1. Introduction

1.1 Background of the Study

Big data is considered as the data collection in massive volume that grows exponentially with time. Telecommunications And Digital Government Regulatory Authority (TDRA) operates with the mission of becoming the leading company operating in the ICT industry (Migdal, 2021). The company aims to maintain healthy competition so that the interest of the subscribers can be protected.  The company has implemented big data in the business to achieve massive goals and increase the customer base. Dealing with the massive amount of data is the prime objective of TRDA to use big data.  The data management tools can be stored and processed the data efficiently.

1.2 Problem Statement

The most crucial problem in the company is the storage of massive data of the customers and other business operations. The lack of skilled people to handle big data is yet another problem that is faced by the company.  The company should implement big data to enhance the level of market research to provide better results to the company. Implementation of big data allows predictive evaluation. The issues faced to handle the massive data of the company aims to be resolved by the use of big data to support various EGovernment Applications.

1.3 Research Aim

The research aims to determine the role of big data to support Egovernment applications in the Telecommunications and Digital Government Regulatory Authority (TDRA).

1.4 Research Objectives

The objectives of the research include

  1. To identify the concept of big data.
  2. To identify the importance of Big Data to support egovernment applications.
  3. To ascertain the factors that affect the implementation of big data.
  4. To identify the limitations of big data to support EGovernance.
  5. To identify the role of big data to offer services to businesses and consumers.

1.5 Research Questions

The research question for the study include

  1. What role does big data play to support EGovernment applications in TDRA?
  2. What is the importance of big data to support egovernment applications?
  3. Which factors affect the process of big data implementation in the company?
  4. What is the role of big data to offer several services to the business and the consumers?

1.6 Significance of the Research

The research is essential to determine the role of big data in egovernment applications in TRDA. Big data is effective for companies to harness the information and utilise the same to recognise new opportunities (P.Maroufkhani, R.Wagner, Baroto, & M.Nourani, 2019). The research is vital to evaluate the benefits of big data that TRDA might achieve including cost and time effectiveness.

1.7 Research Structure

The dissertation will follow the structure that includes

  1. The first chapter has highlighted the introduction that includes the research aim, objectives, and research questions.
  2. The second chapter of the research evaluates research methodology that includes philosophy, strategy, approach and ethical considerations.
  3. The third chapter has highlighted the literature review.
  4. The fourth chapter includes the data collected using the interview method of primary data collection.
  5. The fifth chapter of the study highlights the results and evaluates the data collected.
  6. The sixth and seventh chapter includes recommendations and conclusions to implement big data successfully.

2. Research Methodology

2.1 Introduction

Research methodology refers to the particular techniques utilised to determine, select, process and evaluate the data collected regarding a particular topic. This module of the study aims to determine the philosophy that has been used in the research along with research approach, research design, research strategy, sampling technique.

2.2 Research Philosophy

Research Philosophy is considered as the belief of the researcher about gathering data to conduct the research, evaluated and utilised for the study. The positivism philosophy aims to be followed in the study to attain the results and increase reliability.  The positivism philosophy claims that the world is understandable in an objective manner (Marsonet, 2019). The “factual data” obtained by the researcher through observation is considered trustworthy. The role of the researcher is associated with data collection and later interpreting it in an objective manner. The use of the philosophy increases the reliability of the study.

2.3 Research Approach

Research approach refers to the plan to conduct research that includes assumptions about the data collection method, data analysis and results in interpretation. The approach of the study is ascertained based on the nature of the research problem. The inductive approach has been followed for the completion of the study. The inductive approach allows the researcher to begin with the observations and proceeds to the end of the research (MA.Eger & Hjerm, 2022). The inductive approach is effective to maintain flexibility in the study. The approach also allows attending closely to the context and provides support for the creation of new theories.

2.4 Research Design

Research design is the overall strategy chosen to integrate several elements of the study coherently. Research design allows the completion of the study logically to maintain effectiveness. Descriptive research design allows obtaining data systematically to describe different situations. The researcher can answer several types of questions associated with the research. The research design allows determining the nonquantified issues associated with the topic of the researcher (H.Atmowardoyo, 2018). The researcher possesses the chance to observe the phenomenon in a situation that occurs in a natural environment. The design allows the researcher to complete the study and increase the reliability of the same.

2.5 Strategy of the Study

Research strategy refers to the sequential plan to provide a direction to the efforts in research. The research strategy allows the conduction of research systematically to generate reliable outcomes. The strategy also allows the completion of the research within a deadline. Qualitative interviews have been conducted for the completion of the study. The project manager has been asked a few openended questions which they have answered. Openended questions have been beneficial for them to offer actual feelings and not select from the list provided.

2.6 Sampling Technique and Sample Size

The sampling technique is effective to collect data for the completion of the study successfully. Several sampling techniques are present but convenience sampling will be followed in the study. Convenience sampling allows the utilisation of the respondents that is convenient for the researcher (Zhang, Gong, Zhang, & Chen, 2019).  The sampling allows the researcher to ascertain the variables to gather data for the research. The perspective of the managers can be achieved by convenience sampling about the use of big data in egovernment applications in TRDA. The samples have been collected from one project manager who has handled projects in the company. The project manager has been approached and interviewed after taking consent. The manager has not been forced in any way.

2.7 Data Gathering Method

The data gathering method refers to the method of gathering data for the research from the relevant sources to achieve the answers to ?he research problem. Hypothesis testing is also possible using the data collection method. Data collection can be conducted by primary and secondary methods. The data for the research has been collected using the primary data collection method (Moser & I.Korstjens, 2018).  Primary data for the research has been gathered by interviewing the project manager in Telecommunications and Digital Government Regulatory Authority (TDRA). The answers have been collected and then evaluated for better understanding and achieving the results and increasing the reliability of the study.

2.8 Data Evaluation technique

The data evaluation method is yet another important aspect that allows the completion of the study. Data evaluation is done by qualitative and quantitative methods of data analysis. However, the interview data has been evaluated using qualitative data analysis methods. The qualitative data analysis allows identification and interpretation of the patterns from the textual data (Lowe, Norris, Farris, & Babbage, 2018). The qualitative data evaluation method allows the researcher to ascertain how the answer patterns have helped to provide answers to the research questions. Qualitative data analysis is effective to have an indepth analysis of the collected data. The evaluation methods also allow maintaining openness and encourage people to elaborate on the responses.

2.9 Ethical Considerations

Ethical considerations are crucial to complete the study following all the principles to reduce conflict and increase the trustworthiness of the same. Several ethical considerations aim to be followed in the study that includes proper protection of data collected. Protecting the data collected is vital to prevent malicious attacks on the data (M.Abrar & Sidik, 2019). However, the privacy of the participants will be maintained so that the study can be kept free from discrimination or the participant do not face any adverse effects. The study will remove the chance of discrimination so that the study is completed without any conflict. The participants have not been forced to maintain informed consent towards participation.

2.10 Summary

The methodologies mentioned have been used for the completion of the study and to achieve the outcomes. The positivism philosophy is effective for attaining the results from the factual data collected from the participants. The inductive research approach is beneficial for the researcher to start the study from the observations and proceed further. The ethics will be maintained in the research to mitigate conflicts before and after publishing the paper. The data for the research has been collected using the primary method of data collection. The project manager has been interviewed to collect the data.

3. Literature Review

3.1 Introduction

This section of the literature review consists of information related to the use of big data in supporting egovernance applications, the importance of big data in organizations, the factors related to G2B and G2B, etc. This paper critically analyses the importance of big data for egovernance, by reviewing various literature. Previous works related to the role of big data in egovernance have been used for this purpose. In this study, the company Telecommunications and Digital Government Regulatory Authority, also known as TDRA have been selected as an example of a company to analyze the role of big data in organizations.

3.2 Concept of Big Data

Big data is a type of data that has a massive volume, with its content increasing continuously with time. There has been a rise of the use of such data in several countries for their organizations. To store and apply big data, modern tools and strategies are necessary as the usual methods of storing data might not be of any use (Fredriksson, Mubarak, & Zhan, 2017). For managing data in different companies, various processes have started to be applied to analyze critical information and apply the knowledge in various fields. The major characteristics of big data are the volume of the content that is stored and applied in different situations, the variety of information stored in the systems, and the velocity of the generation of data (Koseleva & Ropaite, 2017).

Recently, big data has become highly important as a form of data that can be used to understand the business problems that an organization might encounter, and the risks they have to manage. Predicting challenges and developing solutions to resolve them have become essential components of the use of big data (Eastin, Brinson, Doorey, & Wilcox, 2016). A large portion of the goods and services provided by companies are developed with the help of big data, that is, organizations depend on big data to a great extent for creating value for their customers.

3.3 Importance of Big Data to support egovernment application

There has been an increasing need for making efficient use of the network that various organizations make to attain their business objectives. In the present digital era, technologies have evolved greatly and have started influencing the ways of marketing, providing customer services, interacting with coworkers, and so on (Grewal, Hulland, Kopalle, & Karahanna, 2020). The use of big data in egovernment operations must be carefully considered to increase the productivity and efficiency of the workforce in many organizations. The UAE is among the first few countries in which egovernment projects were developed (Marzooqi, Nuaimi, & Qirim, 2017). The Telecommunications and Digital Government Regulatory Authority (TDRA) has made efforts to apply big data for the organization and to enhance customer services by using advanced technologies for their benefit.

Expanding the services of egovernment in different sectors and providing a better experience for customers is necessary for the present age of competition among various companies. The use of big data for enhancing customer services is necessary for companies to maintain their competitive advantage in an industry (Anwar, Khan, & Shah, 2018). Costs related to traditional methods of data storage and application can also be reduced by using big data in different organizations. Big data can provide insight to both employees and customers about the effectiveness of the products and services and the necessary changes that can be made to satisfy the needs of the customers (Raguseo, 2018). For instance, the needs of the customers can be accurately identified regarding their problems in bill payment and other activities.

3.4 Factors Affecting the Implementation of big data in organizations

Several factors affect the implementation of big data in different organizations. One of the significant factors related to the use of big data is the knowledge and skills necessary for using advanced technology in different situations (Vassakis, Petrakis, & Kopanakis, 2018). Telecom companies can use such data to predict the behaviors of customers and the trends in the industry. TDRA uses advanced technologies to gain insight into consumer behaviors and develop strategies that can help them retain the interest of their customer base. There is a need for organizations to analyze the realtime data and incorporate the necessary changes into their strategies (Tantalaki, Souravlas, & Roumeliotis, 2020).

The use of advanced technology also depends on some psychological factors such as the ability to understand the importance of making changes in the system and adapting oneself in a changing work environment. Organizations need to hire skilled trainers who can provide the necessary training to the staff and make them accustomed to the new methods of using technology (Akhtar, Frynas, Mellahi, & Ullah, 2019). A skilled team of data analyzers can enable them to make critical business decisions, which in turn can lead to price optimization in the company. Telecom companies can thus reduce unnecessary expenditures, increase the sales of their services and gain a large consumer base. The purchasing behavior of customers is an important aspect of understanding the steps to be taken by a company to retain customer loyalty.

3.5 Limitations of Big Data in Supporting EGovernance

One of the limitations of using big data in supporting egovernance is the lack of a skilled workforce who can effectively apply big data for the companys benefit. The use of big data can lead to an increase in the efficiency of an organization however, a skilled workforce is also required for the company to ensure that big data can be applied in an appropriate way. It is also important to understand that the adoption of big data strategies for a company might not lead to a positive response from customers (Kumar & Zymbler, 2019). For instance, customers might require more time to become accustomed to the advanced technology and the complexities related to the use of big data by a company. Consumers need to have access to the big data services provided by a company to understand its importance and use. Individuals might also express distrust towards the new technologies applied by a company and continue with the traditional methods of using different products and services of the company (Lim, 2016). Some customers of telecom companies might prefer a facetoface mode of interaction rather than using online channels of communication. This can hinder the application of big data strategies in understanding the preferences and requirements of customers.

3.6 Satisfying Consumer Interests with the help of Big Data

Big data strategies are recently being used by many companies to increase their productivity and to deliver better outcomes for their customers. In the case of egovernance, big data can result in increased transparency and improve the quality of their services through a better understanding of customer needs and demands (Flyyerbom, 2016). The development of new projects can be done more swiftly with the help of big data, and better policies can also be made. TDRA uses big data to monitor the trends regarding sales and buying behaviors of consumers and uses the information to make the necessary changes in their organization.

The government can provide strategies to improve the security of the consumers and to understand the methods necessary for reducing fraud. In the case of the COVID19 pandemic, big data helped to a great extent to monitor the areas that faced a threat of the virus and take the necessary measures (Lin & Hou, 2020). Reducing the occurrences of fraud is an important aspect of the use of big data in egovernance. At the workplace, automatic face detection, as well as speech detection tools, can increase security for the company. Big data strategies can be applied to identify the anomalies in an organization that reduce its production and sales of goods and services.

3.7 Role of Big Data to Support G2B interactions

Big data plays an important role in the relationship between the government of a region and various organizations. The implementation of the strategies of big data is essential in successfully implementing the strategies of information systems in the case of G2B (Patel, Roy, Bhattacharyya, & Kim, 2017). The legal components of the sharing of critical data related to G2B systems can also be analyzed with the help of big data. The development of decisions related to company regulations, customer services, and the satisfaction of employees in an organization determines to a great extent how successfully the company might operate. Public sector organizations can obtain more information about the application of advanced tools and techniques for the implementation of government policies related to providing the necessary services to the citizens (Desouza & Jacob, 2017). Businesses can also provide better protection to their customers through a more systematic monitoring plan.

3.8 Summary

Big data has started gaining importance in many companies, and there is ample evidence to suggest that it is one of the tools for increasing the productivity of employees. The use of big data can be done in many organizations however, for the appropriate application of such advanced technology, skilled and knowledgeable employees are necessary. In this paper, the importance of big data in organizations, particularly telecom companies, showed the role it plays in increasing the security of data and identifying the needs of the consumers. Big data is highly important to understand the steps to be taken to tackle the challenges that a company might encounter in the future.

3.9 Literature Gap

There is a lack of data related to the application of big data in the political environment and how such strategies can effectively transform the situation in different organizations into a more digitally efficient environment. There is also a lack of information about how companies carry out the methods of adopting the big data strategies for attaining a better outcome. Information is necessary regarding the education and training of employees about the use of big data strategies for their companies and the socioeconomic factors that might play an important role in adopting these techniques.

4. Investigation

An interview was conducted with the project manager of the TDRA company. The participant was asked five questions related to the use of big data at their organization. The responses to the interview questions are as follows:

  1. Is big data important for attaining the company objectives?

Big data has immense value in any company. For our organization, it is necessary to develop better strategies for storing information and applying the same information in the future. The goals of TDRA can be attained on a timely basis by using the big data strategies.

In which areas of the company are big data mostly applied?

Big data is mostly used at our organization for storing the necessary information and applying the same information to facilitate the data analytics processes. Big data strategies are also applied for understanding the purchasing behaviors of consumers.

iii. What are the barriers to using big data at your organization?

The major barriers of big data use is the time and effort it takes to understand the application of big data and becoming accustomed to the new strategies. Technology is evolving, and people need to evolve their methods of working as well. This is a challenging task for many individuals.

How does big data strategy benefit the employeeconsumer relationships?

Big data helps in understanding the tastes and preferences of customers and identifying the areas which require improvement to fulfill the demands of the consumer base of our organization. Due to the swift delivery of services, the level of trust of the customers towards the company increases, and it helps in retaining customer loyalty.

What are your suggestions for companies using big data to abide by the government regulations and also increase the efficiency of the organizations?

Any company using big data techniques should clearly define their goals and use the big data strategies to follow the regulations. They need to maintain transparency in the use of big data services and monitor the feedback of the customers. Organizations also need to provide training to the employees to effectively use the big data strategies.

5. Results, Evaluation and Discussion

The participant stated that the implementation of big data is vital in TRDA to increase the value of the company. The implementation of big data is effective to store massive data for the company and preventing the risk of losing it. The data stored using big data can be reused and the company can generate the analytics (Ajah & Nweke, 2019). Big data allows the company to store massive amounts of data and gradually utilise new opportunities to bring growth and success for the company. Big data allows the company and the business to take efficient moves that allow the company to achieve better results from the business operations. The profit earned by TRDA can be enhanced and provide satisfaction to the customers.

The company can help the officials to make better decisions to improve the services offered to the customers and businesses. The company can reduce the cost of the processes of the business along with fraud detection. The productivity of the company can be enhanced using big data analytics. The employee’s performance is enhanced using big data in the company (Sahal, Breslin, & Ali, 2020). The barriers that the company faced by the implementation of big data includes time and cost. The cost involved in the implementation of big data is massive along with time. The company should implement several new strategies to achieve the results of big data. Big data is effective to maintain a proper relationship between the employee and the customers. The changing trends of the customer can be identified using big data.

According to Boubiche et al. 2018, the emergence of technology has allowed the integration of big data in various processes to bring innovation. The combination of big data of Wireless sensor networks involves several challenges. Data aggregation is emerging fast in the research area. Big data represents a proper solution for the collection, evaluation, storing of data along with transmitting data (BOUBICHE, BOUBICHE, BILAMI, & CRUZ, 2018). Big data is beneficial for the company to increase sales and increase loyalty among the customers. The enhancement of the customer base is effective for the increase of the company. As per Vassakis et al. 2018, big data impose a great impact on several businesses in the recent time of the industrial revolution of 4.0. The hype of big data is massive among companies across the world. However, data science refers to the process of gathering fundamental principles that allow the promotion of information from the data (K.Vassakis, Petrakis, & Kopanakis, 2018).

6. Recommendations

After the completion of the study, it is recommended that the company should offer proper training to the employees to deal with the operations of big data. The challenges faced by the company should be mitigated in order to achieve better results. The resistance that is faced by the company to implement the change and implement big data in business should be mitigated and the employees should work collaboratively in a team to achieve better results of the technology. The issues faced in TRDA have been identified by the answers offered by the project manager. The lack of skilled employees should be reduced so that the company can implement the changes successfully. However, mitigation of the issues will allow the company to implement the changes fast. However, the researcher should use secondary resources for the completion of the study to increase the reliability factor. More participants should be interviewed to get additional data about the implementation of big data in TRDA.

7. Conclusion

With the rise of advanced technology, various organizations have started using big data strategies to attain the company goals and to increase the efficiency of their workforce. Big data is highly useful in strengthening the security of companies and protecting sensitive information related to several departments. The data obtained from the interview indicates that organizations need to clearly define their goals before implementing the big data techniques, and they also need to train the workforce to develop the necessary skills for using big data. The appropriate use of big data also strengthens the relations between employees and customers, as the feedback obtained from customers can help the company to make the necessary changes in their services. There are however, some barriers to the use of big data, such as the distrust customers feel towards some organizations and their preference for more conventional techniques of customer services.

 

References

Ajah, I., & Nweke, H. (2019). Big data and business analytics: Trends, platforms, success factors and applications. Big Data and Cognitive Computing, 3(2), 32.

Akhtar, P., Frynas, J., Mellahi, K., & Ullah, S. (2019). Big datasavvy teams’ skills, big datadriven actions and business performance. British Journal of Management, 30(2), 252271.

Anwar, M., Khan, S., & Shah, S. (2018). Big Data Capabilities and Firms Performance:. Journal of Information and Knowledge Management, 17(04).

BOUBICHE, S., BOUBICHE, D., BILAMI, A., & CRUZ, H. (2018). Big Data Challenges and Data Aggregation. IEEE access, 2055820571.

Desouza, K., & Jacob, B. (2017). Big Data in the Public Sector: Lessons for Practitioners and Scholars. Administration and Society, 49(7), 10431064.

Eastin, M., Brinson, N., Doorey, A., & Wilcox, G. (2016). Living in a big data world: Predicting mobile commerce activity through privacy concerns. Computers in Human Behavior, 58, 214220.

Flyyerbom, M. (2016). Transparency: Mediation and the Management of Visibilities. International Journal of Communication, 10, 110122.

Fredriksson, C., Mubarak, F., & Zhan, M. (2017). Big Data in the Public Sector: A Systematic Literature Review. Scandinavian Journal of Public Administration 21(3), 3962.

Grewal, D., Hulland, J., Kopalle, P., & Karahanna, E. (2020). The future of technology and marketing: a multidisciplinary perspective. Journal of the Academy of Marketing Science, 48(1), 18.

H.Atmowardoyo. (2018). Research Methods in TEFL Studies: Descriptive Research, case study, error analysis, and R&D. Journal of Language Teaching and Research, 9(1), 197204.

K.Vassakis, Petrakis, E., & Kopanakis, I. (2018). Big Data Analytics: Applications,. Springer, Cham, 320.

Koseleva, N., & Ropaite, G. (2017). Big data in building energy efficiency: understanding of big data and main challenges. Procedia Engineering, 172, 544549.

Kumar, S., & Zymbler, M. (2019). A machine learning approach to analyze customer satisfaction from airline tweets. Journal of Big Data, 6(1), 116.

Lim, M. (2016). Customer relationship management with big data enabled in banking sector. Journal of Scientific Research and Development, 3(4), 17.

Lin, L., & Hou, Z. (2020). Combat COVID19 with artificial intelligence and big data. Journal of Travel Medicine, 27(5), 18.

Lowe, A., Norris, A., Farris, A. J., & Babbage, D. R. (2018). Quantifying thematic saturation in qualitative data analysis. Field methods, 30(3), 191207.

M.Abrar, & Sidik, E. (2019). Analyzing ethical considerations and research methods in children research. Journal of Education and Learning (EduLearn), 13(2), 184193.

MA.Eger, & Hjerm, M. (2022). Identifying varieties of nationalism: A critique of a purely inductive approach. Nations and Nationalism, 28(1), 341352.

Marsonet, M. (2019). Philosophy and Logical Positivism. Academicus International Scientific Journal, 10(19), 3236.

Marzooqi, S., Nuaimi, E., & Qirim, N. (2017). EGovernance (G2C) in the Public Sector: Citizens Acceptance to EGovernment Systems Dubai’s Case. Proceedings of the Second International Conference on Internet of Things, Data and Cloud Computing, (pp. 111). Dubai.

Migdal, M. (2021, 8 23). howbigdataempowersorganizationstoworksmarternotharder. Retrieved from www.forbes.com: https://www.forbes.com/sites/forbestechcouncil/2021/08/23/howbigdataempowersorganizationstoworksmarternotharder/

Moser, A., & I.Korstjens. (2018). Series: Practical guidance to qualitative research. Part 3: Sampling, data collection and analysis. European journal of general practice, 24(1), 918.

P.Maroufkhani, R.Wagner, W. I., Baroto, M., & M.Nourani. (2019). Big data analytics and firm performance: A systematic review. Information, 10(7), 226.

Patel, B., Roy, S., Bhattacharyya, D., & Kim, T. (2017). Necessity of Big Data and Analytics for Good egovernance. International Journal of Grid and Distributed Computing, 10(8), 1120.

Raguseo, E. (2018). Big data technologies: An empirical investigation on their adoption, benefits and risks for companies . International Journal of Information Management, 38(1), 187195.

Sahal, R., Breslin, J., & Ali, M. (2020). Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case. Journal of manufacturing systems, 54, 138151.

Tantalaki, N., Souravlas, S., & Roumeliotis, M. (2020). A review on Big Data realtime stream processing and its scheduling techniques. International Journal of Parallel, Emergent and Distributed Systems, 35(5), 571601.

Vassakis, K., Petrakis, E., & Kopanakis, I. (2018). Big Data Analytics: Applications, Prospects and Challenges. Mobile Big Data, 320.

Zhang, H., Gong, Q., Zhang, H., & Chen, C. (2019). Combining the Biased and Unbiased Sampling Strategy into. Journal of Computational Chemistry, 40(20), 18061815.

 

 

Related Post

Integrated Professional Skills in Digital Age

In terms of ICT, any digital technology that facilitates the acquisition and use of information by i

Introduction of Primark

Retailer Primark specializes on apparel, accessories, and footwear, and is one of the largest in the

Goals have a clearly established goal for the company

a healthier lifestyle has been developing, including customer goods and services. Governments and NG

Recent posts

Chat With Us +44-20-4520-0757
LET'S GET STARTED