Information Systems And Big Data Analysis Assignment Sample

Introduction Of Information Systems And Big Data Analysis

The report is a reflective report to evaluate the topics that are being evaluated in the domain of big data and its application in organisations. There are a number of values that are being integrated into the working of processes with the use of big data in the present times. The use of data in present times is huge and the values of big data are being integrated with cloud services which integrate for many values in the different organisations. Data-centric approaches to business decisions are most preferred as a connection with the present conditions of the market is always preferred.

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Discussion

Different types of data

The use of data is abundant in the present times where most of the business decisions are based on the insights of the data that are being collected from the target market. The data is classified into a number of types based on their nature and characteristics (Balas, et al. 2020). The main types of data include Numerical data which are data in their numerical natures. These data are further divided into discrete data and continuous data. The latter is numerical data which considers numbers that are real and can be assumed any number within a particular range, whereas the former is the type of numerical data that consists of whole numbers and these data are countable.

Figure 1: Types of Data

(Source: self-created in MS WORD)

The other forms of data are categorical data that denotes the data of categories, data that is collected over repeated intervals of time which is denoted as time series data, data which in a textual manner are the text data and similarly the data from images are image and audio data is audio data and image data respectively (Dartmann, et al. 2019). Apart from this, the data obtained from geographical locations are geospatial data and the data which are comprised of 0 and 1 only for the computers are binary data and the data which conveys information about other forms of data is metadata. All these different forms of data are available in a collective form of big data in the cloud-based platforms.

Big data

Big Data is the abundant nature of data that is used in different operations to make decisions and policies for business. The big data refers to the complex and massive sets of data that are analysed and processed using different tools and strategies. The data in the big data sets are of high volume, wide variety and high velocity (Brayne, 2020). Big data is generally divided into 3 types which are structured data, which is the structured and organised form of data that are present in the data sets. The unstructured data are the data which are or framed and organised. The unstructured data are being analysed using techniques after which the data can be processed to obtain insights (N. Chams et al. (2019)). The next type of big data is the semi-structured data which are in between the two types of data discussed above. The semi-structured data may be arranged partially but they are not strictly arranged according to any scheme.

Figure 2: Types of Big Data

(Source: self-created in MS WORD)

Current and future challenges in big data analysis

The use of Big Data is rising greatly with the evolving of times and with the rise in requirement for data-centric approaches in the organisation and industries. However, the use of big data in industries is being faced with a number of current and future challenges which are to be mitigated for better use of big data in the companies and industries (Dubey, et al. 2020). Some of these current and future challenges in data analysis include for:-

• The veracity and Data Quality: The accuracy and reliability of big data are very essential and challenging to be maintained for better use of data in organisations. Complex and huge data sets contain much noise and incomplete data which are even inconsistent (M.L. Davenport et al.(2019)). The presence of such data leads to anomalous data evaluation and wrong insights are obtained.
• Data Privacy and Security: The privacy of the data is very essential to be maintained because the loss of data can lead to many threats of loss of resources. Big data is mainly integrated with the use of cloud services and because of this, the data may lose its quality and there can be risks of loss of data or threats from malware and cyber threats (Bag, et al. 2020). The risk of data breaches and data phishing is very common with those of big data in organisations that should be prevented to maintain the quality and security of data.
• Miscellaneous Challenges: The challenges of scalability and infrastructure are persistent and promoting challenges big data uses large volumes of data and the bulk data needs tools and strategies specialized to handle these bulk data sets. The data in the big data sets are generally from a number of sources and putting all these different forms of data in a single set is complex and challenging (Belhadi, et al. 2019). A number of techniques of big data analysis include the real-time use of big data and this process is challenging. A number of ethical concerns and data governance policies are some of the challenges that are to be mitigated.

Techniques used in big data analysis

The data that are obtained in the big data sets are being analysed with the use of a number of techniques to generate essential insights from the data sets which can be useful for the business of the organisations. The use of cloud services and cloud tools can be used for essential insights and the use of Business Intelligence tools and strategies are some of the ways in which statistical models are useful in big data analytics (Diebold FX, et al. 2019). The use of machine learning-based algorithms is also useful for essential big data analytics and natural language processing which are also some of the techniques to analyse big data are also some of the techniques used in big data analytics. The use of visualisation strategies is useful to draw out the essential curves and trends of the data that are collected.

Summary obtained from the reflective model

The use of Gibbs model is used to summarize the different values obtained from the use of data in organisations. The data can be of different types and all data irrespective of their types are useful to obtain insights for the formation of decisions and business policies to enhance advantages to the business. These databases are being analysed and essential insights are obtained from those data. Different techniques are used to evaluate the different parameters of big data using data analytics and it is evaluated that the Gibbs model is helpful for me to get a clear and transparent understanding of the research.

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Conclusion

The report is a reflective report of assignment 1 and the different parameters of big data is being evaluated in this report. The different types of data and the different types of big data and the different advantages of using big data in the organisation are some of the essential matters of the report. The report has covered for the current the future challenges that are prevalent in big data analysis to obtain insights from the big datasets for the evaluation of decisions and policies of business. The different parameters of big data that have made big data the most essential resources of the organisations are evaluated in this report.

References

Books

• Balas, V.E., Solanki, V.K. and Kumar, R. eds., 2020.Internet of Things and Big Data Applications: Recent Advances and Challenges. Switzerland: Springer. Available from: https://link.springer.com/book/10.1007/978-3-030-39119-5 [Available on 25.06.2023]
• Dartmann, G., Song, H. and Schmeink, A. eds., 2019.Big data analytics for cyber-physical systems: machine learning for the internet of things. Elsevier. Available from: https://shop.elsevier.com/books/big-data-analytics-for-cyber-physical-systems/dartmann/978-0-12-816637-6 [Available on 25.06.2023]
• Brayne, S., 2020.Predict and Surveil: Data, discretion, and the future of policing. Oxford University Press, USA. Available from: https://global.oup.com/academic/product/predict-and-surveil-9780190684099 [Available on 25.06.2023]

Journals

• Chamset al. (2019) On the importance of sustainable human resource management for the adoption of sustainable development goals Resour. Conserv. Recycle.
• M.L.Davenportet al.(2019).Food-related routines, product characteristics, and household food waste in the United States: a refrigerator-based pilot study Resour. Conserv. Recycle
• Bag S, Wood LC, Xu L, et al.: Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resour Conserv Recycl. 2020; 153: 104559.
• Belhadi A, Zkik K, Cherrafi A, et al.: Understanding big data analytics for manufacturing processes: Insights from literature review and multiple case studies. Comput Ind Eng. 2019; 137: 106099.
• Diebold FX, Ghysels E, Mykland P, et al.: Big Data in Dynamic Predictive Econometric Modeling. Elsevier: Amsterdam, The Netherlands. J Econom. 2019; 212(1): 1–3.
• Dubey R, Gunasekaran A, Childe SJ, et al.: Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations. Int J Prod Econ. 2020; 226: 107599.