Uses of big data in business organizations

Uses of big data in business organizations


The term big data indicates huge amount of data, which can be either structured or unstructured. Big data is a new technological trend that affects daily activities of the business. However, John Walker (2014) stated that, the amount of big data an organization is using does not make difference. Instead, the way an organization is using the data is important to obtain the full benefits from this technology. Big data is generally used in organizations for getting deep insight of the concerned matters and taking better decisions. Not only in large organizations, has use of big data now become important for the start-up businesses also. In this research, how big data is being used in the organizations is analyzed with a special focus on the multinational companies.

Project Objective 

  • To find out the way multinational or well-established businesses use big data and how does it affect their activities
  • To find out the way businesses get access to the big data
  • To identify the disadvantages of relying on big data extensively, if there is any
  • To determine the ways, unrecognized and unauthorized businesses access big data

Project Scope

The current research deals with analyzing the use of big data in large businesses, especially in the multinationals and the well-established businesses for managing and maintaining relationship with the customers , to maintain the product quality, business expansion and also for expanding their network. The research also focuses on identifying the ways businesses employ to access the big data. Effects of relying heavily on the big data in decision making are also identified in this research. Finally, the research will find out how do the unrecognized and unauthorized businesses access big data.

Literature Review 

When it comes to use the big data, the first concern that strikes the businesses is how to collect the data. Data mining is a technique which is used by the organizations to gather data. Data mining helps the businesses to identify systematic patterns within the variables (Marz & Warren, 2015). In addition, the data mining techniques also help businesses to validate the identified patterns using new data subsets. The primary objective of data mining is predicting the information in advance. Predictive data mining is the most common technique of exploring information and employing in different business applications. The predictive data mining is done in three different stages, starting from the initial exploration, building models and validating those and deploying the identified data. MapReduce is another approach that helps the businesses to explore huge amount of data. MapReduce is the basic component of the Apache Hadoop software which facilitates the organizations to process huge amount of unstructured and passive data (Wu et al. 2014). The MapReduce technique distributes the data across computer clusters where each node of the cluster possesses a separate storage system. MapReduce is useful for the businesses for two reasons -- firstly, it interacts with different nodes of the system inside the cluster and secondly, it reduces the output obtained from every node in the form of an answer for a query.
Emergence of Big Data has removed the boundaries between data management unit and other unit of the business (Lazer et al. 2014). Previously big data was mainly controlled by the IT department of the organization and the data related activities were performed by a special group of experts. However, nowadays, businesses are in need of a close integration between the data and other business units. Big data is now considered as an important part of the advanced analytics.
The big data platforms are completely different that the conventional relational databases. Using big data platforms the organizations can manage and integrate the information they have efficiently (Wu et al. 2014). Use of the big data platforms also help businesses to ignore the information which are not in focus right now. The facility to process selective data in the big data platforms is advantageous in various ways. It reduces the effort businesses had to spend earlier for analyzing data and deciding whether it is important or not. Big data platforms analyze the entire data set available to the businesses and eliminate the data which is not relevant for the businesses.
The big data help businesses to solve complex business problems in several distinct stages. At the first stage, businesses have to clearly frame the business case (Erl et al. 2016). At the next phase, the businesses have to identify the information they need and decide the analysis approach. The next stages involve with sourcing, normalizing and integrating the data. Once it is done, the data is analyzed and the data is validated. The answer obtained in this way is used to make decisions and analyzing their outcomes.
Using big data is advantageous for the organizations because of various reasons. One of the main causes of using big data is the high capacity of the data platforms. Low cost is one factor that makes the big data platforms attractive to the organizations. Businesses can easily gather data from the source and then select the particular data for solving complex business issues in timely manner.
Although big data benefits the businesses in several ways, there are many risks associated with it. The most obvious risk of relying heavily on the information technology is losing data security. The risk of data theft is growing day by day and the risk increases with the size of data being used by the organization. Theft of valuable data of the businesses can lead to loss of business opportunities and the customers (Raghupathi & Raghupathi, 2014). Privacy loss of valuable data is another risk that is associated with the extensive use of big data. The large businesses usually deal with huge amount of data on the customers. Therefore, it is the responsibility of businesses to keep the data private. Not only from the external criminals, protecting the data is necessary from the employees of an organizations too who are responsible for using the information. If the data is not protected in the right way, the organizations can encounter lawsuits because of violating the rules and regulations. 
Collection, storage and analysis of the data involves huge amount of cost (Chen et al. 2014). In addition to this, the cost can incur in some unwanted situations too, such as violation of the legal compliances. Making a budget before managing the data is an excellent technique to keep the cost in control (Kitchin, 2014). However, in some cases, sticking to the budget may not be possible and therefore, the organizations need to bear extra financial burden.
The big data analytics give the best results when they are interpreted in the right way which requires expertise (Jagadish et al. 2014). However, if the findings of the data is not analyzed and interpreted in the right way, businesses will not be able to take the right decisions which can lead to costly mistakes.
The above discussion indicates a business gets the most out of big data only when it is used in the perfectly. Failure to capture and analyze the real-time data reduces effectiveness of this cutting-edge technology. The current research reveals the right strategy of businesses so that they can obtain all the benefits from big data.

Research Questions:

Primary Question:
How do the businesses use big data to get the maximum benefits?
Secondary Questions:
How to source the big data?
How does the big data help businesses in decision making?
What are the risks associated with extensive use of big data?

Research Design and Methodology

The current research needs collection of data and analysis of the collected data to get the real-life implications of the research problem. In this research both the qualitative and quantitative data will be collected.
Qualitative Research:
The qualitative research will involve collection of qualitative data and analyzing the data. Interview is a useful way to collect the qualitative data (Kumar, 2014). At the first step of qualitative research, non-probability sampling needs to be used for selecting the interviews. As the interviewees should have in-depth knowledge and expertise on the research issue, non-probability sampling is not suitable in this case. Snowball sampling helps the researcher to find out interviewees from own network (Taylor et al.2015). Therefore in this case, snowball sampling will be used. The sample size of 5 interviewees will be selected. Trustworthiness of the participants in the interview will be analyzed to ensure reliability and validity of the data.
Quantitative research:
The quantitative research involves the steps like sampling, selecting the sample, collecting data from the sample, analysis and interpretation of the data. In this research random sampling will be used to choose the sample. Random sampling ensures that everyone in the sample has the same opportunity to get selected (Flick, 2015). As the research needs analysis of unbiased data, non-probability sampling will not be suitable in this case. The experts working in organizations with big data will be chosen through the sampling. A sample size of 60 will be used from the research. Survey is an effective technique to collect data from large samples. In this research also, questionnaire will be distributed among the participants and data will be collected from them. The collected data will be analyzed and interpreted using graphs. All the data will be collected over similar time period and using the similar way. It will make the data reliable. The survey questions will be designed in such way so that they can meet the research objectives. It will ensure validity of the collected data.
Research Limitations:
The current research deals with collection and analysis of data from small number of professionals who work in an organization that uses big data. However, in-depth analysis onn the research can be done if larger samples are selected. Moreover, the research needs to be completed within shorter time. Collection of larger amount of data would be possible if more time was available.
Time Schedule:
Week 1
Week 2
Week 3
Week 4
Week 5
Selection of topic
 Development of research aim, objective and questions                    
Literature review                    
Selection of samples for qualitative and quantitative data                    
Collection of qualitative data                    
Collection of quantitative data                     
 Data analysis and interpretation                    
                                                                                                  Table 1: Schedule of the research


Analysis on the current research indicates that the researcher is responsible for finding out, how the organizations are using big data in their decision making; the research also sheds light on the risks associated with extensive use of big data. In the research, both qualitative abd quantitative data will be collected from the primary research whereas the researcher will analyse the existing literature for the secondary research. Reliability and validity of the collected data will be maintained by collecting the data over the same period and analyzing trustworthiness   of   the   participants.


Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171-209.
Erl, T., Khattak, W., & Buhler, P. (2016). Big data fundamentals: concepts, drivers & techniques. Prentice Hall Press.
Flick, U. (2015). Introducing research methodology: A beginner's guide to doing a research project. Sage.
Jagadish, H. V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J. M., Ramakrishnan, R., & Shahabi, C. (2014). Big data and its technical challenges. Communications of the ACM, 57(7), 86-94.
John Walker, S. (2014). Big data: A revolution that will transform how we live, work, and think.
Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal, 79(1), 1-14.
Kumar, R. (2014). Research methodology: A step-by-step guide for beginners. Sage.
Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google Flu: traps in big data analysis. Science, 343(6176), 1203-1205.
Marz, N., & Warren, J. (2015). Big Data: Principles and best practices of scalable realtime data systems. Manning Publications Co..
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health information science and systems, 2(1), 3.
Taylor, S. J., Bogdan, R., & DeVault, M. (2015). Introduction to qualitative research methods: A guidebook and resource. John Wiley & Sons.
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107.

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