The Influence Of Transactional Data Quality On Monetary Savings Of Eskom Distribution, Free State.

Abstract

Eskom, South Africa’s major electricity generation, transmission and distribution utility, is currently in a very difficult financial position. Electricity sales are declining, debt and primary energy costs are soaring, existing customers owe Eskom billions in debt while tariff increases on electricity sales are too low to recover costs for the generation, transmission and distribution of electricity. Consequently, Eskom is examining all its operations in order to identify areas where possible savings can be realised. Given the centrality of data in both informing prudent corporate decision making and advancing cost saving mechanisms, it is undeniable that inaccurate data can lead to inappropriate decisions and costly corporate blunders. There is sufficient evidence to demonstrate that an improvement in data quality can increase a company’s turnover by approximately 15%. Nevertheless, the reality is that data quality improvement strategies tend to focus on master data. As a result, the researcher sought to establish the exact effects that improvements in transactional data quality could have on monetary savings of Eskom Distribution Free State. Drawing on the aforementioned electricity utility, a survey was conducted on technical field staff’s perspectives regarding transactional data quality of customer calls relating to electricity supply problems (ESP) received from its call centre. In addition to the survey, historical transactional data on the entity’s ESP customer calls were also analysed to establish the influence of data quality on cost savings of this entity. The survey was conducted on 303 Eskom technicians during 2017. The historical data sets for the period April 2012 to March 2017 were also analysed. Since the assessments on monetary impact of the mentioned transactions are carried by Eskom rather than the customer, the perceptions of customers were not considered in this study. It was contended that the individuals directly involved in assessing the monetary effects of data quality would be ideally positioned to have logical and credible opinions on this subject rather than customers who were considered to have limited knowledge on this subject. The results from the historical data analysis using mean, frequency distribution, cross tabulation, correlation analysis of survey data, and mean distribution, regression analysis and correlation analysis for historical data, revealed potential monetary savings of 17.18% arising from avoidable costs on transactions related to ESP customer calls. These monetary savings were dependent on Eskom’s ability to increase its transactional data quality on ESP customer calls from 81.31% to 100%. While it was acknowledged that avoidable costs could only be calculated from quantifiable operational costs, savings would potentially increase if the effects of improved customer service, faster supply restoration times and work hours saved to perform preventative maintenance to reduce overall fault volumes were quantified in monetary terms. It was also noted that if the costs of increasing data quality were lower than the 17.18% monetary savings potential established in the study, then such data quality improvement strategies would improve Eskom’s financial position. Furthermore, descriptive analysis on survey results revealed that an improvement in customer call transactional data quality at the source has the potential of creating savings of up to 47.7% for transactions related to customer calls requesting service for an ESP. This finding was however not supported by inferential analysis. Nonetheless, the study recommends that Eskom should continually identify and investigate high value transactional data quality as it offers significant savings potential through cost avoidance.