Analyzing Data Insight: Quantitative and Qualitative Research Roles

This report provides a detailed analysis of data insight, emphasizing the roles of quantitative and qualitative research in modern marketing and data analysis. It explores concepts like correlation, regression, and time series, highlighting their uses in business decision-making. The report critiques issues surrounding analysis techniques and discusses how data is collected and used to make informed business decisions. It further examines the use of big data in contemporary business contexts, emphasizing its importance in understanding market trends and customer behavior. The conclusion underscores the significance of data collection in developing effective business strategies and planning. This comprehensive overview is designed to aid students in understanding the practical applications of data analysis in a business environment. Desklib offers a range of similar assignments and resources for further study.

Data insight

Table of content
 Introduction
 Role of quantitative research in modern marketing research and data analysis
 Role of qualitative research in modern marketing research and data analysis
 Concept correlation and regression and its uses in business
 Concept of time series and its use
 Provide a critique of issues surrounding the analysis technique
 How data is collected and used to make business decisions
 CONCLUSION
 References

Introduction
Data insight is defined as the deep understanding of an individual
or organisation for analyse information on a particular issues.
Depth understanding helps business to take efficient decisions and
leading business in right direction. There are various research
methods such as qualitative and quantitative marketing research
are used with the objective of gaining market and customer
information.

Role of quantitative research in modern marketing
research and data analysis
Quantitative research: Quantitative research is modern marketing is
referred as the procedure which consist collecting as well as analysing
numerical data and information. This research method is used by business
to analyse pattern, generalise result as well as make future prediction.
Some of quantitative research as well as data analysis in marketing
research are explained below:
 Calculate and analyse data
 Develop numerically rational theories

Role of qualitative research in modern marketing
research and data analysis
Qualitative market research: Quantitative market research helps
business to assess customers beliefs and factors that influences the
behaviour of customers through asking open-ended questions.
Organisation conducts focus group, online forum, depth interview,
observation and research to gain reliable market information.

Concept correlation and regression and its uses in
business
Correlation: Correlation is the statistically term which is used to explain the degree to which
two different variables are interrelated with each other. This concept helps business to analyse
various variables and their importance for taking efficient business decisions. Some uses of
correlation are discussed below:
 It reduces range of uncertainties in business practices and take decisions on the bases of
correlation that are closer of reality.

Continuous
Regression: Regression is the set of different statistical steps that are used by business to
analyse relationship between a dependent variable as well as one or more independent
variables. It facilitates organisation to recognise different aspects which are related to
business and helps and use them in developing effective business decisions and policies. Use
of regression are explained below:
 It enables business to maintain relationship between consumer income and market
demand of specific products to make efficient decisions.

Concept of time series and its use
The concept of time series is referred as observation of data in sequential
manner in discrete-time in different succession intervals. It is also
known as running chart.
 It facilitates organisation to understand variations like random
variations, seasonal variations, trend variations as well as cyclical
variations that enables manager to take efficient decisions.

Provide a critique of issues surrounding the analysis
technique
Business uses different analysis techniques like regression
analysis, pest analysis, decision-making, system analysis,
data mining, histogram and others which enables business
to analyse different business situations as well as create
informed and system decisions that lead business towards
growth and development.

Big data and its use in modern business decision-making
Big data is referred as the set of various data which is large in size and continuously growing
with time. It is available in structured, semi-structured as well as unstructured format. Use of
big data in organisational decision-making are mentioned below:
 Big data facilitates organisation to take informed business decision through accessing
patterns as well as problems.

How data is collected and used to make business
decisions
Data collection is referred as the procedure that helps business to
collect as well as measure information regarding market trends and
customer base. Business uses various methods that are implemented
by business to gather data and take efficient decisions on the bases of
collected information.
 Interview
 Survey
 Documents and records
 Observation

Conclusion
As per above presentation, it can be concluded that business collects various data regarding
customers and market in the form of qualitative and quantitative data that help business
develop effective planning and strategies. It is important for business to take efficient
decisions and divide all business activities in time constraints that help it to complete
determined task on specific time period.

References
 Lu, J., 2022. Data science in the business environment: Insight management for an
Executive MBA. The International Journal of Management Education, 20(1), p.100588.
 Hota, L. and Dash, P.K., 2022. An Insight into Big Data and Its Pertinence. In Handbook of
Research for Big Data (pp. 137-156). Apple Academic Press.
 Kim, H., Shin, H.H. and So, K.K.F., 2022. Actor value formation in Airbnb: insight from
multi-source data. International Journal of Contemporary Hospitality Management, (ahead-
of-print).
 Pearce, J. and Simpson, R., 2022. The role of police in conducting wellness checks: Insight
from a study of police data. Police Practice and Research, 23(4), pp.400-413.

Thank-you