on particular days based upon predicted foot traffic patterns within
individual stores nationwide where they operate outposts worldwide.
Data analytics also provides benefits related specifically towards
customer satisfaction better understanding of consumer habits along
with offering customized experiences accordingly leads directly to
increased brand loyalty among existing clientele whilst simultaneously
attracting potential customers away from competitors who are not able
yet to implement cutting-edge technologies like Big Data Analytics
platforms available these days (Bi & Cochran, 2014).
Data analytics has become an integral part of Walmart’s business
strategy. As one of the largest retailers in the world, Walmart generates
vast amounts of data from its sales transactions, customer interactions,
and supply chain operations daily. However, several challenges arise
when using data analytics at such a large scale. One key challenge is
dealing with the sheer volume and complexity of data being generated
by various sources within Walmart’s ecosystem. This requires
employing sophisticated algorithms to analyze this diverse range of
structured and unstructured datasets effectively. Another significant
hurdle is ensuring accuracy while processing huge volumes quickly;
given that inaccuracies could result in wrong decisions affecting
multiple stakeholders throughout their value chain suppliers included.
Creating insights out of raw big can be a daunting task especially as it
needs human intervention for identifying patterns or anomalies which
again will depend upon analytical skills available inside the organization
including subject matter experts who understand nuances around the
domain intricately (Cao, 2021).
References
Bi, Z., & Cochran, D. (2014). Big data analytics with
applications. Journal of Management Analytics, 1(4), 249-265.
Cao, P. (2021, December). Big Data in Customer Acquisition and
Retention for eCommerce–Taking Walmart as an Example.
In 2021 3rd International Conference on Economic Management
and Cultural Industry (ICEMCI 2021) (pp. 259-262). Atlantis
Press.