Big Data: Challenges, Characteristics, and Business Support
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Added on 2023/06/10
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This presentation covers the basics of Big Data, including its definition, challenges, and characteristics. It also explores how Big Data can support businesses and provides examples from various industries. Techniques like A/B testing, data mining, machine learning, and statistics are discussed in detail.
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History on big Data Big data is a form of technology which is used in storing, analyzing and managing the massive data. The case study describe about the combination of a structured and unstructured or semi-structured part of detail collected by the firm which is mainly used in many kind of projections it includes acquisition of machine operations, forecast modeling etc. They also assists the organization to manage a big amount of information Information Systems and Big Data Analysis Name of the Student What is big Data The company big data involves big detail, bunch of tough data, significantly gathered from innovative or new origins. These kind of information have a combination of some variety of data; It also have a high volume and velocity. This case study includes four V's that shows velocity, volume, value and variety. It gives power to the firms to create profit based decisions. Characteristics of Big data There are some illustrative examples of companies that utilize big information technologies and it involves agriculture, pharmaceuticals, etc. The important features of big information are explained under: •Velocity: The name Big data is itself related to a size which means huge or enormous. Size of data plays a very crucial role in determining the value of data. •Value: Variety refers to the heterogeneous sources and the quality of data, both structured or unstructured are included.. •Volume: The term refers to the generation of speed of the data how fast a data can be generated or processed to meet the demands and determines the potential of the data. •Variety: This refers to the inconsistency which can be shown by the data in some cases, therefore hampering the process of being able to handle and management of data efficiently.. The challenges of big data analytics In the today's era where everything is digitalized from shopping to schooling from education to work, post pandemic everything is highly digitalized. Big data analytics is the process of using this data available in different forms structured, unstructured various sizes in order to analyze and apply in the organizational uses. Big data have following characteristics: •High volume •High velocity •Artificial intelligence •Mobile •Social •Internet of things Alavi, A. and Buttlar, W.G. eds., 2018. Data Analytics for Smart Cities. CRC Press. Cooklev, T and et.al., 2018. Enabling RF data analytics Ghani, N.A and et.al., 2019. Social media big data analytics: A survey. Computers in Human Behavior, 101, pp.417-428. Mayer-Schönberger, V. and Ramge, T., 2018. Reinventing capitalism in the age of big data. Hachette UK. Li, J and et.al., 2018. Big data in tourism research: A literature review. Tourism Management, 68, pp.301-323. Choi, T.M., Wallace, S.W. and Wang, Y., 2018. Big data How Big Data technology could support business & Examples Big data is an assemblage of data that is enormous in quantity, still has a potential or power to grow rapidly with time. It is so vast in size and entangled that none of the conventional data management techniques and tools can store it or operate it with efficiency. Processing Big data carries multiple advantages with it such as: •Clear and improved consumer or customer service •Improved and healthier functional efficiency •Primal determination of risk to the product or service •Organizations can utilize external intelligence service while taking decision. Techniques that are currently available to analysis big data •A/B Testing – This technique helps in comparing the control group and the test group. In order to determine what changes brings improvement. Bid data fits in the model and helps to run test in big data. •Data Mining – It is most common tool to resource only the useful data from the huge data and study the data which is required, saves time and resources and provides results fast. •Machine Learning – It belongs to the field of artificial intelligence; it works on computer algorithm to produce solutions to the problem based on the data available. •Statistics – The technique used to collect, organize, interpreted and experiment.