{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/13217"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/13217","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Towards health management intelligence: a case study from South Africa","abstract":"Over the last two decades various information management processes have evolved in South Africa’s public health system. Most notably a self-service business intelligence tool has emerged at the national level which has been supported by the presence of a Routine Health Information System. Corporate business intelligence and its underlying process are well documented but not in the public health domain. The emergence of this tool and the underlying support processes are investigated in a longitudinal case study. Complex adaptive systems theory is used to demonstrate the evolutionary path of business intelligence processes according to four key areas, namely data quality, master data management, data warehousing and analytics. These processes have developed out of an information management culture that has been nurtured by a participatory approach which required an attractor: the improvement of health services through the collection and use of information. The evolution of these processes took place through a bottom up approach that relied on distributed control structures, self-organization and regular engagement within the CAS that is South Africa’s public health system. This created an environment in which information quality practices and master data management processes enabled the continued production of data for warehousing and analytics. Findings will show how business intelligence processes have evolved within a public health setting to the point that they are supported by a new policy that ensures data integrity, presence, quality and use processes. These processes have developed and stabilized over many iterations and have enabled the establishment of a country level self-service business intelligence platform for health managers.","abstract_html":"Over the last two decades various information management processes have evolved in South Africa’s public health system. Most notably a self-service business intelligence tool has emerged at the national level which has been supported by the presence of a Routine Health Information System. Corporate business intelligence and its underlying process are well documented but not in the public health domain. The emergence of this tool and the underlying support processes are investigated in a longitudinal case study. Complex adaptive systems theory is used to demonstrate the evolutionary path of business intelligence processes according to four key areas, namely data quality, master data management, data warehousing and analytics. These processes have developed out of an information management culture that has been nurtured by a participatory approach which required an attractor: the improvement of health services through the collection and use of information. The evolution of these processes took place through a bottom up approach that relied on distributed control structures, self-organization and regular engagement within the CAS that is South Africa’s public health system. This created an environment in which information quality practices and master data management processes enabled the continued production of data for warehousing and analytics. Findings will show how business intelligence processes have evolved within a public health setting to the point that they are supported by a new policy that ensures data integrity, presence, quality and use processes. These processes have developed and stabilized over many iterations and have enabled the establishment of a country level self-service business intelligence platform for health managers.","abstract_has_math":false,"creators":["Rowles , Gregory Thomas"],"institution":"Department of Information Systems","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["van Belle, Jean-Paul","Hart, Mike"],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014","date_published":"2014","updated_at":"2026-07-22T22:22:51Z","subjects":[],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/13217","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["van Belle, Jean-Paul","Hart, Mike"]},{"key":"dc:creator","label":"Author","values":["Rowles , Gregory Thomas"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2015-07-01T08:55:02Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2015-07-01T08:55:02Z"]},{"key":"dc:date.issued","label":"Date","values":["2014"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Information Systems"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cape Town"]},{"key":"dc:type","label":"Dc Type","values":["Master Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["MCom"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/13217"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Over the last two decades various information management processes have evolved in South Africa’s public health system. Most notably a self-service business intelligence tool has emerged at the national level which has been supported by the presence of a Routine Health Information System. Corporate business intelligence and its underlying process are well documented but not in the public health domain. The emergence of this tool and the underlying support processes are investigated in a longitudinal case study. Complex adaptive systems theory is used to demonstrate the evolutionary path of business intelligence processes according to four key areas, namely data quality, master data management, data warehousing and analytics. These processes have developed out of an information management culture that has been nurtured by a participatory approach which required an attractor: the improvement of health services through the collection and use of information. The evolution of these processes took place through a bottom up approach that relied on distributed control structures, self-organization and regular engagement within the CAS that is South Africa’s public health system. This created an environment in which information quality practices and master data management processes enabled the continued production of data for warehousing and analytics. Findings will show how business intelligence processes have evolved within a public health setting to the point that they are supported by a new policy that ensures data integrity, presence, quality and use processes. These processes have developed and stabilized over many iterations and have enabled the establishment of a country level self-service business intelligence platform for health managers."]},{"key":"dc:title","label":"Title","values":["Towards health management intelligence: a case study from South Africa"]}]}],"canonical_facts":{"dc:contributor.advisor":["van Belle, Jean-Paul","Hart, Mike"],"dc:creator":["Rowles , Gregory Thomas"],"dc:date.accessioned":["2015-07-01T08:55:02Z"],"dc:date.available":["2015-07-01T08:55:02Z"],"dc:date.issued":["2014"],"dc:description.abstract":["Over the last two decades various information management processes have evolved in South Africa’s public health system. Most notably a self-service business intelligence tool has emerged at the national level which has been supported by the presence of a Routine Health Information System. Corporate business intelligence and its underlying process are well documented but not in the public health domain. The emergence of this tool and the underlying support processes are investigated in a longitudinal case study. Complex adaptive systems theory is used to demonstrate the evolutionary path of business intelligence processes according to four key areas, namely data quality, master data management, data warehousing and analytics. These processes have developed out of an information management culture that has been nurtured by a participatory approach which required an attractor: the improvement of health services through the collection and use of information. The evolution of these processes took place through a bottom up approach that relied on distributed control structures, self-organization and regular engagement within the CAS that is South Africa’s public health system. This created an environment in which information quality practices and master data management processes enabled the continued production of data for warehousing and analytics. Findings will show how business intelligence processes have evolved within a public health setting to the point that they are supported by a new policy that ensures data integrity, presence, quality and use processes. These processes have developed and stabilized over many iterations and have enabled the establishment of a country level self-service business intelligence platform for health managers."],"dc:identifier.uri":["http://hdl.handle.net/11427/13217"],"dc:language.iso":["eng"],"dc:publisher.department":["Department of Information Systems"],"dc:publisher.institution":["University of Cape Town"],"dc:title":["Towards health management intelligence: a case study from South Africa"],"dc:type":["Master Thesis"],"dc:type.qualificationlevel":["Masters"],"dc:type.qualificationname":["MCom"]},"updated_at":"2026-07-22T22:22:51Z"}