Data Insights: Quantitative and Qualitative Research Analysis Report

This report delves into the crucial role of data insights in modern marketing research. It examines both quantitative and qualitative research methodologies, including questionnaire design, interview guides, and their practical applications. The report explores correlation and regression analysis, as well as time series analysis, explaining their uses and providing examples. It critically assesses the limitations of these analysis techniques, particularly in the context of Big Data and its impact on business decision-making. The report also covers data collection methods, including primary and secondary sources, and how this data can be used to inform strategic business decisions. The conclusion emphasizes the importance of data insights for strategic decision-making and highlights the key findings of the study, including the roles of quantitative and qualitative research, the use of correlation, regression, and time series analysis, and data collection methods. Finally, the report includes a list of references.

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Introduction
•Data insight refers to as the understanding of organisations information
and different data in order to analyse it relating to a particular issue or even.
•There are many business decisions with the company taken on the basis of
the Analysis of data.
•Thus the data insight provides the different technologies and techniques
with help of which data can be analysed.

The role of quantitative research in modern
marketing research and data analysis
• The quantitative research is being designed as the research which
undertakes the use of numeric facts and figures in order to analyse
business problem.
• There are many different types of problem which the business faces.
Hence the use of different quantitative Tools and techniques assist the
company in evaluating the research problem easily and to take the
necessary steps in order to solve the problem.

The role of quantitative research: Questionnaire design with
examples of good practice
Q1. From how much time you are using the product of out company?
2 month
5-6 months
10- 15 months
More than 15 months
Q2. Up to what extent do you like the products being offered by the company?
Very good
Good
Average
Poor
Very poor
Q3. Will you recommend our product to other people as well?
Yes
No

The role of qualitative research in modern marketing research
and data analysis
• Along with the quantitative research the qualitative research also plays
crucial role within the modern marketing research.
• The reason underlying the fact is that qualitative research is being
defined as the study which includes only the non numerical
information relating to the research questions.
• The qualitative research involves analysing the non numeric
information relating to the business issue and then drawing inferences
in order to solve the problem.

The role of qualitative research. The Interview Discussion guide:
design & use (with examples)
•For example, company is not getting that how they can improve the
marketing strategies.
•Thus in that case the company can evaluate the different secondary
sources like already existing articles books relating to the types of
marketing strategies for improving the number of consumers.

Correlation & Regression:
How & why it is used - with examples
Regression

Time series:
What it is & how it is used - with examples
•Time series analysis is being defined as the method
which helps in identifying the fluctuations within
the different variables over the period of time.
•This time series data analysis helps in identifying
the pattern and recognising the trend which can
assist in predicting the future values is well.
•The uses of time series are being implemented
within the statistics and also an identifying the
pattern over the past for predicting the future.

Critique of issues surrounding the analysis techniques
Correlation & Regression (focusing on Big Data & its use in business decision
making)
Correlation drawbacks:
Result obtained can be affected from the extent of quality work executed
time consuming procedure
difficulties in calculation
limited to offer insights about relation but unable to specify the reason
Inability to identify the major reasons
Regression limitations
Eliminating essential predictor variables
omission & missing of data
Inappropriateness in recognizing errors & mistakes
Non constant variance & weighted least square
Incapable of finding that the data regarding missing information
Involvement of assumption related between dependent & independent components regarding linearity.

Critique of issues surrounding the analysis techniques
Time series focusing on Big Data & its use in business decision making (focusing
on Big Data & its use in business decision making)
Correlation drawbacks:
Result obtained can be affected from the extent of quality work executed
time consuming procedure
difficulties in calculation
limited to offer insights about relation but unable to specify the reason
Inability to identify the major reasons
Regression limitations
Eliminating essential predictor variables
omission & missing of data
Inappropriateness in recognizing errors & mistakes
Non constant variance & weighted least square
Incapable of finding that the data regarding missing information
Involvement of assumption related between dependent & independent components regarding linearity.
Demerits of time series
Issuing regarding obtaining reliable information related to measures
Ineffectiveness & transparency related to presentation of data
Expensive procedure
Closed observation which leads to have problem in non involvement of foreseeable factors

How data can be collected & used to make informed business
decisions.
Primary method
survey
Interview
Observation
Focus group
Secondary method
articles
Books
Journal

Conclusion
• From the above study it can be concluded that data insights is crucial for making the
strategic decision.
• The current study has given emphasis on involving the role of quantitative & qualitative
research in modern marketing research and data analysis such as identification of lacking
areas, etc.
• In addition to this, it has comprised correlation & regression usage which has given depth
understanding that taken into process for recognizing relation, its causes, etc respectively.
• The study has given emphasis on time series role which helps in recognizing pattern, etc.
so that effective decision can be taken.
• There are few limitations of this technique which are highlighted in the particular study.
• Primary & secondary method for data collection can be taken into process for decision
making.

List of References
• Nielsen, A., 2019. Practical time series analysis: Prediction with statistics and machine learning.
O'Reilly Media.
• Lu, M., and et.al.,. A Systematic Review of Questionnaire-Based Quantitative Research on MOOCs.
The International Review of Research in Open and Distributed Learning. 22(2). pp.285-313.
• Aparicio, G., 2017. Data-Insight-Driven Project Delivery: Approach to Accelerated Project Delivery
Using Data Analytics, Data Mining and Data Visualization.
• Geldof, T., Huys, I. and Van Dyck, W., 2019. Real-world evidence gathering in oncology: the need for a
biomedical big data insight-providing federated network. Frontiers in medicine, 6, p.43.
• Hussain, M. and Ahmed, N., 2018. Reservoir geomechanics parameters estimation using well logs
and seismic reflection data: insight from Sinjhoro Field, Lower Indus Basin, Pakistan. Arabian Journal
for Science and Engineering, 43(7), pp.3699-3715.
• Kühne, B. and Böhmann, T., 2020, January. Formative Evaluation of Data-Driven Business Models–
The Data Insight Generator. In Proceedings of the 53rd Hawaii International Conference on System
Sciences.