Chapter One — 1.1 Background to the Study

Over the past decade, the relationship between data cleaning and preprocessing practices and decision-making accuracy has become a subject of considerable debate among scholars and industry practitioners alike, particularly within the context of Selected Commercial Banks in Nigeria where operating conditions differ markedly from more developed markets.

Selected Commercial Banks in Nigeria presents a useful setting for examining this relationship precisely because the conditions there — structural, regulatory, and behavioural — differ from those typically assumed in the broader literature, most of which draws on evidence from more developed economies.

1.2 Statement of the Problem

While data cleaning and preprocessing practices is widely discussed in policy and industry circles, empirical evidence on its actual effect on decision-making accuracy within Selected Commercial Banks in Nigeria remains sparse and, in places, contradictory. This lack of localized, rigorous evidence makes it difficult for decision-makers to know with confidence whether current approaches to data cleaning and preprocessing practices are helping or hindering decision-making accuracy — a gap this study sets out to close.

1.3 Objectives of the Study

1. To examine the effect of Data Cleaning and Preprocessing Practices on decision-making accuracy in Selected Commercial Banks in Nigeria.
2. To assess the extent to which data cleaning and preprocessing practices influences decision-making accuracy within the study area.
3. To identify the challenges associated with data cleaning and preprocessing practices in relation to decision-making accuracy.
4. To recommend strategies for optimizing data cleaning and preprocessing practices in order to improve decision-making accuracy.

1.4 Research Questions

1. What is the effect of data cleaning and preprocessing practices on decision-making accuracy in Selected Commercial Banks in Nigeria?
2. To what extent does data cleaning and preprocessing practices influence decision-making accuracy within the study area?
3. What challenges are associated with data cleaning and preprocessing practices in relation to decision-making accuracy?
4. What strategies can be adopted to optimize data cleaning and preprocessing practices in order to improve decision-making accuracy?

1.5 Significance of the Study

This study is significant to a range of stakeholders. For policymakers and regulators, the findings offer evidence to guide the design of frameworks that support healthier outcomes around decision-making accuracy. For managers and practitioners within Selected Commercial Banks in Nigeria, the study provides practical insight into how data cleaning and preprocessing practices can be better managed. Finally, it contributes to the academic literature on data analysis by extending existing knowledge into a specific empirical context, and offers a reference point for future researchers.

1.6 Scope of the Study

The study is limited to an examination of Data Cleaning and Preprocessing Practices and its relationship with decision-making accuracy within the context of Selected Commercial Banks in Nigeria. It reflects a BSc-level scope of analysis and relies on data and perspectives available within that scope; generalizing the findings beyond this specific context should therefore be done with appropriate caution.