Discuss about the impact of big data for business innovation and its performance.
This research is conducted with a purpose of studying the impact of big data for business innovation and performance. Big data is the term which states the large volume of data which is used in the routine activities of business. It is a process which is used to get the meaning from the large and voluminous data. Big data is helping lot of businesses in order to segment their markets and providing their knowledge about the market so that they can make appropriate investments to make huge profits (Cheah and Wang, 2017).
This assessment is the part of the research where the proposal; will be made for the research. Here, research methodologies will be discussed and a pat of literature review will be summarised which was done in Assessment 1. Finally research plan is made which will be able to complete the research on time. A final conclusion is given in order to give a meaning to the research.
The main objective of this research is to find out the impact of big data on the business innovation and performance. The following are the main objectives of this research:
Big data is used at a massive scale by the organisations at global level. The demand for big data is soaring for professionals. There are huge job opportunities which also meets the skill gaps. It helps in providing the opportunity to improve the performance of the business (Chesbrough, 2010). This research has a wide scope as the research is done by taking into consideration the seven fields like education, transportation, electricity, oil, customer products and health care which will show how big data is creating value for the businesses of various industries.
According to AL-Jaafreh and Fayoumi (2017), the advancement in technology and social computing has created huge amount t of data to be managed by the companies. Now, the companies have entered into an era which involves big data which helps in the management of data and classifying it into the structured and unstructured data from various sources. The author further adds that this data is used by the companies to formulate strategies and to bring innovation in the big digitised world.
In the words of Garmaki, Boughzala and Wamba (2016), Big data analytics has become an important source of competition in the last few years. The authors discusses the potential benefits of Big data for the organisation which includes the enhancement of firm performance. It creates value for the firms but there are some challenges of implementing Big data in the organisation. The challenge is to maintain the ever increasing unstructured form of data. The data should be integrated by the companies in order to generate knowledge and for decision making in the firms.
In the view of Wamba (2016), Big data analytics is the process which helps companies in providing competitive advantage. It can prove game changer for the companies because of its high potential so it is identified that there is a positive relationship between Big data analytics and firm performance. . It can also be considered as a major differentiator between the high and low performing organisations. It helps firms to be proactive and to reduce the costs of the business in acquiring customers. It is seen that the organisations which have adopted big data has decreased the cost of customer acquisition by 47% and it has resulted in the rise of revenue by 8%. The example can be given for Target Corporation which uses Big data analytics in order to run its loyalty card programs for customers. Big data helps them to track customers and their buying behaviour so that their future buying trends can be identified through it. Another example can be taken of Amazon. In. Around 35% of the sales of Amazon. In is made on the basis of purchase recommendations given to the buyers by the company by tracking their interests, browsing trends and past purchase data. The author further shares the example of General Electric which is planning to use Big Data analytics for the usage in improving the efficiency of its gas turbines which could lead to billions of fuel savings for the company in the next 15 years (Kitchin, 2014).
According to Russom (2011), Big data analytics is used in various industries and have its major impacts on them. Like in the retail industry, the data is leveraged so that the customer experience can be improved, frauds can be reduced and recommendations can be made to the customers on the basis of their interests. In healthcare industry, Big Data analytics is used to reduce the cost of operations and to improve the life quality of the individuals. In manufacturing sector, Big data can be used to manage assets and used to monitor the performance of the business. It can also be used to have a strong supply chain and automation of industry so that the overall business can be transformed.
In the opinion of Rogge, Agasisti and De Witte (2017), Big data helps businesses to get insights on new product needs and demand in the market so that the businesses can act accordingly and fulfil the requirements of the society. This helps in bridging the gap between the thought processes of the customers and the businesses. Big data is the tool which reduces the gap between different industries and it also allows companies to share their best practices and to raise the productivity levels.
In the words of Poleto, de Carvalho and Costa (2015), Big data plays a crucial role in finding and analysing the best techniques to cater to the business requirements. It helps in increasing their productivity. Big Data nowadays is not only limited to the Tech industries but also used by various sectors. Regardless of which sector the company belong, big data provides meaningful insights and provides competitive edge to the businesses. It also helps in data visualisation and impact the business critically. As already discussed, it helps in understanding the needs of the customer and analyse the level of competition in the market. It identified the current trends of market and reduces the costs of the business which eventually helps in gaining maximum profits for the companies.
As per Arora and Rahman (2016), Big data also assist in improving the Lean management of the companies which is a process of enhancing the product quality and reduction of costs. Big data also helps in customising the experiences of the users by providing attention to the target audience. It helps in gathering data through social networking sites, internet and other sources which helps organisations in customisation of product and service delivery. In public sector also, Big data is used so that the finances can be managed and productivity and performance of the sick units can be enhanced. It also manages the employment in the public sector. It helps government in taking crucial decision regarding employment opportunities, education, development of society, etc. A survey was conducted in the year 2011 by the global Institute which concluded that the sectors which are most informative intensive are the financial services, gas, electricity, communication and others uses big data in order to use information for providing the services. Another report was prepared by OECD in which it was reported that the private sectors are more open for the use of big data. The sectors like infrastructure, healthcare, manufacturing, advertising, logistics an others use the raw knowledge for using it correctly for increasing the productivity of the companies in relation to their competitors (Labrinidis and Jagadish, 2012).
There are few challenges which is faced by the companies while using Big data. One of those challenges include the dealing with data growth because most of the data is unstructured. Other challenge is of recruiting best talent with professional attitude and knowledge about big data. Cost management is one more challenge as companies have to give training to employees for handling big data and for the maintenance and expansion of big data systems in the organisation. One more challenge is maintaining the security of the system and meeting the compliance regulations (Tole, 2013).
Big data is a significant part of the business where data driven insights are used to focus on the customer satisfaction and retention strategies so that the profitability can be maintained. It acts as a supporter or contributor to have innovation in the business which provides a competitive edge to the business in the current business surroundings (Che, Safran and Peng, 2013).
Primary Question
The primary question of the research aims to answer the following question:
Secondary Questions
The secondary research questions aims to answer the following questions:
Research methodology can be understood as an important part of the research which helps in understanding the methods and processes which are taken up for completing the project.
There are some key steps which are taken in order to ensure the correct process of the research. It is very important to determine that the overall objectives of the research match the research methodology so that valid conclusions can be made. Other important step is to determine that the suitable sample size is selected so that the genuine responses can be gained which helps in obtaining the relevant findings of the research. These steps are taken in order to ensure that the research is being done effectively and all the relevant points are taken into consideration so that the effective conclusions can be made.
The research design adopted is explanatory which aims to connect the ideas so that the cause and effect relationships can be understood between the dependent and independent variables (Wilson, 2016). Here the research question is intended to explain rather than simply describing it. Here, the impact of big data is studied on the performance of the organisations. The research will take into consideration both the qualitative and quantitative data in order to fulfil the purpose of the research.
For conducting this research, questionnaire survey strategy has been followed where the target sample is reached in order to obtain their responses on the survey questions in relation to the big data and its usage in business. The results of this questionnaire will be analysed on order to get the final results (Cooper, Schindler and Sun, 2006).
Mixed approach is used for conducting the research study. Here, both the qualitative and quantitative data is used to gain information and to test the relationship between the use of big data and the performance of the business (Greener, 2008). As per the research objectives, data is to be collected both in numerical and subjective form which makes the researcher use the mixed approach of research.
Survey questionnaire is the instrument which will be used to collect the data from the sample respondents. The survey questionnaire will include 10 questions based on the personal experiences of the respondents and on the basis of their own judgements regarding the use of big data.
Data for the research is collected through both the primary and secondary sources. Primary data which is fresh data collected from the help of survey questionnaire. Secondary data which is already used data is collected through published surveys, articles, journals, books and websites.
The data which will be collected during the research process will be analysed through the use of statistical tools like SPSS or MS Excel. The results were then be collected, sorted and presented with the help of charts and graphs. The methods like correlation, coefficient and regression analysis are also used in order to identify the relationship between the two variables of the research.
Non Probability sampling technique is used for selecting the sample respondents for the research (Sekaran and Bougie, 2016). Random sampling method is used for collecting the data from the participants. Thes will include the people working in different industries and using Big data. The sample size of the research is fixed as 100 employees who are working with big data in businesses. These employees will be able to provide their real experiences and what they think about the future of Big data in the business world.
The questionnaire is designed on the basis of Five Point Likert scale where the questions will carry the options ranging from strongly agree to strongly disagree. Strongly agree will be the first point which will be followed by Agree, neutral, disagree and strongly disagree at point 5. Questionnaire will include the statements related to the use of big data, its impact and importance in the business organisations (Zikmund, et. al, 2010).
The reliability and validity of the research is maintained by using authentic sources of information and by stating the references and citing them. Also, the reliability of data can be measured through the technique named as Cronbach alpha analysis. It is also to be noted that the responses gained from the questionnaire will be protected through strong passwords so that they cannot be used unethically.
The research may get exposed to several limitations which includes the limitation of time. The time variable with the researcher is limited which might hamper the in-depth analysis done by the researcher. The research is done on the small sample size of 100 individuals which might not give the idea of the whole population. The respondents might provide biased responses which hampers the genuine nature of the research.
The total time estimated to take up research is 10 weeks which is divided for completing every chapter. The research is conducted on the basis of plan/ blueprint which is explained below:
Chapter 1: Research plan (Introduction) – 2 weeks
This is the first step of the whole research process in which the plan is made as to how the research will be completed in phases. Here, the chapter introduction states about the actions which the researcher is going to take in the further research.
Chapter 2: Data collection through Literature review- 3 weeks
In this step data will be collected from secondary sources. The secondary sources used are books, journals, articles, websites, etc. which are published by other authors. In-depth analysis has been done to meet the objectives of the research.
Chapter 3: Research Methodology- 1 week
In this step, research methods are discussed which are undertaken to develop a research. This included research design, approach, data collection and data analysis. Limitations of the research also discussed.
Chapter 4: Data Analysis- 3 weeks
In this chapter, data is analysed with the use of various statistical tools in order to get the results out of the collected data.
Chapter 5: Conclusions and recommendations- 1 week
This step is the final one where the conclusions are drawn for the research on the basis of the data analysis and recommendations are given for future research.
Conclusion
It can be concluded from the research that big data is an important technique for enhancing the productivity of the business. It is used significantly by the companies whether private or public in order to compete in the market by using meaningful insights derived from big data analytics. The challenges might include the hiring of talents in order to manage big data appropriately, the organisational resistance for the same, dealing with the increasing growth of data and using the insights in the correct manner.
Thus, it can be said that Big Data is truly a process which is a game changer for the businesses. It provides many opportunities to the businesses and also poses certain challenges.
References
AL-Jaafreh, A and Fayoumi, A (2017). The Role of Big Data Analytics in Innovation: A Study from the Telecom Industry. Australasian Conference on Information System.
Arora, B. and Rahman, Z. (2016). Using big data analytics for competitive advantage. International Journal of Innovative Research and Development, 5(2).
Che, D., Safran, M. and Peng, Z. (2013). From big data to big data mining: challenges, issues, and opportunities. In International Conference on Database Systems for Advanced Applications (pp. 1-15). Springer, Berlin, Heidelberg.
Cheah, S. and Wang, S. (2017). Big data-driven business model innovation by traditional industries in the Chinese economy. Journal of Chinese Economic and Foreign Trade Studies, 10(3), pp.229-251.
Chesbrough, H. (2010). Business model innovation: opportunities and barriers. Long range planning, 43(2-3), pp.354-363.
Cooper, D.R., Schindler, P.S. and Sun, J. (2006). Business research methods (Vol. 9). New York: McGraw-Hill Irwin.
Garmaki, M, Boughzala, I and Wamba, S F (2016). The effect of Big Data analystics calability on firm performance. Pacific Asia Conference on Information Systems.
Greener, S. (2008). Business research methods. BookBoon.
Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. Sage.
Labrinidis, A. and Jagadish, H.V. (2012). Challenges and opportunities with big data. Proceedings of the VLDB Endowment, 5(12), pp.2032-2033.
Poleto, T., de Carvalho, V.D.H. and Costa, A.P.C.S. (2015). The roles of big data in the decision-support process: an empirical investigation. In International Conference on Decision Support System Technology (pp. 10-21). Springer, Cham.
Rogge, N., Agasisti, T. and De Witte, K. (2017). Big data and the measurement of public organizations’ performance and efficiency: The state-of-the-art. Public Policy and Administration, 32(4), pp.263-281.
Russom, P. (2011). Big data analytics. TDWI best practices report, fourth quarter, 19(4), pp.1-34.
Sekaran, U. and Bougie, R. (2016). Research methods for business: A skill building approach. John Wiley & Sons.
Tole, A.A. (2013). Big data challenges. Database systems journal, 4(3), pp.31-40.
Wamba, S F, Gunasekaran, A, Akter, S and Childe, S J (2016). Big data analytics and firm performance: Effect of dynamic capabilities. Journal of Business Research.
Wilson, V (2016). Research Methods: Mixed Methods Research. Evidence Based Library and Information Practice, 11, 56-59.
Zikmund, W G, Babin, B J, Carr, J C & Griffin, M (2010). Business research methods. South-Western Cengage Learning, Mason, Ohio
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