Big Data: Characteristics, Challenges, Techniques and Support to Businesses
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This report discusses the characteristics, challenges, techniques and support of big data to businesses. It covers the definition of big data, its types, characteristics, challenges, and modern techniques of big data analytics.
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INDIVIDUAL POSTER AND ACCOMPANYING REPORT
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TABLE OF CONTENTS INTRODUCTION...........................................................................................................................3 MAIN BODY...................................................................................................................................3 Definition of big data and its characteristics-..............................................................................3 Challenges and techniques of big data analytics-........................................................................4 Big data support to businesses-....................................................................................................5 CONCLUSION................................................................................................................................7 REFERENCES................................................................................................................................8
INTRODUCTION Big data is larger as well as more complex data from the new sources of data. These are very difficult to manage by data processing software as they are much voluminous. These large volumes of data can be utilized to address the problems of the organization which were not able to handle previously. This report will describe the big data as well as its characteristics. Along with this, it will also discuss the challenges of big data analytics and the techniques which are presently available for the analysis of big data as well. Later on it will also discuss how can this big data analysis can support the organizations. MAIN BODY Definition of big data and its characteristics- Data is the characters, symbols, quantities on which the computers operate. These can be stored and transferred from one computer or device from another. Big data is data which is very large in volume and is growing very rapidly(Farboodi, M., et.al., 2019). The size of these data is very large that it is very difficult to manage by the traditional tools of data management and even they cannot store and process it effectively. Big data is set of technology which has been created to analyse, store as well as management of these voluminous data. In present era, it is used in various areas like medicine, environmental protection, businesses, agriculture etc. Big data analytics helps businesses in various aspects such as helps businesses in better understanding of consumers, identification of operational issues, managing the supply chains and detecting the fraudulent activities happens in the businesses. It is very important for the businesses as it helps organization in assessing their data and identify the new opportunity. That turns into the smarter business, earns higher profits and customers are satisfied. Different types of big data- These big data are classified into three categories- 1.Structured data-These data are highly organized and managed by set parameters and these data are very easy for working. 2.Unstructured Data-It is usually all the unorganized data and it takes time and efforts to organize these data.
3.Semi-structured data-It draws a line between structured and unstructured type of data. These are data which does not follow the data model but it has some structure (Rossi, R. and Hirama, K., 2022). Characteristics of Big Data- There are five Vs of big data: 1.Volume-This feature of the big data includes the amount or size of big data that the businesses manages and analysis. 2.Value-It is the most important V of the big data from the business point of view. Value is the term which describes the usefulness of data which has been gathered. To be useful and valuable the big data needs to be converted into the information or insights. Value of big data is very significant as it states that how much the Worthy the data is having positive impact on the businesses. 3.Variety-There are variety of types of data such as unstructured data, semi-structured data and raw data(Ranjan, J., 2019). 4.Velocity-It includes the speed at which businesses store, manage and receive the data. 5.Veracity-It states the accuracy of the data and its information. Challenges and techniques of big data analytics- There are various challenges which are faced using the big data are- 1.Lack of knowledge-Modern technologies and managing tools for large data companies require highly skilled employees. For these companies hire data scientists, data analysts and data engineers to work for these big data. The biggest challenge faced by the companies there is lack of knowledge for working with these big data this is because there are many tools manufactured to handle the data but there is lack of knowledge in professionals to handle these data. 2.Data Growth issues-One of the most difficult challenges faced is professionals does not have much quantity of knowledge to combat with the increasing databases of the companies. As most of the information in data is unstructured which comes from documents, audio, texts, videos etc. Most of the Companies uses modern techniques for handling these big data such as compression, tie ring, de-duplication etc. Companies often uses data tiers like public cloud, private cloud etc.
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3.Integration of data-Data is integrated from various resources to convert it into valuable information. Sources can be like from e-mails, customer logs, financial reports etc. To combine and integrate all the data for organizing reports can be a very difficult task for theprofessionals.Integrationofdataisverycriticalpartforanalysis,business intelligence as well as for reporting. So for meeting up this challenge this tool will be very effective. 4.Securing data-Securing these massive data is very difficult the professionals having high skills can only work with these big data. The companies are much engaged in storing, analysing as well as understanding the data sets that they leave securing data at last stages. But this not the sensible move as not securing data can lead to a very problem for the organization. Modern techniques of big data analytics- 1.Predictive Analytics-One of the most frequently used tools in businesses is predictive analytics. This tool is used to eliminate the risks of decision-making in the businesses. This tools hardware and software solutions can be used to evaluate and deployment of predictive scenarios for processing the big data (Rabhi, L., et.al., 2019). By evaluating this, it enables companies to get prepared for the coming problems and can be solved by understanding them. 2.Knowledge discovery tools-This tools helps the businesses to organize the big data which are stored at various sources. These sources can be such as APIs, DBMS and related platforms. These tools allow the businesses to isolate and can use the information for the company's advantage. 3.Stream Analytics-The data stored of the organizations can be stored on the various sources. This software is very useful for filtering, analysis and aggregation of big data. 4.In-memory Data fabric-This technological tool helps in the distribution of data to different sources like Dynamic RAM, Solid state storage drives. This enables in low latency of the access as well as low processing of big data at the connected nodes. Big data support to businesses- The big data can help the businesses in many ways such as: 1.Making better business decisions-It enables the business to take smarter decisions which related to data. Every company must have access to data for improvement of
decision-making. IT departments are not only responsible for the handling these data, every business user be able to explore and integrate the data when needed. This company access is usually known as democratization. For example, as a single sales steam of the organization cannot understand and evaluate that why the sales are dropped or reduced so everyone in that business is responsible for giving the answer to this problem. By going through the data, they identified that because of some calculation this problem occurred. 2.Understanding the customers-It is very important for the organizations to understand their customers for their satisfaction. Big data enables the organization to understand the customers and through which they can serve them. For example, Facebook uses knows about the life of the customers and by using this big data it helps them to serve the customers. Disney also taking advantage of big data for the growth of the organization by understanding the behaviour of the customers when they visit theme parks and from this they can offer more to them. 3.Providing smarter products-By evaluating the data the organizations can provide better services or products to the customers. Having big data with the organizations helps in providing them with better services to the customers for their satisfaction. For example, Royal Bank of Scotland also uses big data for delivering better services to its customers as they know lot about their customers (Marinakis, V., et.al. 2020). 4.Improvement in operations of businesses-Many of the other business sectors are using automation technology and becoming more efficient (Choi Wallace and Wang, 2018). This increased use of automation is underpinned from big data HR software company People-doclaunchedaRoboticProcessAutomationPlatform,whichrunsinthe companies and also listens to the processes which can be automated. 5.Generationofincome-Bigdataisnotonlylimitedtoimprovingdecisions, understanding the customers but it can also help in increasing the revenue or adding more incomefortheorganizations.Forexample,Amexistakingadvantageofthe organizations stored data to maintain a strong relationship between the customers and the businesses. Like this company has added new technology to its merchant services like online trend analysis as well as benchmarking tools which are used to compare their working with their competitors in the market.
CONCLUSION This assessment report has discussed the types of big data and its challenges in the organization. Along with this, it has also discussed the characteristics of big data and techniques currently available to deal with this. Further, it enables to understand about the support of big data to the businesses.
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REFERENCES Books and journals Choi, T.M., Wallace, S.W. and Wang, Y., 2018. Big data analytics in operations management. Production and Operations Management. 27(10). pp.1868-1883. DeMauro,A.,et.al.2018.HumanresourcesforBigDataprofessions:Asystematic classificationofjobrolesandrequiredskillsets.InformationProcessing& Management.54(5). pp.807-817. Farboodi, M., et.al., 2019, May. Big data and firm dynamics. InAEA papers and proceedings (Vol. 109, pp. 38-42). Marinakis, V., et.al. 2020. From big data to smart energy services: An application for intelligent energy management. Future Generation Computer Systems.110. pp.572-586. Rabhi, L., et.al., 2019. Big data approach and its applications in various fields. Procedia Computer Science.155. pp.599-605. Ranjan, J., 2019. The 10 Vs of Big Data framework in the Context of 5 Industry Verticals. Productivity.59(4). Rossi,R.andHirama,K.,2022.Characterizingbigdatamanagement.arXivpreprint arXiv:2201.05929. 1