Information Systems and Big Data Analysis: Challenges, Techniques and Business Support
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This report discusses the challenges, techniques and business support of Information Systems and Big Data Analysis. It covers the characteristics of big data, the challenges faced in big data analytics, the techniques available to analyze big data, and how big data technology can support businesses with examples.
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BSc (Hons) Business Management BMP4005 Information Systems and Big Data Analysis Poster and Summary 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 analytics4 The techniques that are currently available to analyse big data 6 How Big Data technology could support business, an explanation with examples7 References10 Appendix 1:Poster11 2
Introduction Information system is a collection of various sets of data. It is a system definedforstoring,processingandcollectingdatathatprovidesthevaluable information.Itincludesnetworks,softwares,computersandvariousother components(Kumar, Mookerjee and Shubham, 2018). There are many companies in the world that operates on information system such as Google, Amazon, Alibaba etc. It also involves cloud computing, cloud storage, networking and various social networking sites. In case of big data, it is a combination of structured, unstructured & semi structured set of data. It is larger in size and used for mining the information. It is uniquely important for companies to enhance their operation, to provide better user interface, marketing strategies and to identify market opportunity(Ghani and et. al., 2019). This report will introduction about the information system and big data analytics along with characteristics and various challenges ofmanaging of big data. Further more, it defines the techniques available for big data and how big data is helping businesses in different sectors. What big data is and the characteristics of big data Big data is described as the complex set of structured & unstructured data. It is inlarge variety and in heavy volumes. It is utilized in information mining which is useful in artificial intelligence & machine learning. It is basically a larger amount of information or data collected from different sources such as social media sites, e- commercesites,advertisement,fillingregistrationforms,buyingproductsand transactions etc. Big data analytics has been used by many companies like Google, Facebook to store and authenticate large amount of complex data(Ngiam and Khor, 2019). Big data analytics used to identify various potential customer for business by understanding their interests and choices. Big data consists of large amount of data and information so that is impossible to processing and analysing the data using traditional method. Many organisations are using big data for placing ads to the targeted customers by knowing their choices. T In every business organisation big data has been used in almost all sectors. They are collecting the information and then uses the information to generate leads. Big data stored in computer data base system and this can be accessed by using specially designed software. There are various department in the company such as marketing, sales, human resource 3
managementandfinanceusingbigdatatechniquestoanalysingthemarket sentiments and its size that helps them to make strategies accordingly. Characteristics of big data There are five types of characteristics of big data analytics as following: ï‚·Variety:It is one of the characteristics of big data in which it defines the diversification of various types of data. Any organisation can collect data from varioussourcessuchassocialmedia,websitesandthroughonline registration forms.(Khan and et. al., 2019). the data can be in the form of audios,texts, emails anddocuments. Suchvariety of datais difficult to understand. ï‚·Volume:Volumeofbigdatagenerallyexplainsthathowmuchdatais generating in every bit of second from variety of sources such as online transaction, ticket bookings, cell phone, websites and many more. It stores all type of data such as weather data, technological information, medical data, financial information and many more. ï‚·Value:It refers to the data which is valuable in nature. It can be stored, processed and analysed. It helps in analysing the data quality and in addition with how much data is accurate at the same time. This helps in building strong customer relations. ï‚·Velocity:Velocity described how quick data can be generated, processed and analysed at the same time to have the useful information. It plays crucial role for businesses to taking decision quickly from sources such as social networks and other processed quickly. ï‚·Veracity:Veracity is one of the important characteristics of big data analytics that describes the accuracy of he data and its truthfulness. In this, data is managed in efficient and effective manner. Many times insufficient information or data causes to difficulty in understanding the data. The challenges of big data analytics There are several challenges faced by big data analytic are given below as: ï‚·Data combination:In this data combination, there are numerous sources fromwheredataisbeingcollectedsuchasinternetbrowsing,various magazine, through emails, social networkingplatforms and various other sources. Data collected from such different sources are also different in natures such as audio format, videos, text documents, images and many 4
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more. Therefore it is a big challenging task to segregate and sort this types of data to make it in sequential data type(Naeem and et. al., 2022). It is due to thattherearedifferentorganisationneedsdataaccordingtotheir requirements in which some organisation requires data in text format or some needs it in video form. It is very difficult to organising the data according to the requirements. There are also some data collected from various sources do notposessufficientinformation.Hence,itisnecessarytosortthem accordingly. ï‚·Data privacy and security:It is one of the biggest challenge as security threat to the data becomes common problem now days. Companies first collects data from various sources and then analyses it. But later they do not concern about security issue related to data and do not take appropriate steps in order to secure data(Xia, Wang and Niu, 2020). There are anonymous sourcesthatbreachesdatasecuritythroughhackingvariouswebsites, sending various emails of rewards to steal data from public and through registration forms etc. And most of the organisation also sells data to various companies illegally and it becomes a security threat to the people whose data being collected. ï‚·Data accuracy:Accurate data is most crucial for any organization to defining the strategies. It includes inaccurate data such as misinformation, data gap and quality of data. When organisation collects data from various sources there might be chances that people may be given the wrong information such as identity and contacts or might be they given less information. It leads to less quality and accuracy of data which results in less effective for the organisation to take appropriate decision. ï‚·Lack of understanding of big data analytics:There are various companies facing big challenges dueto insufficiency in dataunderstanding. As the technology is emerging rapidly but at the same time there is less number of skilled professional to understand this. They do not know about how to store data and how to process it. For instance, unskilled employee's are not aware of data importance, they do not keep backup of sensitive kind of data. Hence, when this sensitive data is required, it can not be accessed easily. Therefore, organisationrequirestoskilltheiremployeewiththehelpofproviding workshops and appropriate training to them. 5
ï‚·Datastoragefacility:Bigdataincludesalmostalltypesdatasuchas environmental data, financial data of organisation, weather information, cloud storage, social media contents and goes on. These kind of data is huge in size and amount of data stored in databases of organisations are increasing every bit of second. And it is impossible to manage using traditional method. Sothatitisachallengingfactorthathowandwheretostoresuch uncountable data. ï‚·Data integration:As data in a company comes from various sources like social media feeds, websites, applications, blogs, emails and reports etc. It is challenging task to integrate such data to prepare report. Data integration is essential for businesses in various aspects such as for preparing marketing strategy, defining marketing plan etc. The techniques that are currently available to analyse big data There are some following techniques currently available to analysing big data as: ï‚·Machine learning:It is one of the best technique for analysing big data. In this decision making algorithms is used as like artificial intelligence in which it suggests the relevant things automatically. It collects the data from various sourcesandthencategoriesitaccordingtodatatypes.Afterthatit understands the patterns and convert the data into appropriate information which is helpful for organisation. Machine learning algorithm is essential for collecting, integrating and identifying data(Coad and Srhoj, 2020). It is used in all elements of big data analytics including data analysis, labeling of data, simulation and segmentation of data. Mostly social media, television and other entertainment industry uses machine learning to analysing the interest and likes of people. Machine learning automatically identifies the customer pattern and then place ads accordingly using effective algorithm. It gives prediction that is almost impossible for human to predict. ï‚·Data mining:Data mining is the powerful tool used in big data analytics in which data is segregated according to data types. This is used by various organisations to segmenting the target audience based on their likes and interests. Data collected from various sources such as websites, social media, 6
E-commerce websites, online ticket booking and many other sources which is then mined to useful information. It is necessary to generate potential leads by placing the ads according to people choices. ï‚·Associativetechnique:Inthistechnique,itcreateslinksbetweenthe products sothat customer canbuy more things such as if a personis purchasingthealcoholismorelikelytobuythesteakswithitthen supermarketsplacetheserelativethingtogethersothatcustomercan purchase both things. This technique also involves up selling and cross selling of the products or services. This technique is using in big data in which companies maintain the data of their customers so that they can provide various offers and discounts to them. ï‚·Fixed investigative technique:This technique describes that how changing some variable affect on other variable. This technique is used to collect sentiments such as emotions and feelings. It further helps in investigating the data that where it can be useful in the organisation. ï‚·A/B testing technique:It is one of the important technique involved in the big data analysis. It includes comparison of a control group with different varieties oftestgroup(Anagnostopoulos,2018).Itspecifiesthatwhatchanges improves the objective variables. This technique basically defines thatwhat type of text, images and keywords will enhance the conversion rate that means how many people has shown interest in particular ads. ï‚·Statistics:Statistics technique is used to organising, collecting & interpreting data in various experiments. In this data are in form of different charts that is mostly used in surveys. HowBigDatatechnologycouldsupportbusiness,an explanation with examples Bigdatabecomesessentialformanyorganisationstotakecompetitive advantage. In many sectors new players in the market uses big data strategies to rapidly grow in the market. Now days almost every company is using big data techniquestounderstandtheircustomerpattern.Therearesomebigdata technology which is helpful for businesses as: ï‚·Identifying new revenue source:Big data gives the insights of the market and consumer behaviour. Company can gather the data from various sources and then it can make the marketing strategy accordingly(Hader and et. al., 7
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2022). Big data analytics helps businesses to know about the market potential and to know about direction of the market that which product or services is in demand or which is not. It helps companies to more focus on such products to enhanceprofitability.Withproperlymanagementofbigdataanalytics businesses can grow rapidly and can take benefits of it. It can also be new revenue source of income in which small companies can sell data to larger companies operating in the same industry. ï‚·Data security:Big data technique permit to mapping the overall data across the organisation. This helps in identifying all types of internal threats to the company. It is helpful to keep information safely and protected. There are many industries focusing on big data technique to ensure that data is being protected.ItismoreimportantforcompanieslikeE-commerce,online payments services etc. due to they involves in the financial transaction, credit & debit card information and other such data. Such companies are more focusing on data security in order to maintain the data of customer's financial information secure and safe. ï‚·Feedback:It is most important for any organisation to enhance their brand image. Big data is useful to collect feedback and use that feedback for increasing product quality. It helps to understand customer opinion and views regarding the products or services(Zbakh and et. al., 2019). Big data also helps to ask the suggestion for particular products or services of the particular organisation using emails, google forms and surveys. ï‚·Risk analysis:Big data analysis helps to determine the various internal and external threats to the company by identifying the social & economical factors. It can be done by analysing social media feeds, reports and following news. Hence, company can look into market trends and work accordingly. ï‚·Decisionmaking:Bigdatatechniquealsohelpingcompaniestotake decision quickly. In the company every departmentsuch as human resource management, finance, sales and marketing to planning the strategies. For instance human resource management requires skilled professional therefore HR searches on various websites to find out the suitable candidate similarly withmarketingdepartment,theyanalysesthemarkettrendsusingdata analytics and then create marketing strategy accordingly. 8
Conclusion From the above mentioned report it is concluded that companies are using information system to analyzing financial process, human resource management and toidentifyingpotentialcustomertogenerateleads.Informationsystemisalso important for businesses as well in which it helps to take decisions and to perform various activities in the organisation. It also includes about big data and how it is collectedfromvarioussourcessuchaswebsites,internetbrowser,online transactions, online ticket booking, credit card, social media and many more. It includes five characteristics of big data and various challenges. Further more, it defines the techniques available for big data and how big data is helping businesses in different sectors using data security, risk analysis, decision making, feedback and identifying new revenue sources. References Anagnostopoulos, A., 2018. Big data techniques for ship performance study. InThe 28th International Ocean and Polar Engineering Conference. OnePetro. 9
Coad, A. and Srhoj, S., 2020. Catching Gazelles with a Lasso: Big data techniques for the prediction of high-growth firms.Small Business Economics,55(3), pp.541-565. Ghani, N.A. and et. al., 2019. Social media big data analytics: A survey.Computers in Human Behavior,101, pp.417-428. Hader,M.andet.al.,2022.ApplyingintegratedBlockchainandBigData technologies to improve supply chain traceability and information sharing in the textile sector.Journal of Industrial Information Integration,28, p.100345. Khan,N.andet.al.,2019.The51v'sofbigdata:survey,technologies, characteristics, opportunities, issues and challenges. InProceedings of the international conference on omni-layer intelligent systems(pp. 19-24). Kumar,S.,Mookerjee,V.andShubham,A.,2018.Researchinoperations management and information systems interface.Production and Operations Management,27(11), pp.1893-1905. Naeem, M. and et. al., 2022. Trends and future perspective challenges in big data. InAdvancesinIntelligentDataAnalysis andApplications(pp.309-325). Springer, Singapore. Ngiam, K.Y. and Khor, W., 2019. Big data and machine learning algorithms for health-care delivery.The Lancet Oncology,20(5), pp.e262-e273. Xia, J., Wang, J. and Niu, S., 2020. Research challenges and opportunities for using big data in global change biology.Global Change Biology,26(11), pp.6040- 6061. Zbakh,M.andet.al.,2019.CloudComputingandBigData:Technologies, Applications and Security. Springer. Appendix 1:Poster 10
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