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Using historical data to explore transactional data quality of an African power generation company.
- Source :
- South African Journal of Information Management; Jan2020, Vol. 22 Issue 1, p1-12, 12p
- Publication Year :
- 2020
-
Abstract
- Background: In developing countries, despite large public companies' reliance on master data for decision-making, there is scant evidence to demonstrate their effective use of transactional data in decision-making because of its volatility and complexity. For the state-owned enterprise (SOE) studied, the complexity of generating high-quality transactional data manifests in relationships between customer call transactional data related to an electricity supply problem (captured by call centre agents, i.e. data creators) and technician-generated feedback (i.e. data consumers). Objectives: To establish the quality of customer calls transactional data captured using source system measurements. To compare this data set with field technicians' downstream system transactions that indicated incorrect transactional data. Method: The study compared historical customer calls transactional data (i.e. source system data) with field technician-generated feedback captured on work orders (i.e. receiving system) in a power generation SOE, to ascertain transactional data quality generated and whether field technicians responded to authentic customer calls exclusively to mitigate operational expenses. Results: Mean values of customer call transactional data quality from the source system and technician-generated feedback on work orders varied by 1.26%, indicating that data quality measurements at the source system closely resembled data quality experiences of data consumers. The SOE's transactional data quality from the source system was 80.05% and that of historical data set from evaluating feedback was 81.31% – percentages that exceeded average data quality measurements in literature. Conclusion: Using a feedback control system (FCS) to integrate feedback generated by data consumers to data creators presents an opportunity to increase data quality to higher levels than its current norm. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1560683X
- Volume :
- 22
- Issue :
- 1
- Database :
- Supplemental Index
- Journal :
- South African Journal of Information Management
- Publication Type :
- Academic Journal
- Accession number :
- 148389102
- Full Text :
- https://doi.org/10.4102/sajim.v22i1.1130