BMG843 (CRN: 46485) Department of Management, Leadership & Marketing

1.0 Introduction

Big data has been a key innovation for organisations to improve their business activities as well as stay ahead of the market competition. It helps businesses to remain competitive within the changing business environment due to digital transformation. A case of British Telecommunication was utilised in this research to critically assess the impacts of big data within digital transformation effectively. The first chapter of this study has focused on emphasising the background context of the research topic along with establishing certain research aim, objectives to reach a sound conclusion.

1.1 Research Background

Big data refers to the crucial component of innovation which enables an organisation to make effective data-driven decisions as well as optimise their business operations in an efficient way. As per the views of Miklosik and Evans (2020), adopting big data enables organisations to transform themselves into efficient data-driven companies. It is accomplished through allowing them for innovation in marketing activities as per large data base both unstructured and structured. Considering the technological perspective in digital transformation, companies can also manage and organise big data accurately with higher storage capacity. On other hand, British Telecommunication has defined that the aim of big data is being capable of assembling, interrogating information across each aspect of their business to understand huge scale context and offer better customised experiences to the consumers (Bavasso, 2021). Hence, the application and impacts of big data in digital transformation are evident to be positive in reference to British Telecommunication as well.

Figure 1.1: Share of big data market revenue worldwide till 2027, by major segment

(Source: Taylor, 2023)

Figure 1.1 illustrates the overall share of big data market revenues across the world as of 2027, divided as per major segments. The above statistics highlighted that the services, hardware and software in the big data market are estimated to reach a revenue of around 32 %, 23 % and 45 % respectively by the end of 2027 (Taylor, 2023). Hence, this indicates that the software area in big data holds comparatively more potential than others which can help British Telecommunication in improving its marketing, and. customer services.

1.2 Problem statement

Despite having significant potential, big data often leads organisations to face challenges like data privacy issues, data quality management, integration complications along with poor comprehensive framework to leverage big data during digital transformation.

1.3 Aim

This research aims to assess the ways in which big data impacts the digital transformation as well as fosters business innovation through insights of data analytics within British Telecommunication.

1.4 Objectives

Objective	Specific	Measurable	Achievable	Relevant	Time
To recognise the key role of big data within the digital transformation	Determining the overall role and impacts of big data in business digital transformation.	Evaluation of transformation outcome	Analysis in relation to the operation sector	Integrates two different digital trends	3 months
To analyse the major factors which are leveraged within the big data for innovation in British telecommunication	Key factors which are associated with British Telecommunication	Quality environment matrix	Obtaining industry reviews	A reference to British Telecommunication	4 months
To recognise the role of artificial intelligence (AI) for gaining success in big data for business innovation within British telecommunication	Contributions of AI in British Telecommunication	AI-based results	AI-integrated business outcomes	Efficiently relates to Telecom sector evaluation	3 months
To examine the key challenges in terms of adopting the big data for digital transformation in British Telecommunication.	Efficiently recognise the challenges in implementing big data in data analytics	Evaluation of the key barriers	Through survey process	Addressing British Telecommunication	3 months

Table 1.1: Research objectives using SMART framework

(Source: Created by author)

1.5 Research Questions

What role does big data play in businesses’ digital transformation?
What are the major factors which are leveraged within big data for innovation purposes in British Telecommunication?
How does AI aid in the successful adoption of big data for British Telecommunication’s innovation?
What are some key challenges that hinder the adoption of big data in digital transformation processes?

1.6 Research Rationale

Figure 1.2: Big data security and challenges

(Source: Influenced by Yang et al., 2020)

Considering the impacts of big data in digital transformation, some key identified issues in this research include data privacy issues, data quality management, integration complications along with poor comprehensive framework. As per the views of Yang et al., (2020), data privacy issues might directly impact on the behaviour of consumers as well as their demand, purchasing patterns. This is caused by the customers through shifting their interests towards other companies. An effective measure is needed by British Telecommunication to ensure a protected data privacy system further. On other hand, the value of Big data analytics marketplaces is estimated to reach around US$ 655 billion by the end of 2029 (Taylor, 2023). This indicates that companies need to ensure protected data services, data integrated along with a robust framework to reduce the aforementioned challenges in implementation of bigdata. Considering the severe impacts due to declined consumers, low profits, these issues are justified in the research. Recently, the potential of hacking, cyber attacks have increased the risk for companies to lose confidential customer data for which the research issues are relevant. Hence, this research tends to shed light on analysing every issue with different observations along with utilising effective theories to reduce them for British Telecommunications further.

1.7 Research Significance

This research has been significant considering its focus on analysing the key impacts of big data on British Telecommunication’s overall digital transformation. It helped in elaborating the knowledge areas and sharpens the critical thinking of readers about big data implementation for company innovation in a reference to British Telecommunication. Hence, considering the expected increasing market value and success rate of big data, it can significantly help businesses to succeed through digital transformation.

1.8 Structure of dissertation

Figure 1.3: Structure of dissertation

(Source: Created by author)

1.9 Summary

Concluding the first chapter, it can be stated that it has covered some of the crucial aspects of the research. The rationale of analysing the impacts of big data in digital transformation with a reference to the case study of British telecommunication was accomplished. Establishing the dissertation structure, the chapter covered another important area of this research through specifying the key stages of the research.

CHAPTER 2: LITERATURE REVIEW

2.0 Introduction

Big data analytics helps assessment of customer behaviour patterns while improving digital transformation processes and improvement of innovation activities. This literature review chapter has focused on assessing the importance in understanding the impact of big data in order to bring in digital transformation within telecommunication companies. This literature review chapter would delve into understanding the significance of big data analytics and its impact on the telecom industry. The chapter would also identify the factors necessitating digital transformation, challenges and existing strategies to embrace digital transformation.

2.1 Conceptual Framework

Figure 2.1: Conceptual Framework

(Source: Created by Author)

2.2 Significance of Big Data Analytics in Telecom Industry

Big data refers to a set of large and complex data sets or collections that can be structured, unstructured or semi structured that rapidly grows over time. According to Naqvi et al., (2021), big data involves statistically the largest set of data that are complex that are processed through software integration to discover data points. These data points allow organisations to make effective decisions that help in paving ways towards their desired business objectives. Big data in the context of the telecommunication industry allows companies to gather large sums of data enabling them to collect user data such as demographics, location, and preferences. Analysing data through big data allows telecom organisations to analyse customer behaviour patterns. As a result, companies become able to shape their products or services to satisfy the needs of their target customers. Regarding this, Wassouf et al., (2020) opined that big data analytics is crucial for telecom companies to optimise network usage, services to increase customer satisfaction with a service provider. In this context, the rapid rise of smartphones usages and other connected mobile devices has influenced the requirement of big data analytics to understand and address needs of customers. Therefore, big data can be comprehended to revolutionise the overall operational system and success of a telecom company in its strategic business activities.

The use of big data in the telecom industry assists in spanning network operations, developing products and services. Using big data analytics, telecom companies become able to increase their value chain and relieve congestion in network operations. The market of big data internationally in the telecom industry has stood to be $162.6 billion as per 2022 and projected growth is $273.4 billion by 2026 (Lisowski, 2022). As the telecom industry generates huge amounts of data, big data solutions have high significance in finding resolution and digitally transforming customer centric services. As defined by Lisowski (2022), big data in the telecom industry is highly significant in analysing data regarding call records, server logs, social network, billings and others. Big data analysis helps in exploiting large amounts of data and finding patterns of customer, network, and other usages to make actionable decisions. Thus, big data has been assisting in digital transformation management for customer experience improvement activities for the telecom industry.

Big data analytics helps in deriving insights from customers and reviewing their interactions with the telecom services. As opined by Rejebet al., (2020), big data analytics is utilised to categorise customers into micro segments and provide relevant products and services to increase customer adaptation. Telecom companies also target its market campaigns based on their usage of international calling data patterns and frequencies using big data analytics. On the other hand, Keshavarz et al., (2021) stated that Big Data’s key implementation is to optimise huge amounts of capital and operating expenses in network optimization in telecom services. Data such as data usage, network logs and mobility patterns increases complexities of the overall process of digital transformation to manage operation of the telecom industry. In the context of network congestion, big data analytics also makes the digital transformation rigid for organisational adaptiveness (Xing et al., 2023). Therefore, big data integration in the telecom industry requires rapid and real time development to harness the benefits of big data.

Big data helps to improve organisational performance and reduce expenses in overall operations. As asserted by Alam et al., (2020), big data’s analytics’ practical insights in the telecom industry allows it to monitor users’ activities throughout the network to allocate resources accordingly. In addition, real time data analytics allows it to schedule its data maintenance and updates to provide customers with better services. However, Manyaga and Hacioglu (2022) argued that maintaining data privacy in customer data analysis is a significant concern that is to be adhered to by telecom operators. Performing data analytics requires taking legitimate steps in protecting user rights and data to ensure customer trust and loyalty towards the company. Thus, in the digital transformation of a telecom organisation can be achieved through a safe practice of data analytics for proper utilisation of big data.

2.3 Factors Driving Big Data Analysis In Telecom Industry

Figure 2.2: Factors Driving Big Data Analysis in Telecom Industry

(Source: Created by Author)

2.3.1 Customer Experience Personalisation

Increasing personalisation in rapidly evolving businesses has become highly crucial factor to customise individual needs of customers. As analysed by Ying et al., (2021), big data analytics helps the telecom industry in enhancing the customer experiences through data analytics on customer behaviours. Data analytics allows businesses to improvise its lifetime value perceived by customers. As a result, businesses become able to sustain their client expectations and ultimately increase customer loyalty towards the telecom operator. Big data analytics also allows companies to perform large scale analysis to understand the needs of their consumers. As analysed by Hossain et al., (2020), big data analytics involves analysis of insights on customer preferences behaviours and expectations. Based on the outcome of data analytics, companies become able to leverage customised customer service due to digital transformation customised connectivity plans to contribute to increased customer retention. However, Purao et al., (2021) argued that analysing call records, browning patterns and other personal data can hamper the privacy of customers. A decreased trust in a company’s privacy policies can reduce a company’s customer churning and acquisition rates. In this regard, telecom service providers aiming to digitally transform needs to maintain legitimacy in analysing customer data ethically. Furthermore, companies often perform customer segmentation to better personalise services through understanding behavioural patterns through data analytics. Thus, data analytics is a key driver in improving personalisation of customer experiences by catering to individual needs.

2.3.2 Collaboration between companies and development of Telecom industry

Development of telecom products and services are often done through a collaborative approach of telecom operators in catering to changing needs of customers. The massive volume of data generated daily in the telecom industry requires a comprehensive data analysis of network operations, customer interactions, and trends in the market. As asserted by Tan et al., (2020), a collaborative approach of telecom service providers assists in sharing and facilitating data patterns among industry to ensure innovation to become competitive. Based on a shared understanding, businesses become able to develop an agility leaning towards the customers. As a result, telecom operators become able to digitally transform their products and services to optimise the network, increase predictive maintenance and others. However, Rekogama (2022) opined that intense competitiveness in the industry disallows organisations to collaborate and drive digital transformation. A lack of sharing resources and best practices can reduce the possibility of innovation and transformation using BDA. Therefore, successful organisational partnerships and collaboration helps in leveraging the collaborative development of the telecom industry.

2.3.3 Real-time infrastructural development for digital transformation

Telecom businesses employ data analysis to track network traffic real time. According to Lvet al., (2021), integration of real time operational analysis through big data allows to adjust network bandwidths. It allows companies to save their costs on the quality of network services in different regional landmarks. In addition, real time analytics allows telecom operations to bring innovation in changing dynamics of the customer centric telecom industry. The motivation of a proactive approach to foster innovative infrastructure development to swiftly adapt to changing customer expectations is a driving force behind companies choosing big data analytics (Keshavarz et al., 2021). Therefore, data processing through big data can leverage increasing overall quality of telecom industry’s services and provide seamless experiences to customers.

2.4 CHALLENGES TOWARDS BIG DATA APPLICATION FOR DIGITAL TRANSFORMATION OF BUSINESS

Figure 2.3: Challenges in Big data in telecom industry

(Source: Created by author)

2.4.1 Lack of technological knowledge among employees

Big data analytics in business can be considered as the core technology for empowering the business systems. As commented by Wang et al., (2022) big data technology has assisted towards accelerating information technology through use of large amounts of data for analysis. It has led businesses towards adopting big data over business operations to enhance business operations. However, adoption of big data in an organisational setting becomes a challenging part due to lack of technological knowledge among employees. Use of big data requires an adequate and higher amount of data incorporation over the system for producing accurate results (Hamilton and Sodeman, 2020). Thus, employeesare having a lack of technical knowledge restraining them from being effective towards handling big data activities within the organisation. In this context, employees with lack of technological skills can lead to creating issues in incorporating larger and accurate data sets in the big data system in the telecom industry. It restrains the big data system from producing accurate results towards business decision-making and digital transformation of the telecom industry in the long-term.

2.4.2 Issues in data collection and choosing right techniques

Digital transformation of businesses has led the telecom industry towards adopting big datafor improving data analytics processes. It has assisted the telecom industry to analyse large data sets including organisations previous years of data and others. Regarding this, Bhat and Huang (2021) mentioned that merging data from an assortment of sources can raise issues related to information quality in BDA. However, inaccurate data can lead telecom industry towards encountering issues in the decision-making process for digital transformation. Rathore et al., (2021) highlighted advanced architecture is required by industry towards acquiring, storing and sharing information in big data processes. Having a lack of advanced architecture, the telecom industry might encounter information sharing issues in big data analytics. Despite this, choosing of right techniques for big data has become another challenge for industries to overcome in big data implementation and lead businesses towards digital transformation. Bhat and Huang (2021) reported that big data requires extraordinary methods to effectively process huge quantities of data for an infinite running time. Thus, businesses are unable to choose between techniques like AI-based or ML based big data applications to achieve higher data exploration decision-making towards digital-transformation.

2.4.3 Lack of technological infrastructure for adopting big data analytics

A technological infrastructure highlights the capability of industries to adopt new technologies according to business requirements. Digital or technological infrastructure is the foundation towards digital transformation and innovation for business operations. However, telecom industry lacking IT infrastructure can impact the application of big data for enhancing the data analytics. As commented by Le (2023), due to poor IT infrastructure, there is a higher risk towards system failure in big data processes. System failure or network crashes in between the big data analytics processes can majorly impact the outcomes of digital transformation of the telecom industry. However, application of specialised IT teams over the telecom industry allows mitigating overall risk of system failure in big data operations. As supported by Li et al., (2021), organisations having computing infrastructure can lead towards effective application of big data through following the IT stages. Thus, lack of IT infrastructure or team restrain from mitigation of issues occurring in big data technology.

2.5 STRATEGIES FOR EFFICIENTLY INCORPORATING BIG DATA OVER BUSINESS OPERATIONS TO COPE UP WITH DIGITAL TRANSFORMATION IN BUSINESS ENVIRONMENT

Figure 2.4: Strategies for big data application in telecom industry

(Source: Created by author)

2.5.1 AI-based tool application strategy for big data application

Telecommunication industry is recommended towards adoption of AI-based tool application over BDA as an effective strategy.Artificial intelligence (AI) is modern technologythat can assist the telecom industry to revolutionise the business process like BDA. As commented by Kamrowska-Załuska (2021), AI-based tools are capable of fuller use of potential big data by sourcing higher quality and accurate data. Application of AI-based tools with big data can lead the telecom industry towards effective planning. In addition, AI-based tools are capable of sourcing and storing higher quality data using automation. These data can be utilised by the telecom industry to effectively bring out accurate results that can assist towards decision-making.. Despite this, AI-based tools are costly for organisations to implement and can lead towards issues for the telecom industry. As mentioned by Punia et al., (2021), AI algorithms in big data can lead towards designing problems that are easy to understand and reduction of complexity of the problem. In this context, use of algorithms in the huge data set of the telecom industry would be fixed. Algorithms can assist in removing the ineffective data from the datasets before use in big data analytic (Punia et al., 2021). Therefore, it would lead the telecom industry to bring the most accurate results towards decision-making processes by using AI-based tools in BDA.

2.5.2 ML-based strategy to improve big data application

Machine Learning (ML) is considerably an effective technology to be incorporated in telecom industry to enhance the application of big data. As commented by Nti et al., (2022), integration of ML-based algorithms can lead towardseamless collection of internal and external data through a common platform. It can lead the telecom industry towards collection of higher amounts of data through online platforms to use BDA for effective decision-making. Cravero and Sepúlveda (2021), also mentioned that allocation of structured, semi-structured and unstructured ML algorithms allow incorporating higher data sources. It can assist towards performing ML algorithms concerning input data format to increase data integrity in BDA. Therefore, application of ML algorithms needs to be accurate in BDA otherwise it might lead telecom industry to encounter data integrity issues.

2.5.3 Creation of roadmap for identification and prioritisation of big data use

Clarity in planning or developing a roadmap of big data applications in the telecom industry would improve innovation opportunities through digital transformation.. As commented by Karim et al., (2022), an effective business plan is able to guide through systematic stages to achieve desired business goals. Planning would allow the telecom industry to generate a clear budget towards big data implementation and digital transformation leveraging BDA. Additionally, effective budget planning would allow removing cost constraints for telecom industry big data applications through managing those resources for BDA. On the other hand, Abdullah (2020), planning would allow to utilise the capabilities of business to bring out better performance. Planning can lead the telecom industry to choosing BDA techniques for effectively analysing data for digital transformation. Thus, creation of a roadmap and planning would be an effective priority task for big data applications that can lead organisations towards digital transformation.

2.6 Theoretical Perspectives

2.6.1 Resource Based View Theory

Figure 2.5: Components of Resource Based View Theory (RBV)

(Source: Influenced by Varadarajan, 2020)

“Resource Based View (RBV) theory” determines resource as the most important component to fetch success of an organisation along with providing competitive edge. Figure (2.5) of this study has provided the components or aspects of RBV theory that highlights the way a firm relies on tangible and intangible resources. As per the views of Varadarajan (2020), resources can drive better organisational performance and drive competitive edge in terms of RBV theory. Moreover, heterogeneous aspects meanthat non-tangible resources such as skills and capacities differ from one company to another. Immobile component resources do not move from one company to another and an organisation gets a competitive edge as rivals cannot replicate it in a short run. Thus, innovation such as implementing BDA in terms of the telecommunication industry can get a competitive edge as BDA is a heterogeneous resource.

As per the context with implementing data analytics in business innovation, it is a resource that would increase productivity of the Telecommunication industry. According to Niu et al., (2021), “Big Data Analytics (BDA)” successfully manages risks and supports customer retention through better customer services and informed decision-making. Considering this factor, it exhibits that the telecommunication industry can gain competitive edge and improve operational efficiency by implementing BDA in organisations. As argued by Barney et al., (2022), the broad definition of resources in RBV theory makes it difficult to determine the appropriate level of analysis. Despite this factor, RBV theory highlights the way BDA implementation as a part of digital transformation can benefit the telecom industry by providing a competitive edge. Therefore, integrating the components of RBV theory provides an opportunity to understand the way innovation in terms of implementing BDA as a part of digital transformation in the telecommunication industry supports in achieving competitive advantage.

2.6.2 Innovation Theory

Figure 2.6: Elements of Innovation Theory

(Source: Influenced by Okouret al., 2021)

Innovation theory highlights the way innovation is impacted by external as well as internal forces. Figure (2.6) has provided the elements of innovation theory and it represents an idea regarding an object that is perceived to be new. As mentioned by Okouret al., (2021), external and internal environments are highly connected with technological innovation in an organisation. Characteristics of technology and its availability are interconnected with external environments such as government regulation and internal environments such as communication processes. For instance, data privacy is a major issue in BDA and it can lead to complexity of government regulation. As argued by Horta (2022), innovation theory takes a dismissive stance to those who do not want to adopt innovation. Thus, stakeholder’s resistance to change might be a major issue in telecommunication in terms of implementing BDA following Innovation Theory.

2.7 Gap in Literature

This study has identified gaps in literature in terms of gaining a clear understanding of the BDA implementation as a part of innovative digital transformation in the telecommunication industry. For instance, Barney et al., (2021), has criticised the RBV theory but it has not shed light upon practical approaches of using this theory for BDA management. On the other hand, Rejebet al., (2020), has represented limited information about BDA assisting in the improvement of marketing operation improvement of the telecommunication industry to improve innovation approaches. Hence, this research would be focused on critically analysing interrelevance between digital transformation and innovation by leveraging BDA in operation management of businesses in the telecommunication industry.

2.8 Summary

From the above literature, it can be concluded that big data allocation can lead the telecom industry towards higher digital transformation. Telecom industry would get an opportunity to improve their data analytics process by analysis of previous years of data. Application of techniques AI and ML can further enhance the process of big data analytics in the telecom industry to achieve positive results.

CHAPTER 3: RESEARCH METHODOLOGY

3.0 Introduction

Methodology chapter has played a crucial role in undertaking a set of key methods to conduct the research on the impacts of big data in digital transformation. Using the framework suggested by Saunders, this research has chosen research philosophy, approach, strategy, data collection and analysis methods accordingly. Methodology has been effective in collecting effective data related to the adoption of big data in business digital transformation with a reference to British Telecommunication. Additionally, defining the key ethical considerations has also been a crucial part of the third chapter in this research. Hence, the methodology chapter determined the success of this research through ensuring the application of proper methodologies and maintenance of key ethical guidelines.

3.1 Research Onion

Figure 3.1: Research Onion

(Source: Influenced by Saunders et al., 2019)

Research onion enabled the researcher to gain an effective understanding about all the layered stages of the key methodologies used in the dissertation as suggested by Saunders. As stated by Abdelhakim (2021), the research onion in a research work generally creates a series of steps under which different methods of data collection, analysis might be understood. Research onion illustrated the steps through which this methodological study was described and was able to choose the most suitable methodological approach for this research.

3.2 Research Philosophy

Research philosophy refers to a belief in which information about a certain phenomenon is gathered, analysed and utilised. Research philosophy is mainly categorised into three parts such as pragmatism, interpretivism and positivism. “Positivism philosophy” was incorporated for conducting research on impact of big data in digital transformation of British telecommunication. According to Alharahsheh and Pius (2020), positivism is associated with a philosophical stance that works with observable reality of the phenomenon which leads to the production of generalisation. Hence, positivism philosophy helps to gather observable and measurable facts based on objective views that support to increase reliability of the data. Objective view on the phenomenon helps to develop realistic information on digital transformation. On the other hand, Junjie and Yingxin (2022) depicted “interpretivism philosophy” focused on human factors which might be influenced by the subjective bias of researchers. Thus, interprets hold subjective views on the phenomenon which might increase propensity of bias as interpretivists considered emotional factors. Hence, data generated on impact of big data cannot be generalised across all the phenomena by using interpretivism. Therefore, “positivism philosophy” had utilised to gather valuable data to generalise findings on the digital transformation process by using big data.

Positivism philosophy highly relies on the objective of a phenomenon that increases reliability of information compared to “interpretivism philosophy”. According to Ikram and Kenayathulla (2022), positivism philosophy strives to examine and confirm law-like patterns through reason, observable measure. Hence, impact of big data on improving data analytics or decision making evaluated based on measurable factors like numerical data instead of considering human emotion. On the other hand, interpretivism is also considered human behaviour which raises concern for data bias. Therefore, positivism philosophy was used in the research based on measurable and observable facts to gain objective over the impact of big data in digital transformation.

3.3 Research approach

The research approach refers to the proposal or plan utilised in order to conduct research. “Inductive, deductive and abductive research approach” is generally incorporated by researchers to comprehend a certain phenomenon through research. “Deductive research approach” was utilised in conducting research on impact of big data to influence digital transformation of British Telecommunication. As per research of Okoli (2023), the deductive approach begins with a theory; afterwards, it infers the data that is expected to show a phenomenon. Hence, a deductive approach is beneficial to comprehend a change over a certain period. Thus, “deductive approach” was utilised to comprehend the impact of big data over a period and the way it contributes to the digital transformation. On the other hand Casula et al., (2021), inductive approach is dependent on the generated theory based on the gathered information about a phenomenon. Hence, an inductive approach raises concern about generating incorrect conclusions due to the limitation of evidence or knowledge. Therefore, “deductive approach” assists to inter-relate different variables of big data and the way there’s factors influences digital transformation.

Deductive research approach supports the cause-and-effect relationship regarding digital transformation phenomenon influenced by big data. According to Hall et al., (2023), in deductive approach conclusion is certain provided are true as the conclusion based on logical reasoning. Hence, deductive approaches could be utilised to develop relationships between different variables like big data, forecasting, and data analytics to come to the conclusion of the way it impacts digital transformation. However, the inductive approach does not provide any robust conclusion based on logical reasoning. Thus, “deductive approach” was utilised to comprehend the relationship between big data and digital transformation to develop a reliable conclusion.

3.4 Research design

Research design is a strategy utilised by researchers to address research questions utilising empirical data. There are mainly four types of research design such as correlation, quasi-experimental, descriptive and experimental incorporated by researchers to perform research. “Descriptive design” was used in this research to detail the phenomenon impact of big data analytics on digital transformation. As per Doyle et al., (2020), the rationale for using descriptive design is its straightforward description of the perception and experiences of a phenomenon. “Descriptive design” is a considered in-death elaboration of a digital transformation influenced by big data analytics. Additionally, descriptive design alos covers different aspects such as challenges in using big data or influencing factors of using big data. On the other hand, Mohajan (2020) depicted an “experimental research design” focused on experiments between independent and dependent variables to determine cause and affect relationship. Hence, it might take a high amount of time to conduct the experiment; moreover lack of data might leave gaps in research. Thus, “descriptive design” utilised to gain in-depth knowledge on the digital transformation by evaluating different aspects of big data analysis. Therefore, “descriptive design” provides a holistic overview on the digital transformation process of British Telecommunication.

3.5 Research strategy

Research strategy signifies a plan that guides research activities to achieve research objectives. Survey research strategy incorporated within this research to collect relevant information on digital transformation of British Telecommunication influenced by big data analytics. According to Braun et al., (2021), survey strategy is an effective way to collect rich and relevant information through a set of questionnaires. Hence data generated from survey strategy can be quantified which appears to be more authentic than qualitative data. However, “exploratory research strategy” involves qualitative data collection which can be biased or judgemental. Hence, data generated by using explatory methods could detrimentally impact research result. Thus, “survey strategy” was utilised to gather relevant and precise information on digital transformation of British telecommunication.

3.6 Sampling Techniques and Population

The procedure of selection of sample population from the target population is considered to be “sampling methods”. “Random, snowball, convenience, quota sampling” are mainly used by researchers to conduct research. “Simple random sampling” methods were incorporated for selection of appropriate populations from targeted populations. According to Mahmud et al., (2020), “simple random sampling” is a probability sampling where researchers randomly select a subset of participants from a population. Every subset within the population has equal probability to get selected randomly that increases authenticity of data. Hence, every employee selected for the survey had an equal chance to be selected to represent their opinion of digital transformation. On the other hand, convenience sampling involves collecting information from the best approachable resources. As per research of Emerson (2021), results develop by utilising convenience sampling lack generalisation due to bias of sample. Hence, by adopting convenience sampling, data generated through the impact of big data on digital transformation and innovation might not be authentic. Thus, “random sampling methods’ ‘were adopted for the selection of the population that holds higher authenticity of research results.

100 participants were selected in the initial phases of research to collect relevant information on innovation and digital transformation of British telecommunication influenced by big data by using surveys. However, 55 participants participated in the research to explain the influence of big data on digital transformation. The inclusion criteria for selecting the participants, they need to possess sufficient knowledge on big data analytics and digital transformation.

3.7 Techniques and procedures

3.7.1 Data Collection method

Data Collection is considered to be a process of gathering as well as measuring information on variables of interest along with establishing a systematic approach that helps in answering research questions. As for the study of Schrijverset al. (2020) data collection is the most important part of a research that determines quality of research and possible outcomes. These authors further elaborated that data collection can be divided into two categories such as, “collection of quantitative data” and “collection of qualitative data”. Both qualitative and quantitative data are collected through primary or secondary data collection methods that help in improving overall quality of research outcomes.

Considering data collection methods, this study has adopted “quantitative data” by collecting those through a “survey” method. Collection of quantitative data was executed through “primary data collection method” for enhancing reliability and validity of the research. Following a primary data collection method, the researcher focused on interacting with individuals followed by undertaking their perspective related to impact of data analysis for business innovation. On the other hand, a secondary data collection method was excluded from the study due to risk associated with manipulation of data which could lead to inadequate results. Therefore, “primary quantitative data collection method” was included for collecting firsthand information as well as improving quality of research.

3.7.2 Data Analysis techniques

Purpose of data analysis in research is mostly associated with drawing conclusions on specific data for answering research questions as well as meeting research objectives. Data analysis techniques can be divided into two categories including “qualitative analysis of data” and “quantitative analysis of data”. The qualitative analysis of data is generally associated with development of themes followed by organising a thematic analysis. On the other hand, quantitative analysis is associated with “numerical analysis” and “statistical representation”. Since this study is based on collection of data through a survey method, it included a “quantitative analysis of data”, followed by numerical statistical analysis and numerical representation. On the other hand, thematic analysis was excluded from the study as it is mostly associated with collection of secondary data which can often be manipulated. Therefore, quantitative analysis of data through survey methods was included in the study to meet research objectives.

3.8 Reliability and Validity

Reliability and validity are regarded as one of the most important factors within research that helps in increasing opportunities to deliver the most authentic research outcome. In regard to enhancing reliability and validity of research they study has focused on collecting most authentic data from first hand sources along with avoiding manipulation or miss representation of data. In addition to this, it has also included information from individuals who have experience in concerned research topic. In this study a diversified group of individuals has been referred to understand perspectives of each individual regarding impact of data analysis for business innovation.

3.9 Ethical Consideration

Ethical consideration is another most important factor that plays an important role in enhancing reliability and validity of research. Referring to ethical considerations, this study followed multiple legal and regulatory frameworks which helped in improving quality of research. For instance, in the study, “Data Protection Act, 2018 (c. 12)” has been followed effectively in regard to maintaining confidentiality of respondents along with avoiding misinterpretation of data. University guidelines were also followed to avoid unethical practices such as plagiarism and Outsourcing of work.

3.10 Summary

From the above conclusion it can be summarised that primary quantitative methods were followed to complete the digital transformation of British telecommunication influenced by big data analytics. Positivism and deductive approaches were incorporated to gain an objective view of the digital transformation process. Descriptive design detailed elaborate relationships between different variables like innovation, digital transformation and big data. Survey methods were implemented while conducting to collect primary information from the employees of British Telecommunication. “Simple random sampling” was used for selecting participants for conducting the survey. In conclusion, quantitative analysis was incorporated to develop reliable and validate results to gain in-depth insight on digital transformation.

CHAPTER 4: FINDINGS, ANALYSIS, EVALUATION AND DISCUSSION

4.0 Introduction

Finding an analysis of data helps in interpreting information that is beneficial for enhancing the quality outcome of research. This chapter focuses on determining key findings from numerical representation and statistical analysis of data along with developing a detailed discussion related to research topic.

4.1 DATA ANALYSIS

4.1.1 Primary Quantitative: Survey

Question 1

Option	Number of respondents	Total number of participants	Ration (%)
Very familiar	30	55	55%
Notfamiliar	5	55	9%
Tosome extinct familiar	20	55	36%

Table 4.1: Familiar with big data analytics about BT

Figure 4.1: Thoughts with application of big data analytics about BT

According to Figure 4.1, out of 55 employees of British Telecom (BT), responses related to 30 represented familiarity with the application of big data analytics. However, 5 participants have shared their viewpoints on not being familiar with application of big data analytics in BT whereas the rest 36% of responses are familiar with approaches of big data analytics. Well-managed familiarity with application of big data analytics helped individuals to describe the process of uncovering trends, patterns and correlation of data in infrared decision-making approaches. Thus, analysing the responses has helped to determine business transformation and optimisation of better decisions by applying big data analytics in BT.

Question 2

Option	Number of respondents	Total number of participants	Ration (%)
In personaliseservices	10	55	18%
Inproblem dissolving	20	55	36%
Inimproving network	25	55	46%

Table 4.2: Role of big data in innovation in BT

Figure 4.2: Certain areas of improvement in BT due to big data application

As per Figure 4.2, a wide range of perspectives have been addressed that denote the effective role of big data in management of business innovation and performance management within BT. A total of 55 responses, 25 employees of BT have denoted big data plays an effective role in improving the network within BT. Additionally, the rest of 30 participants have agreed that big data analytics have enabled innovation in personalised services as well as problem solving activities. From this, it has been understood that big data analytics have leveraged on optimising business innovation within BT across multiple areas such as personalised services, problem-solving and network.

Question 3

Option	Number of respondents	Total number of participants	Ration (%)
In marketing and sales	15	55	27%
customer service	30	55	55%
Network Optimisation	20	55	36%

Table 4.3: Influence of big data on BT’s operation process

Figure 4.3: Big data influences efficiency of overall operation process at BT

Above Figure 4.3 comprehends valuable insights related to influences of efficiency on overlap operation of BT. A total of 100% of respondents, 27% responses denoted marketing and sales, 55% highlighted customer services and 36% depicted network optimisation. Focusing on positive responses in this survey process helped to determine the effective influence of big data analytics in optimising working efficiency with operation process optimisation within BT.

Question 4

Option	Number of respondents	Total number of participants	Ration (%)
Technical issues	26	55	47%
Data security	25	55	45%
Skill personals	4	55	7%

Table 4.4: Issues faced by BT in implementing big data

Figure 4.4: Challenges in big data

Figure 4.4 deals with interpretation of valuable responses accumulated from employees of BT as research participants on challenges rising while implementing big data analytics. Focusing on the percentage of the responses, it has been observed that majority of participants, like 51 have faced challenges such as technical-issues and data-security in implementing big data analytics. Based on these issues, associated individuals have prioritised strategic decision making process to overcome such issues and strengthen operation process of BT.

Question 5

Option	Number of respondents	Total number of participants	Ration (%)
Yes	40	55	73%
No	10	55	18%
Not sure	5	55	9%

Table 4.5: Application of big data in customer experience

Figure 4.5: Attributes of big data technology on customer experiences of BT

According to Figure 4.5, it has been observed that 73% of responses deal with the positive answer of the survey question associated with application of big data analytics in customer experiences. Despite focusing on contradictory responses, employees of BT represented their viewpoints regarding improvement in customer services through application of big data analytics.

Question 6

Option	Number of respondents	Total number of participants	Ration (%)
Significantly	30	55	55%
Moderate manner	20	55	36%
Minor	5	55	9%

Table 4.6: Influence of big data in decision making process

Figure 4.6: Effect of big data analytics in optimising decision making process of BT

The above Figure 4.6 denoted valuable responses of the associated research participants on influence of big data analytics towards influencing decision making process within BT. Additionally, majority of respondents like 50, out of 55 have agreed that big data analytics have significant influences on strengthening the decision making process within BT. It helped to understand business process optimisation approaches through applying big data analytics in BT.

Question 7

Option	Number of respondents	Total number of participants	Ration (%)
Strongly agree	20	55	36%
Agree	25	55	45%
Strongly disagree	2	55	4%
Disagree	5	55	9%
Neutral	3	55	5%

Table 4.7: Involvement of big data

Figure 4.7: Competitive advantage of implementing big data

The above pictorial representation highlighted positive and negative responses of research participants regarding involvement of big data analytics in BT to enable competitive advantages. A total of 55 participants, employees of BT (research participants) have strongly agreed on the facts of applying big data analytics towards providing competitive advantages to BT. Apart from this, contradictory results have been found by analysing responses of rest 10% of participants. Focusing on negative responses, consideration of positive responses helped to determine relevance of implementing big data analytics in providing competitive advantage to BT.

Question 8

Option	Number of respondents	Total number of participants	Ration (%)
To some extent	35	55	67%
Not at all	5	55	9%
Somewhat	15	55	27%

Table 4.8: Implication of big data

Figure 4.8: Data privacy issues in implementation of big data

The Figure 4.8 depicted those 35 employees of BT as research participants stated that data privacy has been a major concern in business processes while implementing big data analytics. Relating to the responses of associated research participants, data privacy issue has been considered as a major concerning factor within BT’s operation process while implementing big data analytics.

Question 9

Option	Number of respondents	Total number of participants	Ration (%)
In the joint venture process	30	55	55%
In the collaboration process	15	55	27%
In the strategic alliance process	10	55	18%

Table 4.9: Application of big data in promotion of partnership in BT

Figure 4.9: Big data optimised BT towards influencing on partnership

From above figure 4.9, it has been found that 30 respondents have stated that application of big data analytics promoted joint venture processes that influenced partnership activities. On the other hand, other respondents like 25 participants have stated other influences of big data analytics within the field of partnership such as collaboration and strategic alliance. From this, it can be asserted that big data analytics promoted partnership activities within BT by initiating joint venture process, collaboration and strategic alliances.

Question 10

Option	Number of respondents	Total number of participants	Ration (%)
In the promotion of brand	10	55	18%
Helping in differentiate its product	15	55	27%
Leadingto market representation	30	55	55%

Table 4.10: Competitive process improvement in BT

Figure 4.10: Big data analytics improved competitive process

Based on Figure 4.10, it has been found that 30 respondents stated that big data analytics have improved the competitive process of BT by leading towards market penetration. Apart from this, promotion of brand and product differentiation has been found in other research participants of associated research participants in the current survey process. From this, it has been analysed that big data has effectively improved the competitive process of BT through development of brand promotion, product differentiation and market presentation.

4.2 Key Findings

Considering data analysis, it can be stated that most of the participants were aware of big data analytics of British Telecommunication which define increasing popularity of data analytics in telecommunication. Despite the significant number some of the respondents also represented their perspective regarding moderate familiarity with data analytics. Following analysis of this data, it has been found that understanding of big data analytics would allow individuals to realise process of patterns and trends associated with decision making approaches in business. From second question of the survey, it has been found that most of the respondents have agreed to the fact that big data analytics plays an important role in improving networks along with dissolving problems. Both of these factors help in increasing opportunities for business related innovation. Third question of the survey dealt with understanding influence of big data on operation process of British Telecom and from this it has been found that most respondents have agreed on customer service perspective. Therefore, adoption of big data analytics helps in enhancing quality of operation by offering adequate customer service strategies.

The survey data analysis also helps in understanding current issues or limitations faced by BT to implement big data which causes a negative impact on business operations. Following analysis of 4th question, it has been found that most of the respondents have agreed on technical issues and data security as two of the most important factors that create challenges in adopting big data analytics. Considering analysis of impact of big data on customer experience it has been found that in 5th question maximum number of respondents have provided a positive response. Therefore, through analysis of this data it can be stated that big data has a potential in enhancing customer experience. 6th question of the survey included understanding impact of big data in decision making process and in response to this question most participants provided a positive response. Hence, it has been found that Big Data Analytics has a major impact on decision making that leads to improved opportunities related to innovation.

Application of big data in business helps in improving success factors while adopting innovation and this perspective was strongly agreed by most of the participants who represented their perspectives regarding involvement of big data in business. Most of the participants also agreed that implementation of big data plays an important role in providing competitive advantage to organisations along with ensuring their growth. However, a major concern that has been found from the primary data analysis involves privacy issues associated with implementation of big data. Most of the participants agreed that data breaching and lack of security concern is a major challenge that causes a negative impact on implementation of big data. Despite these issues respondents have agreed that big data analytics is helping BT to optimise partnerships in business by organising joint ventures. Therefore, application of this process has played an important role in enabling BT to lead in market representation.

4.3 Discussions

Big data analytics is not a new concept for the telecommunication sector and thus employees of British telecommunication are aware about it. As stated by Kamrowska-Załuska (2021), the telecommunication sector prefers using big data analytics for gaining clear understanding about customer behaviour patterns and trends. Application of big data analytics in coordination with AI is considered to be a privileged aspect for British Telecommunication in increasing digitisation and improving decision-making. Contrastingly, British Telecommunication needs to use big data analytics for reshaping their services in order to satisfy demand of customers. Apart from improving their business operations through relieving congestion, British Telecommunication also prefers using big data analytics for enhancing their value chain. Therefore, opportunities that British Telecommunication can also achieve from big data analytics application is categorising customers for providing them best services as per their demand.

Telecommunication organisation such as British Telecommunication is interested in investing more on big data analytics for increasing customers base and gaining better position as well as reputation in global market. As mentioned by Nti et al., (2022), automation is an important segment of AI-based tools used by telecommunication sector assisting in storing as well as sourcing data of high quality. According to employees working in telecommunication sector, big data analytics needs to be used mainly for improving networks, solving problems and personalising services appropriately. Optimisation of innovation in businesses is only possible through implementation of big data analytics within British Telecommunication for improvising networks.

British Telecommunication is noticed to be able to improve its entire businesses operation through relying on both big data analytics and AI. Wassouf et al., (2020) identified that the influence of big data upon telecommunication organisations is high as it helps in improving customer service, sales and marketing as well as optimising networks. Application of ML-based big data is another strategy that British Telecommunication can implement for overcoming challenges in maintaining a huge range of data. On the other hand, it was found that telecommunication organisations in recent times were not able to manage a huge range of data effectively. Finally, through using big data analytics along with AI, British telecommunication is able to make improvements in its business operations.

4.4 Summary

From this chapter it can be summarised that big data has played an important role in enhancing business opportunities of British Telecom which is also positively influencing factors related to innovation. This chapter has focused on detailing data findings along with inter relating results with factors proposed by previously executed research papers. This chapter also plays a legitimate role in determining authenticity and validity of data along with its ability to meet research objectives.

CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS

5.0 Conclusion

Based on the above discussion it can be concluded that the importance of big data analytics is high as it can help British Telecommunication increase its profitability. British Telecommunication can increase digitalisation within its business operations through assisting in optimisation of network usage as well as services, improving security and enhancing customer experience. It can also be concluded that British Telecommunication needs to adopt big data on an emergency basis for bringing innovation in their supply chain. Apart from that, it can also be found that through adopting emerging technologies such as Big data and AI, British Telecommunication is able to develop customer behaviour patterns which further helps in improving revenue. Application of big data is a positive and mandatory aspect for British Telecommunication as it would help in increasing profits across the value chain. Therefore, through empathising on big data, British telecommunication is able to improve product development, spanning network operations, customer service, sales and marketing.

Use of big data analytics is highly recommendable for British telecommunication as it enables in predicting peak network usage for taking measures in relieving congestion. Moreover, it can further be concluded that big data analytics is also responsible for helping British Telecommunication identify target audiences along with improving organisational activities. On the other hand, it was also found that through using big data analytics, British Telecommunication is able to use large datasets for extracting valuable insights about customer behaviours. Furthermore, big data analytics is accountable for necessitating innovative and new data processing approaches along with assisting data analytics in improving businesses operations. Thus, it can also be deduced that through including big data in data analytics, British Telecommunication is able to extract useful information for making better decisions on businesses during ensuring digital transformation.

British Telecommunication needs to focus on the immediate implication of big data for making digital transformation within businesses. It can be concluded that British Telecommunications is capable of using big data analytics as it is responsible for adding value within optimising businesses performance through taking right decisions. Apart from that, through interpreting big data within business operations, British Telecommunication is able to make improvements in analysing data regarding “social network, call records and server logs”. It can further be concluded that British telecommunication needs to use big data analytics as it can gain the opportunity of analysing a large range of data within a short span of time. Moreover, application of big data analytics is considered to be a crucial aspect and valuable resource for British telecommunication for gaining opportunities of achieving competitive advantages. As a result, through using big data analytics British Telecommunication are able to forecast outcomes in coordination with enhancing strategies as well as processes.

Based on the above discussion it can be concluded that this study has potential of providing innovative technical solutions to British Telecommunication. Furthermore, another positive side of following study is mentioning ways of overcoming identified challenges which include integration compliance, data privacy challenge and data quality management. Additionally, for gaining innovative technical solutions and identifying challenges the telecommunication industry is facing, suitable methods were chosen. It included selecting “deductive approach, positivism philosophy, descriptive data design and primary data collection method”. This study was also concluded to be as ethically correct as authentic data were collected from 55 employees of British Telecommunication through surveying them and by following “Data Protection Act, 2018 (c. 12)”. Apart from that, this study also concluded that for adopting big data analytics successfully, British telecommunication included AI in business operations. Therefore, it can be summarised that application of big data analytics and AI simultaneously, helped British Telecommunication in tailoring marketing strategies effectively.

British Telecommunication is highly prioritising big data analytics as it combines a “diverse set of capabilities’ ‘ with huge data for delivering beneficial outcomes in respect to timing competitor performances. Alternatively, it was also noticed that the telecom industry has been facing challenges such as poor firm performances due to lack of knowledge of employees on big data analytics. British telecommunication in recent times, was noticed to be increasing its investment in execution of big data analytics along with developing the majority of their time. Big data analytics is of more importance for telecommunication companies such as British telecommunication as through it, accessibility to huge customer data is easily achieved. Adopting big data analytics is considered to be a holistic method for managing huge data of subscribers connecting with their networks and services on a daily basis. Therefore, through including big data analytics in businesses operations British telecommunication is able to gain multiple opportunities such as real-time analysis, delivering productivity. Improving decision-making and reducing operational costs.

5.1 Linking With Objectives

5.1.1 Linking with objective 1

Objective one of this study has been fulfilled in survey question one and two along with LR (2.2) of this study is highly associated with each other. Survey question one and two have developed for identifying the role of “Big Data Analytics (BDA)” within digital transformation and innovation in organisations. On the other hand, LR (2.2) also discussed the role or significance of BDA in the telecom industry in terms of digital transformation and innovation.

5.1.2 Linking with objective 2

This objective was met in (2.3) in literature review and survey question 10 and 5 in chapter 4, 6 of data analysis chapter. It has critically evaluated the major factors of BDA for innovation and digital transformation in the telecommunication industry.

5.1.3 Linking with objective 3

This objective is fulfilled in LR (2.2) and survey question 6,5,8 has fulfilled these research objectives. It has discussed the role of AI for gaining success in big data for driving innovation in British Telecommunication.

5.1.4 Linking with objective 4

This objective is fulfilled in (2.2) of literature review and through survey question 4 and 8 of this study. All these aforementioned sections in this study have critically discussed the challenges of adopting BDA for digital transformation in the telecommunication industry by considering British telecommunication.

5.2 Recommendations

5.2.1 Employee training

Employee training is recommended for mitigating issues regarding lack of employee knowledge along with employee’s resistance towards change. As per Amanullah et al., (2020), lack of digital literacy increases chances of data breach while using Big Data Analytics (BDA). This factor might be a challenge for British Telecommunication in terms of innovation and digital transformation by implementing BDA. However, employee training would assist in enhancing the implementation of BDA for digital transformation and reduce risk in terms of data privacy issues. Moreover, productivity of British Telecommunication would increase as all employees would be trained to access BDA in the proper way. It would take approximately 6 months for making all employees trained and it would cost around £2900 for providing relevant training. Thus, employee training would be recommended for successful implementation of digital transformation through BDA.

5.2.2 Development of infrastructure to support BDA innovation

Infrastructure development is recommended as implementing BDA as a part of innovation of digital transformation in British Telecommunications. As stated by Raut et al.,(2021), poor infrastructure leads to failure of implementing big data analytics due to lack of technical support. For instance, installing control and monitoring systems, cloud based monitoring and other tools are required for implementing BDA in British Telecommunication. On the other hand, storage resources are also important in terms of implementing BDA. Development of infrastructure at British Telecommunication can cost around £50000 and would take about 6 to 8 months.

5.2.3 Restrict Access

Restricting access is one of the key criteria as it would be beneficial for mitigating issues with data breach along with cyber threats. As mentioned by Kavianpouret al. (2022), the risk of misuse or unintentional use of data can be reduced through restructuring access to those employees that are required to handle it for their job roles. British Telecommunication can be able to mitigate legislative issues regarding data breach. Additionally, reducing the risk of data breach can successfully implement BDA as a part of digital transformation in British Telecommunication. Access restriction requires very less cost along with time as it is not a major task. However, all employees required to communicate well before restricting access to limited people in British Telecommunication. Thus, restricting access might not require any capital investment but it would take around 1 month of time for communication, accumulate employees’ opinion on that and finally implement the change in this organisation.

5.3 Research Limitations

This research has identified few limitations and it is mainly associated with time and budget constraints along with the highest quality of data . This study cannot accumulate optimal data due to restricted sample size and it is a consequence of time as well as budget constraint. This study has also found it difficult to get topic specific articles in literature review that include all research variables such as BDA, innovation, digital transformation . All of these issues limit the quality interpretation for satisfying objectives of this research paper. Therefore, time, cost and lack of topic specific articles are key limitations of this study.

5.4 Future Scope

Assessment of big data analytics within the telecom industry would be essential for developing critical knowledge about specific approaches of using this advanced technology for digital transformation. Findings of this research would be essential for gaining in-depth knowledge about effective techniques required for businesses and associated managers to understand better ways to leverage BDA in innovation management. Further, assessment of operational and technological approaches of transformation for BDA implementation would be essential for improved operational management.

REFLECTION ON PERSONAL LEARNING

Completing the research on analysing the key impacts of big data within business digital transformation, I have gained a wide set of knowledge, understanding and experience of research and research context. A dissertation work helps an individual to develop understanding of research processes. I have developed my knowledge in terms of implementing accurate strategies to gain relevant information related to bid data implementation in business. I also experienced that establishing primary research, it is very crucial to maintain the critical ethical guidelines such as data confidentiality of participants for fair research conduction. Selection of survey methods has been beneficial for me in collecting relevant quantitative data on the impacts of big data in digital transformation as well.

Considering my personal development, this research has helped me in learning that completing a research project successfully requires me to establish a certain timeline to meet all the outcomes effectively and on-time. Reviewing the existing observations, data on research topics, I also learned effective search strategies to gain significant data for a research work utilising key words and recent journal articles. Hence, the overall research has been an effective learnable and knowledgeable experience for me that developed my key understanding related to research conduction, core aspects related to big data along with a reference to British Telecommunication case study.

In future alternative methodology can be applied in regard to enhancing reliability as well as validity of research. For instance, during this research it was found that different types of methodologies such as secondary data collection method and qualitative data analysis can also be adopted. Considering perspective of a secondary data collection method, future research projects would focus on collecting data from secondary sources such as previously published journal articles, research papers or books. Referring to qualitative analysis, future research papers would conduct thematic analysis of data in which themes would be based on research questions that would help in meeting research objectives.

Problems associated with conducting research include multiple factors such as time management and lack of effective decision-making strategies. In regard to dealing with these problems it would be beneficial to implement strategies related to decision making approaches that would help in ensuring inclusion and exclusion criteria for research. Time management skills are also required to be improved through effective adoption of multitasking which would help in enhancing opportunities to meet faster outcomes. Therefore, problems can be easily resolved by enhancing skills associated with decision making as well as problem solving.

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BMG843 (CRN: 46485) Department of Management, Leadership &amp; Marketing

BMG843 (CRN: 46485) Department of Management, Leadership &amp; Marketing

1.0 Introduction

1.1 Research Background

Figure 1.1: Share of big data market revenue worldwide till 2027, by major segment

1.2 Problem statement

1.3 Aim

1.4 Objectives

Table 1.1: Research objectives using SMART framework

1.5 Research Questions

1.6 Research Rationale

Figure 1.2: Big data security and challenges

1.7 Research Significance

1.8 Structure of dissertation

Figure 1.3: Structure of dissertation

1.9 Summary

CHAPTER 2: LITERATURE REVIEW

2.0 Introduction

2.1 Conceptual Framework

Figure 2.1: Conceptual Framework

2.2 Significance of Big Data Analytics in Telecom Industry

2.3 Factors Driving Big Data Analysis In Telecom Industry

Figure 2.2: Factors Driving Big Data Analysis in Telecom Industry

2.3.1 Customer Experience Personalisation

2.3.2 Collaboration between companies and development of Telecom industry

2.3.3 Real-time infrastructural development for digital transformation

2.4 CHALLENGES TOWARDS BIG DATA APPLICATION FOR DIGITAL TRANSFORMATION OF BUSINESS

Figure 2.3: Challenges in Big data in telecom industry

2.4.1 Lack of technological knowledge among employees

2.4.2 Issues in data collection and choosing right techniques

2.4.3 Lack of technological infrastructure for adopting big data analytics

2.5 STRATEGIES FOR EFFICIENTLY INCORPORATING BIG DATA OVER BUSINESS OPERATIONS TO COPE UP WITH DIGITAL TRANSFORMATION IN BUSINESS ENVIRONMENT

Figure 2.4: Strategies for big data application in telecom industry

2.5.1 AI-based tool application strategy for big data application

2.5.2 ML-based strategy to improve big data application

2.5.3 Creation of roadmap for identification and prioritisation of big data use

2.6 Theoretical Perspectives

2.6.1 Resource Based View Theory

Figure 2.5: Components of Resource Based View Theory (RBV)

2.6.2 Innovation Theory

Figure 2.6: Elements of Innovation Theory

2.7 Gap in Literature

2.8 Summary

CHAPTER 3: RESEARCH METHODOLOGY

3.0 Introduction

3.1 Research Onion

Figure 3.1: Research Onion

3.2 Research Philosophy

3.3 Research approach

3.4 Research design

3.5 Research strategy

3.6 Sampling Techniques and Population

3.7 Techniques and procedures

3.7.1 Data Collection method

3.7.2 Data Analysis techniques

3.8 Reliability and Validity

3.9 Ethical Consideration

3.10 Summary

CHAPTER 4: FINDINGS, ANALYSIS, EVALUATION AND DISCUSSION

4.0 Introduction

4.1 DATA ANALYSIS

4.1.1 Primary Quantitative: Survey

Table 4.1: Familiar with big data analytics about BT

Figure 4.1: Thoughts with application of big data analytics about BT

Table 4.2: Role of big data in innovation in BT

Figure 4.2: Certain areas of improvement in BT due to big data application

Table 4.3: Influence of big data on BT’s operation process

Figure 4.3: Big data influences efficiency of overall operation process at BT

Table 4.4: Issues faced by BT in implementing big data

Figure 4.4: Challenges in big data

Table 4.5: Application of big data in customer experience

Figure 4.5: Attributes of big data technology on customer experiences of BT

Table 4.6: Influence of big data in decision making process

Figure 4.6: Effect of big data analytics in optimising decision making process of BT

Table 4.7: Involvement of big data

Figure 4.7: Competitive advantage of implementing big data

Table 4.8: Implication of big data

Figure 4.8: Data privacy issues in implementation of big data

Table 4.9: Application of big data in promotion of partnership in BT

Figure 4.9: Big data optimised BT towards influencing on partnership

BMG843 (CRN: 46485) Department of Management, Leadership & Marketing

BMG843 (CRN: 46485) Department of Management, Leadership & Marketing