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.
Highlights
Master data, which capture core information about an organisation’s stakeholders, products and relationships amongst them (Haneem, Kama & Kazmi 2016), have been the staple discourse for explaining organisational operations (Infovest 2018)
To establish the quality of transactional data, http://www.sajim.co.za we examined the coherence of data generated by an African power generation and distribution company (APGDCO1) from two fronts: data generated from customer calls received at the call centre and technician feedback on customer transactions they executed
Customers would wait longer for their supply to be restored because of invalid electricity supply problem (ESP) calls receiving undue attention. The variances between these perspectives on data demonstrate that transactional data constitute large, complex, unstructured data sets that are hard to deal with using conventional tools and techniques and reflect public institutions’ incapacity to optimally unlock value from such data (OECD 2016)
Summary
Master data, which capture core information about an organisation’s stakeholders, products and relationships amongst them (Haneem, Kama & Kazmi 2016), have been the staple discourse for explaining organisational operations (Infovest 2018). The concentration on master data has been accentuated by the recognition of big data as a strategic asset for organisations (Hagerty 2016) and increased business and data management capabilities in the digital economy (Bärenfänger, Otto & Gizanis 2015) These capabilities manifest in advanced analytics tools’ capacity to generate insightful business information and enhance the provision of digital services to customers (Organisation for Economic Co-operation and Development [OECD] 2016). Such capabilities are evident in digital technologies that avail and pool masses of data from multiple modes to one central place or distributed locations for in-depth analysis. 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)
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