Analytics for Business Decision Making

This assignment delves into the realm of business analytics, examining its application in driving informed decision-making. It covers various analytical techniques like descriptive, predictive, and prescriptive analytics, illustrating their use cases with examples from different industries. The focus also extends to big data analytics and its role in fostering innovation within businesses, particularly for small and medium-sized enterprises (SMEs).

MANAGING BY ANALYTICS

TABLE OF CONTENTS
(A)Define nature of mathematical corporation and way in which these one capitalise business
opporutnities that are discovered in ocean of data...........................................................................1
(B) Compare and contrast descriptive, predictive and prescriptive analytics in respect to three
firms.................................................................................................................................................1
© Limitations and pitfalls of analytics methods..............................................................................2
(D) Use of big data analytics for innovation by firms specially small and medium size enterprise
.........................................................................................................................................................3
REFERENCES................................................................................................................................4

(A)Define nature of mathematical corporation and way in which these one
capitalise business opporutnities that are discovered in ocean of data
Mathematical corporation refers to the organisations that are developing analytics and
matematics related software. Some of them are Matlab, Tableau, R and SAS comes in this
category. Major characterisitcs of these firms are that theyrun capital projects in respect to
development of analytic software (Siemens and Long, 2011). These organisations are
capitalising business opportunities that are hidden on ocean of data by developing new features
in analytic software which make them more user friendly for the customers. With passage of
time, due to updation of software their cmputation power get increased and they become able to
handle more complex data. Such kind of things increase demand of software that are developed
by mathematical corporations and lead to their high pricing in the market. Hence, it can be said
that there are number of opportunities that can be capitalised by the mathematical corporation.
(B) Compare and contrast descriptive, predictive and prescriptive analytics in
respect to three firms
Descriptive, predictive and prescriptive analytics are different from each other.
Descirptive analytics is used to identify what sort of things happened in past time period. On
other hand, predictive analytics is used to find out that what may be happened in the upcoming
time period. Prescriptive analytics is used to identify possible outcomes that can be observed in
the upcoming time period. Thus, it can be said that there is difference between all these analytics
tools.
Netflix Uber Glaxosmithkline
Business
performance
Netflix make use of
predictive analytics
and by analysing
chunk data identify
that which sort of
programs customers
would prefer to
watch. Thus, only
selected items that
Uber uses
prescriptive analytics
and it helps it in
identifying driver that
is most nearby to
prospective customer.
Thus, instant service
is provided by the
firm to its customers
GSK uses descriptive
statistics to monitor
its R&D expenses
and maintaining
control on
expenditure.
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people prefer are
presented in from of
them which help firm
in increasing number
of visitors on its
website (Zikopoulos
and Eaton, 2011).
which lead to
improved business
performance.
Quantified benefits Major benefit that
firm received is that it
is focusing only on
specific serials or
shows and more
specifically targeting
customers in better
manner.
Uber is making the
best use of its
workforce and in
short duration,
serving customers in
better manner.
Firm is easily
identifying that which
sort of expenses are
increasing at rapid
rate out of all R&D
expenses. By doing
so, cost is controlled
on time.
Profitability Profitability of
Netflix increasd at
rapid rate.
Uber in few years
become known
company across the
globe in terms of
profit.
GSK controls cost
and increases profit
in the business.
( C) Limitations and pitfalls of analytics methods
There are number of limitations of the analytic methods for the business firms. Some
limitations in respect to variety of analytic are given below. Descriptive analytics: Major limitation of descriptive analytics is that in case of standard
deviation, there is no range within which if value come then higher or lower deviation
can be assumed in variable (Chen, Chiang and Storey, 2012). Hence, every analyst make
assumption whether STDEV is high or low. Such kind of wrong assumption can prove
costly to the firm and will lead to making wrong decisions. Predictive analytics: In case of predictive analytics, major limitation is that there are lots
of things that need to be taken into account while picking specific algorithm for making
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decisions. For example, if Data scientist intend to use regression method to explore
relationship between dependent and independent variable, one need to satisfy number of
conditions like heterodasticity, linearity and similarity in variance of error etc. If these
assumptions are not fulfilled and data scientist does not have sufficient knowledge about
these assumptions then in that one can make wrong decision and firm may face loss in its
business. Prescriptive analytics: Some of the techniques that are used in prescriptive analytics are
linear programing and integer programing etc. In case of linear programing method, one
need to determine constraints on basis of assumptions(LaValle and et.al., 2011). Thus,
major limitation of prescriptive analytics is that wrong assumptions may results in
obtaining wrong output which ultimately lead to making wrong decisions.
(D) Use of big data analytics for innovation by firms specially small and
medium size enterprise
Big data analytics is used to analyse unstructured data that can not be evaluated by using
BI and other analytics tools. Text, large call details and unstructured transaction data are one of
the best example of the big data. Big data analytics can be used to get input for innovation in
business operations. This is because in text many times lots of facts are hidden which big data
analytics help firm to identify. By working on received input, firms that comes in small and
medium size category can innovate their business operations under which they can make
effective use of raw material for prodution of products. Banks can also use text analytics and can
review feedack given by customers online. By doing so, areas where service need innovation can
be identified and performance can be improved to great extent (Lebied, 2017). Third example of
innovation is that in Paris four hospitals that are part of Assistance Publique-Hôpitaux de
Paris use analytics and identify pattern in patient admission record of last 10 years. Varied
algorithms were used to meausre computation accuracy and by using same now prediction is
made about admission of patients in hospital specific department. Accordingly, resources are
allocated in the business. Hence, big data analytics assist in innovating operations in terms of
identify way in which resources must be distirbuted among varied departments of hospitals.
Thus, it can be said that analytics ensue innovation at the workplace and assist firms in
innovating their business at fast pace. In current time period, there are many small and medium
size firms that are using analytics to make decisions and by using varied techniques like cluster
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analysis, market basket analysis etc. These methods helps in identifying lots of facts that assist
firms in finding out areas where innovation can be done in the business operations.
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REFERENCES
Books and Journals:
Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business intelligence and analytics: from big
data to big impact. MIS quarterly. pp.1165-1188.
LaValle, S. and et.al., 2011. Big data, analytics and the path from insights to value. MIT sloan
management review. 52(2), p.21.
Siemens, G. and Long, P., 2011. Penetrating the fog: Analytics in learning and
education. EDUCAUSE review. 46(5), p.30.
Zikopoulos, P. and Eaton, C., 2011. Understanding big data: Analytics for enterprise class
hadoop and streaming data. McGraw-Hill Osborne Media.
Online:
Lebied, M., 2017. [Online]. 9 Examples of Big Data Analytics in Healthcare That Can Save
People. Available through:< https://www.datapine.com/blog/big-data-examples-in-
healthcare/>.
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