Abstract

In today's data-driven world, the quality of data is critical for making informed decisions. This paper explores how geolocation techniques enhance granular data, focusing on the Haversine formula to calculate precise distances between geographical coordinates. To demonstrate this method, we examined household electric consumption patterns across Peninsular Malaysia using utility data and other relevant administrative records. Our study employs a diverse set of techniques, including spatial analysis, geocoding, the Haversine formula, and advanced data analytics. By adding geolocation attributes to traditional datasets, we assess the impact on data quality, specifically in terms of accuracy, completeness, and temporal relevance. Furthermore, we explore how geolocation-enhanced data can reveal hidden patterns and correlations not apparent in non-spatial datasets. The results of our study indicate that the inclusion of geolocation data using the Haversine formula greatly enhances the level of detail in the dataset, resulting in more precise and contextually relevant information. This enhancement facilitates more accurate targeting in marketing (awareness) campaigns, improved allocation of resources in government programmes, and enhanced effectiveness in environmental monitoring. In conclusion, we discuss the implications of these findings for future research and practical applications. We strongly recommend the wider implementation of geolocation techniques, specifically the Haversine approach, in strategies aimed at improving data quality.