Information Systems and Big Data Analysis - BSc (Hons) Business Management
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This report covers the concept of big data and its features, challenges in big data analytics, techniques to analyze big data, and how big data technology can support businesses. The BSc (Hons) Business Management course code is BMP4005.
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BSc (Hons) Business Management BMP4005 Information Systems and Big Data Analysis Poster and Accompanying Paper Submitted by: Name: ID: 1
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Contents Introduction3 What big data is and the characteristics of big data3 The challenges of big data analytics3 The techniques that are currently available to analyse big data 4 How Big Data technology could support business, an explanation with examples4 Poster5 References6 2
Introduction Big data termed as form of data which is large, massive, prompt and complex. This form of data is different because it is difficult to handle and manage the large data. Data is a raw form of facts and figures which are required to be organized in the form of tables. This report contains the concept of big data and features of the big data. There are certain challenges which are to face while using the technology of big data analytic. There are various techniques which are available to analyze the concept of big data. There are various ways in which big data technology support business(Nemati and Khajeheian, 2018). What big data is and the characteristics of big data Big data is a form of technology which is used in storing, analyzing and managing the massive data. It is a tool which is used to identify the patterns of the data. In every field of area such as medicine, agriculture, gambling and environmental protection, the application of big data is vital(Markus, 2021).There are numerous characteristics of big data. Some of them can be elaborated as given below: 1.Variety– The data is a raw facts and figures which can be structured, unstructured and semi-structured. The structured form of data is arranged in database using the relational data base management system. The unstructured form of data is not available in the organized form. Semi structured form of data is partially arranged in the organized format. There are diverse and range of different data types. 2.Velocity –It refers to the speed at which organization receive, store and manage the varied data. 3.Volume– The data is available in large quantity which cannot be organized easily. There are clusters of data available. It is usually the collection raw facts and figures which are not sorted. The computerized data keeping system is a process of recording data which helps to analyze the data and make conclusions from the same. 4.value– It is another important feature of big data. The value of big data is that it helps in the finding the patterns from the sequence of data which results in increasing the performance of the business. The challenges of big data analytics In today's era, everything works with the use of technology. There are various challenges faced by the big data analytic which can be described as given below: 1.Lack of knowledge professionals: There are various new technology which require trained personnel. The hired professionals require training sessions which involve huge cost. The professionals include data scientists, data analytic and data engineers. The organizations are investing huge cost on the recruitment of professionals who are having good command in technical knowledge(Galetsi, Katsaliaki and Kumar, 2019). 2.Integrating data from a spread of source – There are several sources of collecting data. The information can be collected from social media pages, customer logs, financial reports, email presentations and reports created by employees. Integration of data is necessary for the interpreting the results and make judgments from the same. 3.Data security – One of the toughest challenge of big data is its security. The massive information stored on the database are unprotected and it is a big advantage for the malicious hackers. There are various malicious software such as Trojan horse, viruses and worm which adversely affect the working of the system. 4. Data growth issues – The quantity of the data is very large and it grows exponentially. Data usually comes in the unstructured form and comes from documents, videos, audio, text 3
files and other sources. While sorting the data into various categories, it is important to know the category of the data. Thus, if the personnel do not proper knowledge of the information, it became difficult to keep the backup of the data. 5. Confusion in selecting big data tools- there are several tools to handle the big data. The tools of big data are applied according to the different data. Therefore, it is a typical task to choose the right data tool(Wang and Luo, 2021). The techniques that are currently available to analyse big data As per the report of McKinsey,there are several techniques which are used to analyze the big data. It can be described as given below: A/B testing: The tool of a/b testing helps in comparing the control group and experimental group. In the initial phase of a/b testing, the hypothesis are created and categorization of variables such as independent and dependent variables are being done. The test is performed on the independent variables and impact is studied on the dependent variable(Noonpakdee, Phothichai and Khunkornsiri, 2018). Data fusion and integration- The main aim of the data collection from various sources is to analyze the pieces of data to study its aspects separately. Data integration is a process which synthesis the data to find the conclusions and make interpretations from the same. Statistics – It is the branch of knowledge which relates with collection, organization, analysis, interpretation and presentation of data. The whole statistics is divided into two major categories: descriptive statistics and inferential statistics. The descriptive statistics deals with the measures of central tendency and inferential statistics helps in testing of data. Data mining – the process of data mining enables the organization to extract the data from large pool. It is used to find patterns and correlations from the existing set of data. Natural language processing – In the fifth generation, there is introduction of artificial intelligence and cloud computing. The language used in this recent generation resembles with the normal human language. It helps to easily understand the coding and bugs can be easily removed by the same. HowBigDatatechnologycouldsupportbusiness,an explanation with examples Big data technology contributes in business to evaluate various trends that are prevailing in market at present and what are the preferences of a customer. There are certain ways that help to explain big data analytic support business described as under: Improvising the product quality: With the help of technologies that are related to Big data it is comparatively easier to improve product and services provided by a organization(Manogaran and et.al., 2020). The reason behind is that it already knows where the firm is lacking and what are certain methods that the business would adopt. Data safety and security: Big data ensures that the information is kept at priority and protected from unwanted frauds, risks and breach of useful data. Data can be guarded by high technologies and developing system that would improve efficient working of the business. 4
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ï‚·Better decision making: Data collection that help in providing guidance as what can be done, how can be done, the strategiesthat would contribute in facilitating comparison between companies and over years as well. It helps to find out reasons that lead to wastage and increase of unwanted costs. ï‚·Optimum use of scarce resources: It helps to locate resources that are limited in nature for best results. It thus provides numerous ways for increasing revenue generation and use resources in such a way that would prove to be the best option. It focuses on utilization of funds in such a way that provides competitive advantage over others as well through innovative policies and strategies. ï‚·Tap potential customers: Big data provides information about customers that would provide maximum profit and output in a business. It helps to provide various tools that would attract more consumers from market that would help top develop system contributing in carrying out related activities and operations. It can tap areas that remain uncovered and can prove to be an opportunity for organization in near future (Globa and et.al., 2018). Poster 5
References Globa and et.al., 2018, June. Ontology model of telecom operator big data. In2018 IEEE InternationalBlackSeaConferenceonCommunicationsandNetworking (BlackSeaCom)(pp. 1-5). IEEE. Manogaranandet.al.,2020.FDM:Fuzzy-optimizeddatamanagementtechniquefor improving big data analytics.IEEE Transactions on Fuzzy Systems.29(1). pp.177- 185. Noonpakdee, W., Phothichai, A. and Khunkornsiri, T., 2018, April. Big data implementation forsmallandmediumenterprises.In201827thWirelessandOptical Communication Conference (WOCC)(pp. 1-5). IEEE. Galetsi, P., Katsaliaki, K. and Kumar, S., 2019. Values, challenges and future directions of bigdataanalyticsinhealthcare:Asystematicreview.Socialscience& medicine.241. p.112533. Markus, M.L., 2021. Not your PAPAS’problem—users and ethical use cases in the big data analytics age: A rejoinder to Richardson, Petter, and Carter.Communications of the Association for Information Systems.49(1). p.21. Nemati, S. and Khajeheian, D., 2018. Big data for competitiveness of SMEs: Use of consumeranalytictoidentifynichemarkets.InCompetitivenessinemerging markets(pp. 585-599). Springer, Cham. Wang, P. and Luo, M., 2021. A digital twin-based big data virtual and real fusion learning referenceframeworksupportedbyindustrialinternettowardssmart manufacturing.Journal of manufacturing systems.58.pp.16-32. 6