{"title":"Improving Government Decision Making in Africa through Digital Data Collection","authors":"Johan Breytenbach, Mahier Hattas","doi":"10.1109/ICIM49319.2020.245372","DOIUrl":null,"url":null,"abstract":"Governments look towards national statistical organizations (NSO) for high quality data with which to improve decision making processes. Digital data collection (DDC) offers NSOs in Africa possible, albeit partial, solutions to several current performance and profitability concerns as they struggle to meet public sector decision makers’ requirements. Perceived potential benefits of DDC methods over paper-based methods include increased speed of data collection, increased data accuracy, timeous data availability, higher data quality, data security, and lower costs of data collection. Secondary benefits include better informed policies from governmental departments reliant on NSO’s for decision support data. This article presents data from two iterations of a large scale DDC implementation in South Africa – the first of its kind in Africa. Speed, accuracy, availability, quality, and costs of data collection receive attention as generalizable themes. Findings include: poor initial speed of DDC interviews followed by a significant speed increase as interviewers master DDC technology and skills, the importance of training within DDC processes, proof of higher accuracy in geographic data capturing, real time availability of data for decision making, a shorter data cleaning and release process, and higher initial costs of mobile devices.","PeriodicalId":129517,"journal":{"name":"2020 6th International Conference on Information Management (ICIM)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 6th International Conference on Information Management (ICIM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIM49319.2020.245372","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Governments look towards national statistical organizations (NSO) for high quality data with which to improve decision making processes. Digital data collection (DDC) offers NSOs in Africa possible, albeit partial, solutions to several current performance and profitability concerns as they struggle to meet public sector decision makers’ requirements. Perceived potential benefits of DDC methods over paper-based methods include increased speed of data collection, increased data accuracy, timeous data availability, higher data quality, data security, and lower costs of data collection. Secondary benefits include better informed policies from governmental departments reliant on NSO’s for decision support data. This article presents data from two iterations of a large scale DDC implementation in South Africa – the first of its kind in Africa. Speed, accuracy, availability, quality, and costs of data collection receive attention as generalizable themes. Findings include: poor initial speed of DDC interviews followed by a significant speed increase as interviewers master DDC technology and skills, the importance of training within DDC processes, proof of higher accuracy in geographic data capturing, real time availability of data for decision making, a shorter data cleaning and release process, and higher initial costs of mobile devices.