Chapter One — 1.1 Background to the Study

Data Cleaning and Preprocessing Practices has increasingly attracted the attention of researchers, regulators, and practitioners concerned with decision-making accuracy. This growing interest reflects the recognition that data cleaning and preprocessing practices does not operate in isolation, but interacts with a wider set of institutional and market conditions found within Ogun State.

Ogun State 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

Despite a growing body of literature on data cleaning and preprocessing practices, there remains limited consensus on the precise nature of its relationship with decision-making accuracy, particularly within Ogun State. Many organizations continue to make decisions about data cleaning and preprocessing practices without a clear, evidence-based understanding of how those decisions ultimately affect decision-making accuracy. This gap between practice and empirical understanding is the central problem this study seeks to address.

1.3 Objectives of the Study

1. To examine the effect of Data Cleaning and Preprocessing Practices on decision-making accuracy in Ogun State.
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 Ogun State?
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

Beyond its academic contribution to the field of data analysis, this study has practical value for management teams within Ogun State seeking to understand how data cleaning and preprocessing practices translates into measurable outcomes around decision-making accuracy. It is equally useful to students and future researchers looking for a localized empirical reference on this relationship.

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 Ogun State. 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.