MG413 - Data Insights: Analysis Techniques & Big Data in Business
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This presentation delves into the significance of quantitative and qualitative research in contemporary marketing research and data analysis, emphasizing questionnaire and discussion guide design with practical examples. It elucidates correlation and regression, explaining their usage in analyzing variable relationships, and explores time series analysis for predicting future values based on observed data. A critical examination of the analysis techniques, particularly correlation, regression, and time series, is provided, focusing on issues surrounding their application to Big Data and their impact on modern business decision-making. The presentation references academic journals to support its arguments and insights.

Data Insight
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Table of Contents
What is the role of quantitative research in modern marketing research and data analysis........1
What is the role of qualitative research in modern marketing research and data analysis..........1
Set out what correlation and regression is and how it is used.....................................................1
Set out what time series is and how it is used.............................................................................1
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.....................................2
References:.......................................................................................................................................3
What is the role of quantitative research in modern marketing research and data analysis........1
What is the role of qualitative research in modern marketing research and data analysis..........1
Set out what correlation and regression is and how it is used.....................................................1
Set out what time series is and how it is used.............................................................................1
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.....................................2
References:.......................................................................................................................................3

What is the role of quantitative research in modern marketing research and data analysis
Quantitative research data are those information which consist of having numerical data or which
can be analysed by using charts or graphs (Hussain and Ahmed, 2018) . It is very essential to use
this type of research in marketing analysis as it will provide appropriate outcome about the
collected information. When qualitative data is used in reports then the information becomes
much easier to understand by reader.
What is the role of qualitative research in modern marketing research and data analysis
Qualitative research are those research which are done to analyse the behaviour of a individual or
a group of individuals. This type of research does not include numerical data. The outcome of
this research has no appropriate proof because it is the prediction of researcher. This type of
research plays an important role for those researches which are based on literature or any other
subject or topic which is not related to numerical data.
Set out what correlation and regression is and how it is used
Correlation is a type of research where researcher analyse the relationship between two or more
variables used within a study. It help to analyse how these variables impact each other and which
variable has more power to influence another variable (Akoum and et. al., 2019).
Regression is a technique which help to analyse dependant and independent variable of study and
further help to analyse relationship between a dependant variable upon one or more independent
variable.
Set out what time series is and how it is used
Time series is basically a concept to assign the sequence of time to the data points that
has been occurred in the successive order over several period of time. This thing that cab be
contrasted or opposed with the point in time. For setting out the time of series there is no
minimum or maximum amount of the time that should be involved or used, thus, giving
permission of the data to be gathered. The time series is basically the data set that has ability to
track the sample over the time. Time series is used to predict the future values on the basis of the
observed values (Huebner and Moore, 2019).
1
Quantitative research data are those information which consist of having numerical data or which
can be analysed by using charts or graphs (Hussain and Ahmed, 2018) . It is very essential to use
this type of research in marketing analysis as it will provide appropriate outcome about the
collected information. When qualitative data is used in reports then the information becomes
much easier to understand by reader.
What is the role of qualitative research in modern marketing research and data analysis
Qualitative research are those research which are done to analyse the behaviour of a individual or
a group of individuals. This type of research does not include numerical data. The outcome of
this research has no appropriate proof because it is the prediction of researcher. This type of
research plays an important role for those researches which are based on literature or any other
subject or topic which is not related to numerical data.
Set out what correlation and regression is and how it is used
Correlation is a type of research where researcher analyse the relationship between two or more
variables used within a study. It help to analyse how these variables impact each other and which
variable has more power to influence another variable (Akoum and et. al., 2019).
Regression is a technique which help to analyse dependant and independent variable of study and
further help to analyse relationship between a dependant variable upon one or more independent
variable.
Set out what time series is and how it is used
Time series is basically a concept to assign the sequence of time to the data points that
has been occurred in the successive order over several period of time. This thing that cab be
contrasted or opposed with the point in time. For setting out the time of series there is no
minimum or maximum amount of the time that should be involved or used, thus, giving
permission of the data to be gathered. The time series is basically the data set that has ability to
track the sample over the time. Time series is used to predict the future values on the basis of the
observed values (Huebner and Moore, 2019).
1
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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
As per the critical analysis of the time series model it is analysed that it not a systematic
approach for the analysis of the Big Data in the modern business world where everything is
operated through Big data technology. As through there can be error and difficulty in verifying
or setting out the time scale for the large scale information like Big Data. The correlation and
regression are the effective techniques of analysis but in the modern world decision making on
the basis of large size big data the technique are its on limitation and critiques (Triguero and et.
al., 2019). In this the independent variable of the big data does not interact and provide the
incorrect decision making.
2
reference to Big Data and its use in modern business decision making
As per the critical analysis of the time series model it is analysed that it not a systematic
approach for the analysis of the Big Data in the modern business world where everything is
operated through Big data technology. As through there can be error and difficulty in verifying
or setting out the time scale for the large scale information like Big Data. The correlation and
regression are the effective techniques of analysis but in the modern world decision making on
the basis of large size big data the technique are its on limitation and critiques (Triguero and et.
al., 2019). In this the independent variable of the big data does not interact and provide the
incorrect decision making.
2
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References:
Books and Journals
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.
Akoum, M and et. al., 2019, November. Big Data Insight towards Well planning, A case study.
In Abu Dhabi International Petroleum Exhibition & Conference. OnePetro.
Huebner, R. and Moore, P., 2019, March. Data Insight: Helping Districts Use the Right Data for
Improving Student Outcomes. In Society for Information Technology & Teacher
Education International Conference (pp. 1033-1033). Association for the Advancement
of Computing in Education (AACE).
Triguero, I and et. al., 2019. Transforming big data into smart data: An insight on the use of the
k‐nearest neighbors algorithm to obtain quality data. Wiley Interdisciplinary Reviews:
Data Mining and Knowledge Discovery, 9(2), p.e1289.
3
Books and Journals
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.
Akoum, M and et. al., 2019, November. Big Data Insight towards Well planning, A case study.
In Abu Dhabi International Petroleum Exhibition & Conference. OnePetro.
Huebner, R. and Moore, P., 2019, March. Data Insight: Helping Districts Use the Right Data for
Improving Student Outcomes. In Society for Information Technology & Teacher
Education International Conference (pp. 1033-1033). Association for the Advancement
of Computing in Education (AACE).
Triguero, I and et. al., 2019. Transforming big data into smart data: An insight on the use of the
k‐nearest neighbors algorithm to obtain quality data. Wiley Interdisciplinary Reviews:
Data Mining and Knowledge Discovery, 9(2), p.e1289.
3
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