Data Insights for Modern Business Decision Making - Desklib

This presentation covers the role of quantitative and qualitative research in modern business decision making, time series analysis, and issues surrounding analysis techniques, with specific reference to Big Data. It also discusses how companies gather data from various sources and areas to improve client engagements. The presentation is relevant for students studying MG413 Data Insights.

DATA INSIGHT

INTRODUCTION
Marketing analytics are critical because they
enable a business to stay above of each of its
competing companies by precisely researching
and assessing the conditions and expectations
of the businesses and the business in the
foreseeable future, whereas by meeting these,
the business could remain competitive for a
prolonged length of duration (Al-Qirim, Tarhini
and Rouibah, 2017).

the role of quantitative research
A strategy of addressing systematic questions
to a prospective client via evaluations,
discussions, or questionnaires is known as
quantified competitive analytics. This type of
financial inquiry often employs a wide range of
findings. The purpose of quantitative inquiry in
methodological approach is to generate data
and gain a better understanding of the
macroeconomic situation (Antignac,
Scandariato and Schneider, 2016).

Role of qualitative research
Non-statistical data is gathered and evaluated in a
qualitative surveying study in an attempt to better
understand concepts, attitudes, or circumstances
(Bataev, 2019). It can be used to improve one's
comprehension of an issue or to develop new ideas for
subsequent research.
The cognitive reaction, physical attitude, or voice
modulation of a client or prospective is examined every
time they see an advertisement or product packaging.
Qualitative analysis might be utilised in study methods
to better comprehend people's thoughts and feelings.
Community discussions are a fantastic approach to get
qualitative statistics, especially while participants want
to have more content which is specific to the community

Set out what time series is and how it is used, with
examples taken from coursework
A time series is a collection of data items
that appear in a specific order across time.
It generates forecasts for the future by
analysing trends and changes, and also
regularity and recurrent variations

Provide a critique of issues surrounding the analysis techniques
in 3 and 4, with specific reference to Big Data and its use in
modern business decision-making
There are a variety of distinct assessment
instruments and methods which a
corporation could utilise to differentiate
itself from its competitors, but it must pick
the one that is most advantageous to the
industry because choosing all of them will
be extremely costly, and it is also extremely
critical to pick an applicable one because it
will assist the corporation accomplish and
reach its objectives in the protracted term
(Efendioğlu and Bulkan, 2017).

Course material
Companies gather data from a variety of
sources and in a variety of areas. Some
techniques are quite sophisticated, while
others are relatively associated, but they all
need mathematical systems. Appropriate
consumer inquiries, clients who may be
monitored, and connections to other user-
related services are all possibilities for
obtaining data. This information might be
utilised to improve client engagements.

CONCLUSION
It could be inferred from the foregoing that
there are a variety of elements that affect the
functioning of a business, both internal and
external, and therefore a business must
examine each and every region of the
business in addition to be effective in a
business that is both changing and competing.
There are several components to this, all of
those are mentioned in detail previously, and
a firm could develop and thrive in the industry
wherein it operates by utilising every one of
those criteria.

References
Roth, S., Schwede, P., Valentinov, V., Žažar, K. and Kaivo-
oja, J., 2019. Big data insights into social macro trends
(1800–2000): A replication study. Technological
Forecasting and Social Change, 149, p.119759.
Scharf, F. and Nestler, S., 2018. Principles behind variance
misallocation in temporal exploratory factor analysis for
ERP data: Insights from an inter-factor covariance
decomposition. International Journal of
Psychophysiology, 128, pp.119-136.