THE ROLE OF DATA ANALYST IN ENHANCING ORGANIZATIONAL PERFORMANCE (A CASE STUDY OF MTN LAGOS)

CHAPTER ONE

INTRODUCTION

1.1   Background to the Study

Organizational performance remains a widely debated topic, yet there is great uncertainty and disagreement about its definition, conceptualization and assessment. Karanja (2014) notes that organizational researchers hold different views on the most appropriate methods for defining and assessing organizational performance. Nevertheless, a significant number of scholars have adopted a framework that emphasizes the organization's ability to achieve its economic goals through the efficient and effective use of its resources.

 

Various metrics were used to evaluate the company's performance, including, but not limited to, profitability, gross profit, return on assets, return on assets, return on equity, return on sales, sales growth, market share, share price, sales growth, export growth, liquidity and operational efficiency. The main goal of any business is to ensure its continuous operation; Therefore, in order to maintain their profitability, numerous organizations have implemented various strategies to improve organizational performance.

 

In the current digital environment, data has emerged as a vital asset for businesses in a range of sectors. The market is flooded with analytical software tools that make it possible to apply advanced big data analytics (ABDA) to boost business performance and make well-informed decisions, which eventually helps businesses succeed. To successfully implement big data analytics, which can significantly improve business performance, a company must have analytical insights from massive volumes of data (Strawn, 2012; Saltz, 2015).

 

Amazon, which attributes 35% of its profits to personalized customer recommendations based on big data analytics, is a well-known example of this concept, claims Wills (2014). Organizations can benefit from ABDA (McAfee and Brynjolfsson, 2012; Goel, Datta, & Mannan, 2017). However, its effective use necessitates specific abilities for handling vast volumes of data, obtaining important insights, and producing useful information from data use (Davenport & Patil, 2012; Schoenherr & Speier-Pero, 2015). Research on Fortune 1000 companies shows that 91% of these organizations are investing in big data analytics, up from 85% the previous year (Kiron, Prentice, & Ferguson, 2014).

 

ABDA is seen as a tool for effectively managing organizational resources and monitoring corporate operations (Davenport, Barth, & Bean, 2012; Russom, 2011). It enhances supply chain efficiency, encourages industrial automation and manufacturing processes (Goel et al., 2017), and fosters business transformation (Akter et al., 2016). Columbus (2014) stated that a clear majority, 87% of companies, view advanced big data analytics (ABDA) as a critical tool for achieving competitive advantage in the next three years.

 

Additionally, 89% of these companies believe that those that do not implement big data analytics are at greater risk of losing market share than those that do. ABDA represents not just a technological innovation, but a comprehensive operational framework (Wilkins, 2013). Decision-making processes that are based on data and information rather than pure intuition are likely to produce more strategic outcomes (Davenport & Patil, 2012; Gardner, 2013). By adopting an analytical mindset, companies can improve their competitive position and achieve their goals more efficiently (Kiron et al., 2014). The connection between ABDA and effective customer loyalty and superior business performance is particularly positive (Akter et al., 2016).

 

The quality of analytical tools has a significant impact on data integrity and the decision-making process, which in turn affects the overall performance of the organization (Davenport & Patil, 2012; Russom, 2011). Additionally, big data analytics serves as a differentiator between high-performing and low-performing organizations. As a result, companies that use big data analytics tend to take a proactive and forward-looking approach, resulting in a 47% reduction in customer acquisition costs and an approximately 8% increase in sales (McAfee & Brynjolfsson, 2012).

 

In recent years, ABDA has attracted significant attention on the corporate agenda due to its potential to increase efficiency and profitability by 5-6% (Kiron et al., 2014). Therefore, big data analytics can bring significant benefits to companies by improving various performance metrics, including financial, marketing and partnership results, and strengthening competitive advantage (Akter et al., 2016; Davenport et al., 2012; McAfee & Brynjolfsson, 2012; Russom, 2011). Consequently, ABDA contributes significantly to improving organizational performance (McAfee & Brynjolfsson, 2012).

 

Data analysts are becoming increasingly important to extract useful insights from huge data sets. This increased focus on data analytics is based on the recognition that, when used skillfully, data can provide important strategic benefits and support informed decision-making. In addition to basic numerical analysis, a data analyst's responsibilities include analyzing patterns, trends, and correlations to help companies make data-driven decisions that improve overall performance (Davenport and Harris, 2007).

 

By transforming unstructured data into insightful knowledge, data analysts play a critical role in improving business performance. To identify patterns and trends that would otherwise be missed, they use statistical methods, machine learning strategies and data visualization tools. Data analysts are crucial to the decision-making process as they understand the importance of various data points that help companies improve their operations, refine their strategies and find new growth prospects.

 

Essentially, data analysts act as intermediaries, bringing together raw data with useful insights that help companies make more informed and strategic decisions (Provost & Fawcett, 2013; Shmueli & Koppius, 2011). Therefore, the aim of this study is to examine the role of data analysts in improving organizational performance, with particular focus on the case of MTN Lagos.

 

1.2   Statement of the Problem

The increasing reliance on data-driven decision-making within organizations has highlighted the critical role of data analysts in enhancing organizational performance. As businesses accumulate vast amounts of data from various sources, the challenge lies in extracting meaningful insights that can inform strategic initiatives and operational improvements. The problem arises from the growing gap between the availability of data and the ability of organizations to leverage it effectively. Many companies struggle to harness the full potential of their data due to a lack of skilled data analysts who can navigate complex datasets, employ advanced analytical techniques, and communicate findings in a way that informs decision-makers. Consequently, there is a pressing need to address the shortage of proficient data analysts and empower them with the tools and resources necessary to unlock the value hidden within the vast sea of organizational data.

 

Furthermore, the role of data analysts is not merely confined to technical expertise; it extends to bridging the communication gap between technical and non-technical stakeholders within an organization. Often, data analysts face challenges in translating their findings into actionable insights that resonate with decision-makers and contribute to strategic planning. This communication barrier hinders the seamless integration of data-driven insights into organizational processes, limiting the overall impact on performance enhancement. The problem, therefore, encompasses not only the technical proficiency of data analysts but also their ability to effectively communicate insights, collaborate with diverse teams, and ensure that data-driven recommendations align with organizational goals. Addressing these challenges is crucial for organizations seeking to fully leverage the potential of data analytics in optimizing their performance and staying competitive in today's data-driven business landscape.Top of Form

 

 

1.3 Objectives of the Study

The main objective of the study is to examine The Role of Data Analyst in Enhancing Organizational Performance. Specific objectives of the study are:

1. 1.  Quantify the impact of data analyst intervention on key performance indicators (KPIs) across different organizational functions.
2. 2.  Identify the specific data analysis practices and methodologies that contribute most significantly to performance enhancement.
3. Assess the role of data analyst integration within organizational structures and decision-making processes.

1.4 Research Questions

To guide the study and achieve the objectives of the study, the following research questions were formulated:

1. 1.  To what extent do data analyst activities, such as data cleaning, analysis, and reporting, correlate with improvements in financial performance metrics like revenue, cost reduction, and return on investment (ROI) across various industry sectors?
2. 2.  Which specific data analysis techniques, such as predictive modeling, machine learning, or statistical analysis, have the most demonstrably positive effect on operational efficiency and customer experience metrics in different organizational contexts?
3. How do differences in data analyst organizational placement, communication protocols, and decision-making influence the effectiveness of their contributions to organizational performance, and what are the best practices for optimizing these factors?

1.5 Research Hypothesis

The following research hypothesis was developed and tested for the study:

Ho: There is no statistical significant relationship between Date Analyst and Organizational Performance.

1.6 Significance of the Study

The study is important for many reasons. The following are the major stakeholders this paper through its practical and theoretical implications and findings will be of great significance:

Firstly, the paper will benefit major stakeholders and policy makers in the Data Science sector. The various analysis, findings and discussions outlined in this paper will serve as a guide in enabling major positive changes in the industry and sub-sectors.

Secondly, the paper is also beneficial to the organizations used for the research. Since first hand data was gotten and analyzed from the organization, they stand a chance to benefit directly from the findings of the study in respect to their various organizations. These findings will fast track growth and enable productivity in the organizations used as a case study.

Finally, the paper will serve as a guide to other researchers willing to research further into the subject matter. Through the conclusions, limitations and gaps identified in the subject matter, other student and independent researchers can have a well laid foundation to conduct further studies.

1.7 Scope of the Study

The study is delimited to MTN Lagos. Findings and recommendations from the study reflects the views and opinions of respondents sampled in the area. It may not reflect the entire picture in the population.

1.8 Limitations of the Study

The major limitations of the research study are time, financial constraints and delays from respondents. The researcher had difficulties combining lectures with field work. Financial constraints in form of getting adequate funds and sponsors to print questionnaires, hold Focus group discussions and logistics was recorded. Finally, respondents were a bit reluctant in filling questionnaires and submitting them on time. This delayed the project work a bit.

1.9 Organization of the Study

The study is made up of five (5) Chapters. Chapter one of the study gives a general introduction to the subject matter, background to the problem as well as a detailed problem statement of the research. This chapter also sets the objectives of the paper in motion detailing out the significance and scope of the paper.

Chapter Two of the paper entails the review of related literature with regards to corporate governance and integrated reporting. This chapter outlines the conceptual reviews, theoretical reviews and empirical reviews of the study.

Chapter Three centers on the methodologies applied in the study. A more detailed explanation of the research design, population of the study, sample size and technique, data collection method and analysis is discussed in this chapter.

Chapter Four highlights data analysis and interpretation giving the readers a thorough room for the discussion of the practical and theoretical implications of data analyzed in the study.

Chapter Five outlines the findings, conclusions and recommendations of the study. Based on objectives set out, the researcher concludes the paper by answering all research questions set out in the study.

 

1.10         Definition of Terms

 

1. 1.  Data Analyst:

A professional responsible for collecting, organizing, and analyzing data to identify patterns and trends that help organizations make informed decisions.

 

1. 2.  Organizational Performance:

 

The ability of an organization to achieve its goals and objectives efficiently and effectively, often measured through key performance indicators (KPIs) including, but not limited to, profitability, gross profit, return on assets, return on assets, return on equity, return on sales, sales growth, market share, share price, sales growth, export growth, liquidity and operational efficiency.

 

1. 3.  MTN Lagos:

Refers to the Lagos branch of MTN, a leading telecommunications company in Nigeria, which serves as the case study for this research.

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