Effective Data Collection & Usage for Strategic Business Decisions

This report provides a comprehensive analysis of data insights and their significance in contemporary business decision-making. It discusses the roles of quantitative and qualitative research in modern marketing, highlighting their importance in gathering measurable and non-mathematical data, respectively. The report also elucidates correlation and regression analyses, explaining how they are used to assess relationships between variables, alongside time series analysis for tracking data trends over time. Furthermore, it assesses the issues affecting these analysis procedures, particularly concerning Big Data and its impact on current business decisions. The report concludes by suggesting methods for more effective data collection and usage to enhance strategic business choices, emphasizing the value of data insights in improving organizational knowledge and reducing risks.

Data insights For
Business Decisions

CONTENT
 INTRODUCTION
 Discuss the significance of quantitative research in contemporary marketing
 Explain the importance of qualitative research in the context of discussion guide design.
 Using examples, explain what correlation and regression are and how they are used.
 With an example, explain about time series and how it is useful.
 Give an assessment of the issues affecting the analysis procedures in 3 and 4, with specific reference to Big
Data and its use in current business decision – making.
 How data could be more effectively collected and used to make business decisions
 CONCLUSION
 REFERENCES

INTRODUCTION
Data insight is defined as knowledge attained by a
company from evaluating information on a
specific topic or condition. It is necessary to assess
information that provides insights that will assist
the organisation in increasing its knowledge of
decisions and lowering the risk associated with the
trial-and-error testing methodology.

Discuss the significance of
quantitative research in
contemporary marketing
Quantitative research is regarded as an efficient method of acquiring measurable
information and data, and it also focuses on executing factual, numerical, and
computational procedures. It is primarily concerned with gathering information from
existing users through various types of sampling methods such as questionnaires and
online surveys in numerical form. Hence, it includes both primary and secondary research:
 The questionnaire design mainly focuses on the format and structuring of the
questions.
 It helps in collecting the data on the information that is useful for the research.
 It is a quick process which reduces the measurement error and in not expensive also.

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The responses which are given by users help in taking decisions in regards to change in labour and products that
assistance in guaranteeing fulfilment level of client.
For example,
Have the monthly expenses of house increased by how much percentage?
a) 10 %
b) 10 – 20 %
c) 20 – 30 %
d) 30 % and above

Explain the importance of qualitative research
in the context of discussion guide design.
 The qualitative research method is used to collect and evaluate non-mathematical
information and data for factual assessment. It is taken on to build their agreement
on how each individual experiences the world. There are various types of approach
of subjective exploration which will be utilized for deciphering information.
Subsequently, there are various kinds of strategies like perception, meet, studies
which is utilized for gathering subjective information to lead research properly

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It is useful in expanding understanding with respect to conviction and
perspective of unique individual and furthermore they give evaluation in
regards to circumstance and central consideration which impact its conduct.
Customers' attitudes toward products, for example, are as follows:
a) Good
b) Moderate
c) Bad

Using examples, explain what
correlation and regression are and how
they are used.
Correlation analysis is used to assess the relationship between two persistent
factors, such as an independent and dependent variable or two autonomous
factors (Kaczan and Orgill-Meyer, 2020).
Regression analysis refers to determining the relationship between the
outcome variable and at least one factor. The outcome is referred to as the
reaction or dependent variable, while the risk components are referred to as
independent variables.

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Correlation is a measurable technique that is taken into consideration to examine
conceivable straight relationship among two ordinary variants. Not really settled as an
easy to compute just as to incorporate. It is used to concisely summarise the strength of
relationship between a set of two or more mathematical variables. A model was used to
build the information in order to improve understanding of the relationship.
A B C
0 2 2
14 6 11
1 8 3
10 5 13
5 6 4
A B C
A 1 - -
B
0.1915
16 1 -
C
0.9092
68
0.1088
93 1

With an example, explain about
time series and how it is useful
A time series is a collection of perceptions of well-defined information items
obtained through repeated estimations over time. A period series, for example, would
be used to estimate the value of retail deals every month of the year. This is because
business income is obvious and consistently estimated at similarly separated
stretches. Information gathered rarely or only once is not a time series.

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Uses of Time Series:
 It is used to formulate the decision related to policy and future activities.
 To compare the changes in the valued of the different aspects at time times.
For instance, the sales revenue of the organisation of 8 years is as follows with the time period:
Year Quarter Period Sales
2013 1 1 $ 986.2
2014 2 2 $ 595.8
2015 3 3 $ 865.9
2016 4 4 $ 1296.89
2017 1 5 $ 989.54
2018 2 6 $ 1482.00
2019 3 7 $ 992.8
2020 4 8 $ 695.4

Give an assessment of the issues affecting the analysis
procedures in 3 and 4, with specific reference to Big
Data and its use in current business decision – making.
According to the above analysis of correlation, time series, and regression, large
information strategies are useful in making critical decisions. Big Data is a catch-
all term for any variety of information assortments that are so vast and modern that
they are difficult to deal with utilising standard data examination programmes as
well as data frameworks strategies. On massive social data bases, it is customarily
used inductive insights as standards to discover corporations, associations, and to
forecast events and events.