This presentation covers the basics of big data, its characteristics, and techniques for analysis. It also discusses how big data can support businesses and includes case studies and challenges of big data analytics.
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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: It express the value of speed at which data is gathered, elaborated the flashy information. •Value: This is the important part of big information. It confirm that the gathering of information is equalizing with the firm's necessities or not. •Volume: The information of big data is always come in large amount and it gathered in from of raw data. It cannot be used instantly. •Variety: It express the information which is mainly recorded by the machine or person in semi-structured or unstructured way for some particular reason. The challenges of big data analytics There are various challenges of big data which can be explained as given below: Union and consolidation of data: This process is the collection of many methods it mainly examine the information from collective origins and matters. Statistics: It is a very similar technique which is used in analytics of big data that involves aggregation, interpretation, firm and summarize the information. A/B testing: In this technique it includes comparison between the power of association, in order to determine what changes will raise the available objectives. Learning through machine: This is the current part to examine the information. Data mining: It mainly remove the design of big information bunch by connecting methods from statistics and technological learning, within the system of database 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 services and applications via cloudification. IEEE Aerospace and Electronic Systems Magazine. 33(5-6). pp.44-55. Fugini, M., Finocchi, J. and Locatelli, P., 2021. A big data analytics architecture for smart cities and smart companies. Big Data Research. 24. p.100192. Gökalp, M.O and et.al., 2019, November. Open-source big data analytics architecture for businesses. In 2019 1st International Informatics and Software Engineering How Big Data technology could support business & Examples •Enhance the product quality: Big data help in analyzing the demand of the consumers which help to improve the quality of existing product. It also suggests the company to launch new product to meet consumer wants. Big data provide support in that areas where company is lacking. •Data safety and security: Big data ensure that the data is stored at a place where it is secured and protected from the various risks, frauds and hacking. Techniques that are currently available to analysis big data •Raise the quality of good: It mainly assist in examining the customers demand which assist to better the quality of available good. They also recommend the firm to introduce new goods to achieve the customer desires. It provide helps in that fields in which firm is deficit. •Security and safety of information: It is confirmed that the information is protected at a place where it is kept and secured from many risks, hacking and frauds. Information is protected below more technologies which assist the company run without any mistake(Oneto and et.al., 2020). •Good decision making: Data methods assist in bettering the company decision making procedure in respect to increase