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BUSN20019 Professional Project

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Chapter One : Introduction To The Study

Introduction 

This project aims to investigate the impacts of the Big Data to the organizations. The new information technologies such as the Big Data is one of the technologies that could be disruptive, hence change on how the organization undertake and performs their activities (Agrawal, Das & El Abbadi, 2011). Currently, most of the researches which have been undertaken on the topic of the Big Data have been theoretical; there has been hardly any empirical study on the topic. In this introduction chapter it will fulfil various goals which are examined as follows in regards to this research study.

Background to the Study

Why should the academics as well as the practitioners be interested in undertaking about the impacts of the big data? The simple answer to this question would be Big Data concept has the potential of transforming the entire organization process and this paper would play main role to conceptualize on this approach (Braganza, Brooks, Nepelski, Ali & Moro, 2017). In this research study, it will examine the impact of the big data on the organizations. The extant literature will highlights the big data as the next big things in the innovation.

Statement of the Problem

The information technology has brought forth to the importance of the improvements in the operations of the business (George, Haas & Pentland, 2014). The big data is believed to offer both a challenge as well as opportunity for the businesses. There has gaps in the research of the impact of the big data in organization and the existing research is all theoretical, this research will address this and provide an empirical study. It will highlight how the big data has impacted on the businesses.

Purpose of the Study.

The main objective of this study is to investigate the impacts of the big data on the organizations.

Objectives of the Study

  • To investigate the impact of the big data to the organization process?
  • To find out how big data analytics could impact the performance of an organization?

Research Questions

It is important to possess a definite objective as to the reasons this study would be carried out based on the study issue description provided above, it really is clear the aim of the study would be to offer empirical attestation regarding the influences of big data to the businesses (Hussain & Roy, 2016).

  • What are the potential impact of the big data analytic on the organization?
  • How does the fit between data, data analytic tools, and the task influence the performance antecedents of an organization?

Significance of the Study

This research is aimed to be of the scientific in addition to practical significance. Even though the data has become the modern form for the capital in addition to the source for the competitive advantage, the principle off the big data is a whole new emerging concept for many individuals to gain. This research will fill the gap, by providing the empirical study which not many research has been done

Limitation of the Study

The researcher may encounter constraints from the respondents where some of them could hold the information due to the confidentiality aspects in the workplace, but as a researcher I will be able to convenience them that the information will be confidential and it will be utilized only for the purpose of academic.

Scope of the Study

The scope of the study will be within the setup of the banking institution with a target population of approximately seventy eight employees and a sample size of thirty nine in the organization. The research is aimed at addressing the impacts of the big data on organizations.

Chapter Two : Literature Review

Introduction

This chapter focuses on the literature review from the various sources which includes the books, magazines, journals, periodical as well as the websites. It entails the review of the analytical literature, summary of the gaps to be filled.

In this chapter it will describe various concepts particularly to the existing literature and providing the insights into the relevant existing studies especially in the fields of IT, big data and the management control systems (Wang, Kung & Byrd, 2016). In this chapter it discusses the theoretical basis for the study.

Information Technology

The information technology has become omnipresent and it is changing each aspect of individual’s lives. Additionally, IT has become an integral part especially in the modern organizations and it has changed on the way many of the organizations processes and operations (Chen, Chiang & Storey, 2012). Therefore, this is a consensus that the information technology has a great impact towards the productivity of the organizations.
In the recent decades, the information technology has advanced at a fast pace, and what was hard to imagine has now become part of every individual live (Chen, Chiang & Storey, 2012). Among others, is the online shopping, digital marketing, cloud computing, digital communication along with social networking such as Facebook (Raghupathi & Raghupathi, 2014). To make usage of the available data many organization have acquired various kinds of IT systems and programs.

Big Data

The data has been a brick to which any organization could blossom. The big data and the analytics offers various benefits. It has helped in the creation of the various kind of the values, transparency, expose of the variation as well as the improvement of performance (Russom, 2011). Moreover, it has been found to support the human decision making with the use of the automated algorithms and innovating new business models, services and the products. It is evident that with the rapid development of the information technologies for example the cloud computing, the mobile internet as well as the internet of things there has been all kind of data which has been generated and accumulated (Ross, Ressia & Sander, 2017). Therefore, this could present opportunities and unprecedented challenges which could be associated to the available data.

Definitions and Features of the Big Data.

The big data has been a concept which has been leading the world and it has been taken as storm. Some of the research regards as a huge set of the data which is possible to analyze by hand or even through the traditional methods, for example the spreadsheet. A portion of the scholar thinks that it is a set of the data asset to the organization, which has many values and there is a lot of benefits which could be generated from using it. It has been changing on the way the business are performing (Ross, Ressia & Sander, 2017). Many organization are investing in it to derive the value from their data which have an advantage over the rival firms. There would be a performance gap which will still grow as more relevant data has been generated, emergence of new technologies as well as the digital channels which provider a better acquisition and a delivery mechanism. The big data has a datasets which has sizes which go beyond the abilities of the traditional as well as the common tools in the business to capture, manage as well as store the data processes.

Big Data and its Significance 

As more volume of data keeps to grow exponentially, organization have turned to the huge amounts of the data as well as analytics to form a strategy and decision making (Gunasekaran, Papadopoulos, Dubey, Wamba, Childe, Hazen and Akter, 2017). The Big Data technologies are impacting the businesses positively as they are providing the possibility of storing and analyzing the huge amount of data to unseen patterns, sentiments as well as the customer intelligence (Gandomi & Haider, 2015). In today world which has been fast changing, it is vital for the businesses to stay ahead of their peer and at the same time react faster to the changes in the market, act on the present and future shortcomings. Big data is becoming a significant assets to many organization in making of decision (Wang, White & Chen, 2015). The Big Data technologies analyze huge amount data from various sources to offer opportunity to deliver benefits to organizations. According to Wang, White & Chen, (2015) one of the significant benefit of the Big Data is the ability of making a wider availability, transparency as well as visibility to the information to the decision makers in the business. Big data and analytics offers  manager ability to measure and be able to know more about their organization, customers and  market, and be in a position of translating the knowledge (Tesfaye, 2017).

Challenges of Big Data 

While there are many benefits which Big Data provides there are also some challenges which exists and should be addressed to realize the potential of Big Data (Ross, Ressia & Sander, 2017).  Some of the challenges are function of the features of Big Data, some are associated to the existing analysis methods, models and others are through limitation to current data processing system. For the organization to reap benefits of Big Data and analytics and become fully, Big Data empowered organization, they need to overcome some of these problems and challenges. According to Chen, Chiang and Storey, (2012) one of the major problem of Big Data is the high cost of the infrastructure. Moreover, based on Diesner, (2015), argued that based on lifecycle of the data the challenges could be grouped as data processes and the management. Additionally according to Diesner, (2015) also categorized the challenges of the Big Data as the managerial as well as technological challenges. Moreover, the process challenges which have been identified by Gandomi & Haider, 2015), have been classified as the technological challenge. This thus, shows that most of the scholars agree to the fact that these challenges of the Big Data are major managerial as well as technological. 

Chapter Three : Research Design And Methodology

Introduction

The research was done quantitative. This is because some numerical data was collected in order to explain phenomena and frequencies sought to enable explanation of meanings, it entails the research design that was utilize in the research and data analysis.

Research Design

Descriptive investigations are created to obtain pertinent and precise data concerning the present level of phenomena. Descriptive design is furthermore aimed to acquire information which is often analyzed, patterns extracted and compression created (Havakhor, 2016). The researcher utilize questionnaire which consisted of organized and unstructured questions.

Target Population

The population is an entire set of people who have commonly observed characteristics. The target population interest in this study was compromised of employees working in the financial organization.

Table : Target Population

Categories

Target population

Management

5

Supervisory staff/

12

Operational staff

33

Total

50

Sample and Sampling Technique

The researcher used random sampling techniques that assist the researcher to achieve representation that is desired to various sub-groups in population. Respondents were drawn from each group randomly to ensure that all the departmental associations were represented in the sample population (Diesner, 2015).

Table : Sample Size

Categories

Target population

Management

1

Supervisory staff

3

Operational staff

6

Total

10

Source: Author (2017)

Data Collection Instruments 

The researcher used both primary and secondary data, primary data collected through open-ended and close-ended questionnaires, which present the banking institution, defects noted corrected. The corrections were included in the questionnaire to enable collection of requisite data from the employees; the researcher administered the final questionnaire to the respondent, and because it was appropriate to administer the primary data was supplemented by secondary data from the available literature relating to the study area.

Validity and Reliability   

Validity is the level which analyzes activities what seemed to be designed to measure. The validity of instruments is the level of precisely how correctly the information acquired in the study signify the adjustable. To check for information authenticity within this research the researcher relied on the professional judgments who commented on simplicity of the tool and content coverage.

Data Collection Procedure

The researcher communicated to the manager to seek authority to access information on technology they have implemented. After the permission was granted the questionnaires were hand delivered and they were distributed to other respondents through the assistance of a research assistant, after which the researcher collected the feedback for analysis.

Data Analysis and Presentation 

The researcher analyzed the information that was obtained from the research questions. The data was analyzed using qualitative that is mainly descriptive information and quantitative that is numeric information procedures (Moniruzzaman & Hossain, 2013). An excel computer packages was used to analyze the data. This data was presented in frequency tables.

Chapter Four : Data Analysis And Presentation

Introduction

This chapter presents and discusses the analysis of the presentation and interpretation of the researcher’s findings to relate to the research objectives. These explained the procedures and investigate effects of Big data on the performance of the banking institution.

Response Data

Table : Response Rate

Response

Frequency

Percentage

Management staff

4

40%

Operational staff

6

60%

Total

10

100%

From the analysis in Table 4.1 the researcher found out that the management staff in the organization to be 40% while that of the operational staff was 60%.

Table : Effects of big data on competitiveness of organization

Response

Frequency

Percentage

Very large extent

2

20%

Large extent

3

30%

Small extent

1

10%

Very small extent

4

40%

Total 

10

100%

Source: Author (2017)

From Table 4.2, the data shows that 20% of the respondents indicated that organizational competiveness could affect the performance in a large extent, 30% of the respondents indicated that competitiveness is affected at a large extent, 10% of the respondents indicated its on a small extent while 10% indicated that organizational competitivenessis affected on a very small extent.

Table : Management effort to enhance the Big Data

Response

Frequency

Percentage

Yes

3

75%

No

1

25%

Total

4

100%

Source: Author (2017)

From Table 4.3, the data showed that 75% of the respondents indicated that the management is doing enough a lot to enhance the Big Data services While 25% of the respondents indicated that they are the management is not doing enough anything to enhance the improvement of Big Data technology.

Table 4.4: Focus of Big Data technology to enhance organization performance.

Response

Frequency

Percentage

Customer care services

1

25%

Media advertisement

0

0%

Publishing of organizations journals

3

75%

Quality products and services

0

0%

TOTAL

4

100%

Source: Author (2017)

From Table 4.4 , the data showed that 25% of the respondents indicated that the organization use customer care data to improve their service delivery especially in communication and marketing tool to ensure customer loyalty while 75% of the respondents indicated that they use the data from the web, journals as well reports.

Table : How competition affects performance

Response

Frequency(outcome)

Percentage

Very large extent

3

75%

Large extent

1

25%

Small extent

0

0%

Very small extent

0

0%

Total

4

100%

Source: Author (2017)

From Table 4.5, data showed that 75% of the respondents indicated that competition can affect the business to a very large extent while 25% of the respondent said it only affect on a large extent.

Chapter Five : Summary, Conclusion And Recommendation

Introduction

The chapter will summarize the research findings as along with providing an analysis of the research questions. In the chapter it will provide conclusions as well as the recommendations. This research will provide an impact of the Big Data to the organization.

Summary of the Findings

The summary will focus on the answers to the research questions which have been raised in the research.

Potential Impact of the Big Data Analytic on the Organization.

One of the major impact of the Big Data has been on the organizational change or even the transformation which is necessary in order to support as well as exploit the big data opportunity. There would be a need of a new role which is necessary for the creation of the opportunities as well as the anxiety for the people and the organizations (Kwon, Lee & Shin, 2014). The aspect of the BI and the data science have various roles and they require different skills and approaches. Many organization are taking the advantage of the Big Data technologies to be able to collect, interpret as well as capitalize on the vast amount of the new data. According to the review it has highlighted that the Big Data would be harnessed in order to understand the consumers, improvement of the healthcare as well as cut the organization operational costs.

How the data and data analytic enhance the performance of the organization.

The data analytic are the business intelligence technologies which are grounded in the data mining as well as the statistical analysis. This tool relies on the mature commercial technology of the relational DBMS, the data warehouse as well as OLAP (LaValle, Lesser, Shockley, Hopkins & Kruschwitz, 2011). This technology has been a revolutionary new platform which the large scale parallel data could be accessed (McAfee, Brynjolfsson & Davenport, 2012). This tool has been used by many organization to study on the large volume of the data which patterns could be drawn that could help the organization to make decision and to offer it competitive advantage of the other firms. Additionally, some of the technique has been used to study the dynamic nature of the social network (Lee, 2017).

Conclusion 

Big Data is changing to the way organizations performs. It has led to more volume as well as higher variety along with the veracity of the data. However, the business have encountered numerous obstacles. However, although there have been urgent need for the business to respond to the unstable environment of the business timely, the business need to have numerous capabilities and the capitals. Without overcoming the aspects of the infrastructure, the technological as well as the managerial challenges with the Big Data technologies, the business will not realize these benefits. Therefore, to enable an organization which is Big Data enabled the changes in the business are inevitable. This thus means that the Big Data could impact the organizations in numerous ways especially to the way they operate and control their processes.

Recommendations 

For the organization to fully make use of the Big Data technology they need to first identify on their market niche so that they can be able to know the kind of technology they want. Moreover, they need to invest on the various infrastructure which should be dependent on what they are willing to spend as a business. Organization should not spend on what they are not able to afford to loss since the cost of the operation cost increase and lender them bankrupt. Additionally, there is need to look at the current trends in the market in regards to the technologies and adopt the one most useful to the business.

Suggestions for the Further Research

In this research the findings seems to be conclusive, but there are areas which needs further investigation such as the value of the big data analytics tools as well as the developing theories to have a better understanding the circumstances are under which the information technology  resource could be translated into the improvement of the performances.

Limitations

Notwithstanding the contributions of this research, there are various limitations. In this research it has been undertaken in one industry which is financial sector there is need for further research to other industries since it involved a lot of generalization of the variables. Another constraint was funding, this made the research not to be extensive since the research focused only few institution. A large sample size would have provided a more comprehensive data analysis.

References

Agrawal, D., Das, S., & El Abbadi, A. (2011, March). Big data and cloud computing: current state and future opportunities. In Proceedings of the 14th International Conference on Extending Database Technology (pp. 530-533). ACM.

Braganza, A., Brooks, L., Nepelski, D., Ali, M., & Moro, R. (2017). Resource management in big data initiatives: Processes and dynamic capabilities. Journal of Business Research, 70, pp.328-337.

Chen, H., Chiang, R.H., & Storey, V.C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 36(4).

Diesner, J. (2015). Small decisions with big impact on data analytics. Big Data & Society, 2(2), p.2053951715617185.

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.

George, G., Haas, M. R., & Pentland, A. (2014). Big data and management. Academy of Management Journal, 57(2), 321-326.

Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. J., Hazen, B., & Akter, S. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308-317.

Havakhor, T. (2016). Big Data and Organizational Impacts: A Study of Big Data Ventures  (Doctoral dissertation, University of Arkansas).

Hussain, A., & Roy, A. (2016). The emerging era of Big Data Analytics.

Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management, 34(3), 387-394.

LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT sloan management review, 52(2), 21.

Lee, I. (2017). Big data: Dimensions, evolution, impacts, and challenges. Business Horizons, 60(3), 293-303.

McAfee, A., Brynjolfsson, E., & Davenport, T. H. (2012). Big data: the management revolution. Harvard business review, 90(10), 60-68.

Moniruzzaman, A. B. M., & Hossain, S. A. (2013). Nosql database: New era of databases for big data analytics-classification, characteristics and comparison. arXiv preprint arXiv:1307.0191.

Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health information science and systems, 2(1), 3.

Ross, P. K., Ressia, S., & Sander, E. J. (2017). Work in the 21st Century: How Do I Log on?.

Russom, P. (2011). Big data analytics. TDWI best practices report, fourth quarter, 19, 40.

Tesfaye, B. (2017). What is the influence or Big Data and Analytics on Management Control System.

Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77-84.

Wang, X., White, L., & Chen, X. (2015). Big data research for the knowledge economy: past, present, and future. Industrial Management & Data Systems, 115(9).

Wang, Y., Kung, L., & Byrd, T. A. (2016). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change.

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