Financial cost implications of inaccurate extraction of transactional data in large African power distribution utility

Abstract

In view of the increasingly competitive business world, prudent spending and cost recovery have become the driving force for the optimal performance of large public organizations. This study, therefore, examined the cost-effectiveness of a Large Energy Utility (LEU) in a Southern African country by exploring the relationship between extraction of transactional customer data (that is, data on the servicing and repairing energy faults) and the Utility’s recurrent expenditure (especially its technicians’ overtime bill). Using data mining, a large corpus of the LEU Area Centre (AC) data was extracted to establish the relationship between transactional customer data extraction including capture and the financial cost of the LEU (e.g., recurrent expenditure on overtime bill). Results indicate that incorrect extraction and capturing of transactional customer service data has contributed significantly to the LEU’s escalating overtime wage bill. The data also demonstrate that the correct extraction and capturing of transactional customer service data can positively reduce the financial costs of this LEU. The paper demonstrates one of the few attempts to examine the effects of correct data extraction and capture on the financial resources of struggling large public energy utility. Using Resource Based Theory, the study also demonstrates how technicians’ feedback on incorrect transactions enhances the measurement of inaccurate transactional data albeit a burgeoning overtime wage bill incentives.