Data Analysis Techniques and Methods for Data Management

This report presents a comprehensive overview of various data analysis techniques essential for effective data management. It begins by emphasizing the significance of data analysis, particularly in addressing challenges like slow responses in surveys, and then delves into specific methods. The report details qualitative analysis, which focuses on understanding 'why' and 'how' questions; quantitative analysis, which involves numerical data; text analysis, which extracts information from text; statistical analysis, which uses graphs for data representation; predictive analysis, which utilizes machine learning; and prescriptive analysis, which defines actions based on predictive outcomes. References from academic sources support the discussed concepts, providing a strong foundation for the understanding of data analysis techniques.

Data management

Table of content
 Ways to analyse the data
 References

Ways to analyse the data
The data analysis is considered as key approach which is
beneficial in increasing data management and
synchronisation approach (Ghavami, P., 2019). After the
slow response from Gravitas customers for the survey, data
analysis is important for the whole group.
 Qualitative analysis- The qualitative analysis is
considered as key approach in data analysis which helps
in increasing knowledge and awareness. The qualitative
analysis includes questions like why, what or how.

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 Quantitative analysis- The quantitative data analysis
includes analysis of data which is measured in terms of
numbers.
 Text analysis- In this approach texts are analysed to extract
machine readable facts. The main aim of this technique is
to develop data with structure and unstructured content

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 Statistical analysis- In this technique, data is
collected with key focus on primary objectives.
Then in the next step data is depicted on graphs in
order to develop major and precise understanding.
 Predictive analysis- The predictive analysis
often use historical data and feed it into machine
learning. This helps to find major patterns and
trends related to data in a short span of time.

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Prescriptive analysis- This data analysis technique is
used by many organisations and individuals to develop
crucial analysis. The prescriptive analysis focuses on
different course of action and defines all possible
implications that should be reached after predictive
analysis.

References
 Ghavami, P., 2019. Big data analytics methods: analytics techniques in data mining, deep learning
and natural language processing. Walter de Gruyter GmbH & Co KG.
 Maione, C., Barbosa Jr, F. and Barbosa, R.M., 2019. Predicting the botanical and geographical origin
of honey with multivariate data analysis and machine learning techniques: A review. Computers and
Electronics in Agriculture, 157, pp.436-446.
 Miles, M.B., Huberman, A.M. and Saldaña, J., 2018. Qualitative data analysis: A methods
sourcebook. Sage publications.
 Wesoły, M. and Ciosek, P., 2018. Comparison of various data analysis techniques applied for the
classification of pharmaceutical samples by electronic tongue. Sensors and Actuators B:
Chemical, 267, pp.570-580.