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Big data analytics1 SIGNIFICANCE OF BIG DATA ANALYTICS IN BUSINESS MANAGEMENT AND INNOVATION Name Course Tutor Institution Location of institution Date
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Big data analytics2 Executive Summary Big data analytics describes a complex procedure of assessing vast and extensive variety types of facts. Big data analytics aims to unearth the concealed facts (Evelles, Fukawa & Swayne, 2016:898). The facts may include customer preferences, hidden correlations and market trends. It is one of the most significant tools that organisations should have to assist the managers to make informed decisions. Big data is an expression that is used to make a distinction amid the new type of data and the old structured form of data. An organisation cannot rely only on its information from the internal sources, and it has to explore the external environment and gather data that will be used to make critical and innovative decisions (Evelles, Fukawa & Swayne, 2016:903). A vast number of research studies have given an attempt to provide the connotation of the word big data. It is described with three "Vs" (Bhatnagar & Kurnar, 2015:4). There is need to utilize other resources such as Big Data infrastructure to reap the benefits of big data analytics (Chen & Zhang, 2014:315). The use of big data analytics can help a business flourish in the market. As a result, there is the creation of employment opportunities which leads to an improvement in the living standards of people in various countries (Rajaraman, 2016:698). The paper begins with an introduction, followed by research rational and research questions. A review of literature is then conducted followed by methodology, ethical considerations and study limitations are presented.
Big data analytics3 Table of Contents Executive Summary.........................................................................................................................2 1.0.Introduction...........................................................................................................................4 Research Topic.............................................................................................................................4 The Significance of the Research Topic......................................................................................4 Rationale of the Research Project................................................................................................5 2.0.Research Questions...............................................................................................................6 General Research Question..........................................................................................................7 Specific Research Questions........................................................................................................7 3.0.Literature Review.................................................................................................................7 4.0. Methodology...........................................................................................................................10 4.1. Research Design..................................................................................................................10 4.2. Data collection....................................................................................................................11 4.3. Data Analysis......................................................................................................................11 4.4. Importance of the study.......................................................................................................11 5.0. Ethics......................................................................................................................................13 6.0. Limitations..............................................................................................................................13 7.0. Conclusion..............................................................................................................................13 7.0. Reference List.........................................................................................................................15
Big data analytics4 The significance of Big Data Analytics in Business Management and Innovation 1.0.Introduction Big data analytics describes a complex procedure of assessing vast and extensive variety types of facts. Big data analytics aims to unearth the concealed facts (Evelles, Fukawa & Swayne, 2016:898). The facts may include customer preferences, hidden correlations and market trends. It is one of the most significant tools that organisations should have to assist the managers to make informed decisions. Big data is an expression that is used to make a distinction amid the new type of data and the old structured form of data. An organisation cannot rely only on its information from the internal sources, and it has to explore the external environment and gather data that will be used to make critical and innovative decisions (Evelles, Fukawa & Swayne, 2016:903). However, the data collected from the outside sources are generally in large quantities, and in different types. This is why there is a need for big data analytics that helps to analyse the big data and come up with information that is helpful to the organisation. Research Topic The topic of the research will be an investigation into the significance of big data analytics in business management and innovation. The Significance of the Research Topic A large amount of data available from external environments is becoming a big challenge to many organisations in the world today (Hashem et al., 2015:106). It has become difficult to make the right choices on the information to use in their decision making. Big data analytics helps the managers of the organisations to analyse the big data to come up with well-structured details that can be used in decision making. Big data analytics also helps the managers to identify hidden information, especially about other competing businesses.
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Big data analytics5 In today's dynamic business environment, innovation has become a decisive tool needed by firms to make certain their continued existence in the marketplace. The owners of business and managers therefore, need to be modernized with the dynamic technology (Hashem et al., 2015:110). They have to ensure that they provide new services, find ways of producing and introducing new products in the market. The only way that they can do this is by having the ability to right of entry to both external and internal environment and make analysis to know the requirements of the market from the organisation. Rationale of the Research Project A vast number of studies show that analysis on the big data have had a strong bang in increasing the innovation of many firms. Conversely, BDA is not adequate to ensure that an organisation experiences increased growth and development (Hazen et al., 2014:74). To create big data analytics capabilities in a firm, there are numerous of other types of resources such BDA infrastructure and BDA personnel that the organisation needs to employ. Big data analytics capabilities describe the effects that big data analytics create to a firm's modernization. Also, there are other factors that a company should consider to ensure big data analytics (Hazen et al., 2014:78). One of the main factors is an organisation’s absorptive capacity. This describes the ability of a firm to acquire, implement and exploit the information acquired to ensure growth and development of the firm (Hazen et al., 2014:79). Absorptive capacity helps a company to gain a competitive advantage against their competitors (Hazen et al., 2014:79). This research project aims at investigating the significance of big data analytics in business management and innovation. The investigator hopes to accomplish the key objective of the study by: finding out the impact of significant data analytics capabilities on an organisation's innovation sector; examining the importance of absorptive capacity to organisations' innovation; investigating on
Big data analytics6 the function played by absorptive capacity in shaping the association between big data analytics capabilities and improvement sector of an organisation. Meta-analysis methodology will be used by the investigator to come up with the relevant information on the study. Various web pages, relevant previewed journals, and organisational reports will be used to seek more information about the research topic. To identify the relevant sources, the following keywords will be used: Innovation, big data, business management, analysis on the big data, ability to absorb and impacts of big data analytics. Next, the researcher will identify the superlative references which counterpart the criteria for inclusion-exclusion. Lastly, there will be monitoring of the prose for chosen references to search on the significance of analysis of big data in business management and innovation. 2.0.Research Questions The objective of the study is to examine the significance of big data analytics in business management and innovation. The researcher is aggravated by knowing that there is narrow study on how firms should exploit analysis of big data to achieve its benefits on the business' profitability in the market The vast amount of information from the external sources is becoming confusion to many companies when it comes to making critical decisions. Organisations should adopt the use of big data analytics as one of the most vital resources that can help create a competitive force against their competitors. Studies have shown that despite the intense competition and many challenges in the business world today, many firms have been able to ensure their survival in the market (Gunesekaran et al., 2017:310). Most of these firms have been able to adopt big data analytics that has helped them on how to deal with various challenges that exist in the competitive world of business today.
Big data analytics7 Nevertheless, big data analytics is not sufficient for organisations to guarantee big data capabilities in improving the innovation sector (Gunasekaran et al., 2017:315). Organisations need to utilize other resources such as BDA technology and personnel. Other factors such as the absorptive capacity of the firm should be considered to enhance the capability of the organisation in exploiting big data analytics. Therefore this study aims at examining the impact of absorptive capacity on the organisation’s modernization. Also the research also objects at the discovery of the function of absorptive capacity in shaping the affiliation amid the impacts of analysis of big data and a firms modernisation. General Research Question What is the significance of big data analytics in business management and innovation? Specific Research Questions 1.What is the impact of big data analytics capabilities on an organisation's innovation sector? 2.What is the importance of absorptive capacity in the organisation's innovative sector? 3.What is the function of absorptive capacity in shaping the affiliation amid big data analytics capabilities and business innovation? 3.0.Literature Review A vast number of research studies have given an attempt to provide the connotation of the word big data. It is described with three "Vs" (Bhatnagar & Kurnar, 2015:4). The first "V" is used to describe the volume of the big data. It is said to be in huge, and it comes from different types of sources both internal and external. The second "V" is used to describe the variety of big data. Studies show that it is of a vast number of varieties and it ranges from structured data to unstructured data. This is one of the main reasons why it creates a big challenge for firms when it
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Big data analytics8 comes to making critical decisions. The third "V" represents the velocity of the big data. This refers to the speed at which big data is produced, analysed and interpreted. Big data need to be analysed to be of importance to the organisations. This is where big data analytics arises. It involves a complicated procedure for assessing enormous amounts of big data originating from diverse sources to expose the unseen facts (Akter et al., 2016:120). The information may include consumer preferences, unknown correlations, information about competitors and market trends. Besides, big data analytics helps in unveiling information about what happened in the past and what is happening today in the market (Akter et al., 2016:126). This information is used by the managers and the owners of the business to predict what will happen in the future and the possible outcomes. According to the studies, there are three types of data analytics: Descriptive data analytics, predictive data analytics, and prescriptive data analytics. Descriptive data analytics involves data analysis on the occurrences that occurred in the past. Predictive data analytics requires analysis of data to predict what is probable to occur in the coming days (Ji-Fan Ren et al., 2017:5020). Prescriptive data analysis involves analysing various types of data from different sources to help the organisation make informed decisions to achieve its goals in the future. Besides, there is a vast number of big analytics tools that a firm can employ. Some of them are extracted information load, Hadoop distributed file systems and Data warehouse tools (Ji-Fan Ren et al., 2017: 5012). There is a need for organisations to know the meaning of big data and big data analytics to achieve the big data analytics capabilities in improving their innovation sector. Many firms have been struggling to make decisions with the wide variety of data from both external and internal sources (Kwon, Lee & Shin, 2014:388). This data may lead to wrong decisions that may lead to the failure of the business. Organisations need to create a
Big data analytics9 competitive advantage against their competitors and increase their efficiency for them to survive in the business environment. Conversely, an organisation cannot achieve the capabilities of big data analytics without using other resource. There is need to utilize other resources such as Big Data infrastructure to reap the benefits of big data analytics (Chen & Zhang, 2014:315). Big data analytics infrastructure may include infrastructure that is composed of information technology which helps the sectors of the company to work as a team to attain the main objective of the business. Also, BDA personnel expertise is another type of big data analytics infrastructure. These include the knowledge, professional skills, and familiarity with big data analytics (Cheng & Zhang, 2014:340). A firm can attain big data analytics capabilities if it is able to combine all the required resources to create a competitive advantage. The absorptive capacity of a firm is increased by its ability to apply big data analytics in its decision making process. The ability of an organisation to acquire information, analyse it , interpret and utilize it to improve the firm is known as absorptive capacity (Kwon, Lee & Shin, 2014:390). There are two types of absorptive capacity: Potential absorptive capacity and realised absorptive capacity. Potential absorptive capacity refers to the process of acquiring information from external sources, interpreting and analysing the obtained data (Jin et al., 2015:59). Realised absorptive capacity involves the ability of a company to bring the newly received data and the old information together. It also requires implementation of the acquired knowledge and exploiting it to create a competitive advantage that can help ensure its survival in the competitive market (Jin et al., 2015:63). Absorptive capacity is one of the critical tools for achieving big data analytics capabilities by an organisation.
Big data analytics10 Moreover, absorptive capacity is also one of the factors that lead to a firm’s improvement. Modernization involves the introduction of updated goods, systems and the firm’s structure. (Lazer et al., 2014:1204). Absorptive capacity helps a firm to acquire information from the external sources, analyse it through big data analytics and exploit it to come up with informed, innovative business decisions. This shows that the absorptive capacity has a big role in determining the big data analytics capabilities of a firm with its innovation. 4.0. Methodology 4.1. Research Design The main research design to be used in the study will be meta- analysis. According to Saunders (2011:47), Meta analysis research design refers to an approach where important qualitative and quantitative data from a number of studies are combined to come up with one conclusion that is more relevant to the research topic. The main objective of the study will be to look into the significance of big data analytics in business management and the innovation sector of organisations. The researcher will achieve the aim of the study by looking in to the effects of immense data capabilities on the firm's modernization sector. Also, he hopes to accomplish the primary objective of the study by investigating the importance of absorptive capacity in an organisation's innovation sector. Moreover, researcher will also look in to the function of absorptive capacity in the determination of the affiliation amid the capabilities of the big data analytics and business innovation. Various web pages, previewed journals, and organisational reports will be used for more information on the research topic. The data used must come from convincing sources and must have widespread prose on big data analytics and business management and innovation.
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Big data analytics11 4.2. Data collection The researcher will use Google search to seek information on the research topic. Firstly, the researcher will use the keywords of the research study to find the most satisfying sources. The key terms include: Business management, big data, improvement or innovation, capabilities of the big data analytics and the organisation’s absorptive capacity. In the next step, the investigator will use the criteria called inclusion –exclusion to come up with the most appealing sources. He will also select the best sources from the first twenty sources by screening them.In the next stage, the investigator will determine if the references have wide-ranging data on analytics and business innovation. By doing this, the references that mention the keywords of the topic but have little information about it will be avoided. In the last step, the examiner will monitor the prose of each selected reference to investigate on significance of big data analytics in business management and innovation. 4.3. Data Analysis Analysis of data will be done using the information found in the chosen references according to the strength portrayed in supporting the research area. The type of the variables to be used in analysing the data will be determined by the type of information unruffled from various references. The researcher will use NVivo 11 software to determine the main themes of the study and carry out the coding of the collected data. 4.4. Importance of the study Research area “An investigation of the significance of big data analytics in business management and innovation" is significant to the world's economic growth and development. Big data analytics is an important tool that any business may require to guarantee continued existence within the today's changing business world (Mulller et al., 2018:506). It helps business
Big data analytics12 managers make informed decisions in the organisations. Managers are the top decision-makers in most of the firms. The rate of innovation of an organisation highly depends on the strength of the innovative decisions made by the managers (Muller et al., 2018:489). The use of big data analytics can help a business flourish in the market. As a result, there is the creation of employment opportunities which leads to an improvement in the living standards of people in various countries (Rajaraman, 2016:698). Moreover, growth and development of organisations also increase the GDP of the nations where the organisations are located. Besides, the research is important since the existing scarcity in the study about the subject. Numerous businesses don’t understand how to utilise big data analytics to generate a active institutional competence. There is a necessity to create awareness that the capabilities of the big data cannot be produced using only the big data analytics. Firms need to use other resources and also consider factors such as their absorptive capacity (Rajaraman, 2016:698). This is to ensure that they reap the benefits of using big data analytics by creating a strong competitive force against their competitors in the business environment. The research study aims at providing adequate literature on the significance of big data analytics in business management and improvement. In the study, researcher focuses in looking in to the impact of capabilities of the big data analytics in the firms' innovation sector. It will also investigate the importance of absorptive capacity to an organisation's innovation sector. Moreover, the study will also look in to the function of absorptive capacity in shaping the affiliation amid big data analytics capabilities and improvement in various organisations.
Big data analytics13 5.0. Ethics Before carrying out the research study, the investigator will first present the objectives and the topic of the study to the University. If the School gives consent to the student to go ahead with the research work, the researcher will conduct the study. Moreover, he will also ask for permission from different authors to be given the opportunity to criticize and analyse their work. Also, the researcher will go through the literature of different authors and come up with his original report without copying their work. At the end of the research, he will use the results of the study to come up with literature about the significance of big data analytics in business management and improvement. This information may be used by other researchers and managers from various organisations to conduct their studies on the topic. 6.0. Limitations The chosen research methodology gives the key limitations of the study. Meta-analysis takes much time. The researcher must read a lot of literature to come up with the relevant sources. Moreover, the information obtained through meta-analysis may be biased and some of it may be omitted. This is because the researcher is not given a chance to collect primary information from various people in the field. The researchers are subjective to their own opinions depending on their experiences; there the conclusions made by each researcher may not be satisfying to the other. 7.0. Conclusion Big data analytics has several implications on business. Data collected can be used by companies to find out new income streams. The effective use of data analytics in business
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Big data analytics14 operations can promote the performance of an organisation. The data collected can be used to show the performance indicators in a certain sector which will, in turn, allow decision makers to develop strategies on how to remain competitive. Organisations can use big data to predict the future trends and upgrade performance of a process or system.
Big data analytics15 7.0. Reference List Akter, S., Wamba, S.F., Gunasekaran, A., Dubey, R. and Childe, S.J. (2016) How to improve firm performance using big data analytics capability and business s strategy alignment?.International Journal of Production Economics,182, pp.113-131. Bhatnagar, V. and Kumar, N. (2015)Big Data Analytics. Springer International Publishing. Chen, C.P. and Zhang, C.Y. (2014) Data-intensive applications, challenges, techniques and technologies: A survey on Big Data.Information Sciences,275, pp.314-347. Erevelles, S., Fukawa, N. and Swayne, L. (2016) Big Data consumer analytics and the transformation of marketing.Journal of Business Research,69(2), pp.897-904. Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S.F., Childe, S.J., Hazen, B. and Akter, S. (2017) Big data and predictive analytics for supply chain and organisational performance.Journal of Business Research,70, pp.308-317. Hashem, I.A.T., Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A. and Khan, S.U. (2015). The rise of “big data” on cloud computing: Review and open research issues.Information systems,47, pp.98-115. Hazen, B.T., Boone, C.A., Ezell, J.D. and Jones-Farmer, L.A. (2014) Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications.International Journal of Production Economics,154, pp.72-80.
Big data analytics16 Ji-fan Ren, S., Fosso Wamba, S., Akter, S., Dubey, R. and Childe, S.J. (2017) Modelling quality dynamics, business value and firm performance in a big data analytics environment.International Journal of Production Research,55(17), pp.5011-5026. Jin, X., Wah, B.W., Cheng, X. and Wang, Y. (2015) Significance and challenges of big data research.Big Data Research,2(2), pp.59-64. Kwon, O., Lee, N. and Shin, B. (2014) Data quality management, data usage experience and acquisition intention of big data analytics.International Journal of Information Management,34(3), pp.387-394. Lazer, D., Kennedy, R., King, G. and Vespignani, A. (2014) The parable of Google Flu: traps in big data analysis.Science,343(6176), pp.1203-1205. Müller, O., Fay, M. and vom Brocke, J. (2018) The effect of big data and analytics on firm performance: An econometric analysis considering industry characteristics.Journal of Management Information Systems,35(2), pp.488-509. Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M., Seliya, N., Wald, R. and Muharemagic, E. (2015) Deep learning applications and challenges in big data analytics.Journal of Big Data,2(1), p.1. Rajaraman, V. (2016) Big data analytics.Resonance,21(8), pp.695-716. Reyes-Ortiz, J.L., Oneto, L. and Anguita, D. (2015) Big data analytics in the cloud: Spark on hadoop vs mpi/openmp on beowulf.Procedia Computer Science,53, pp.121-130. Saunders, M.N. (2011)Research methods for business students, 5/e. Pearson Education India.
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Big data analytics17 Singh, D. and Reddy, C.K. (2015) A survey on platforms for big data analytics.Journal of big data,2(1), p.8. Storey, V.C. and Song, I.Y. (2017) Big data technologies and management: What conceptual modeling can do.Data & Knowledge Engineering,108, pp.50-67. Sun, Y., Song, H., Jara, A.J. and Bie, R. (2016) Internet of things and big data analytics for smart and connected communities.IEEE access,4, pp.766-773. Tsai, C.W., Lai, C.F., Chao, H.C. and Vasilakos, A.V. (2015) Big data analytics: a survey.Journal of Big data,2(1), p.21. Wamba, S.F., Akter, S., Edwards, A., Chopin, G. and Gnanzou, D. (2015) How ‘big data’can make big impact: Findings from a systematic review and a longitudinal case study.International Journal of Production Economics,165, pp.234-246. Wamba, S.F., Gunasekaran, A., Akter, S., Ren, S.J.F., Dubey, R. and Childe, S.J. (2017) Big data analytics and firm performance: Effects of dynamic capabilities.Journal of Business Research,70, pp.356-365. Wu, X., Zhu, X., Wu, G.Q. and Ding, W. (2014) Data mining with big data.IEEE transactions on knowledge and data engineering,26(1), pp.97-107. Yin, S. and Kaynak, O. (2015) Big data for modern industry: challenges and trends [point of view].Proceedings of the IEEE,103(2), pp.143-146. Zakir, J., Seymour, T. and Berg, K. (2015) BIG DATA ANALYTICS.Issues in Information Systems,16(2).