Original Research

A tool to increase information-processing capacity for consumer water meter data

Heinz E. Jacobs, Kerry A. Fair
South African Journal of Information Management | Vol 14, No 1 | a500 | DOI: https://doi.org/10.4102/sajim.v14i1.500 | © 2012 Heinz E. Jacobs, Kerry A. Fair | This work is licensed under CC Attribution 4.0
Submitted: 27 August 2011 | Published: 11 June 2012

About the author(s)

Heinz E. Jacobs, University of Stellenbosch, South Africa
Kerry A. Fair, GLS Software, South Africa

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Background: Water service providers invoice most South African urban consumers for the water they use every month. A secure treasury system generates water invoices at municipalities’ financial departments. Information about the water usage of customers initially comes from reading the water meters, usually located in gardens near the front boundaries of properties. Until as recently as 1990, the main purpose of the water meter readings was to generate invoices for water usage. There are various treasury systems for this purpose.

Objective: The objective of this research article was to describe the development of Swift, a locally developed software tool for analysing water meter data from an information management perspective, which engineers in the water field generally use, and to assess critically the influence of Swift on published research and industry. This article focuses on water usage and the challenge of data interchange and extraction as issues that various industries face.

Method: This article presents the first detailed report on Swift. It uses a detailed knowledge review and presents and summarises the findings chronologically.

Results: The water meter data flow path used to be quite simple. The risk of breaches in confidentiality was limited. Technological advances over the years have led to additional knowledge coming from the same water meter readings with subsequent research outputs. However, there are also complicated data flow paths and increased risks. Users have used Swift to analyse more than two million consumers’ water meter readings to date. Studies have culminated in 10 peer-reviewed journal articles using the data. Seven of them were in the last five years.

Conclusion: Swift-based data was the basis of various research studies in the past decade. Practical guidelines in the civil engineering fraternity for estimating water use in South Africa have incorporated knowledge from these studies. Developments after 1995 have increased the information processing capacity for water meter data.


water; demand; consumption; management


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