Revisiting Sensemaking: The case of the Digital Decision Network Application (DigitalDNA)




dashbnoards, big data, management information systems, data-use


During this age of data proliferation, heavy reliance is placed on data visualisation to support users in making sense of vast quantities of information. Informational Dashboards have become the must have accoutrement for Higher Education institutions with various stakeholders jostling for development priority. Due to the time pressure and user demands, the focus of development process is often on designing for each stakeholder and the visual and navigational aspects. Dashboards are designed to make data visually appealing and easy to relate and understand; unfortunately this may mask data issues and create an impression of rigour where it is not justified. This article proposes that the underlying logic behind current dashboard development is limited in the flexibility, scalability, and responsiveness required in the demanding landscape of Big Data and Analytics and explores an alternative approach to data visualisation and sense making. It suggests that the first step required to address these issues is the development of an enriched database which integrates key indicators from various data sources. The database is designed for problem exploration allowing users freedom in navigating between various data-levels, which can then be overlaid with any user interface for dashboard generation for a multitude of stakeholders. Dashboards merely become tools providing users and indication of types of data available for exploration. A Design Research approach is shown, along with a case study to illustrate the benefits, showcasing various views developed for diverse stakeholders employing this approach, specifically the the Digital Decision Network Application (DigitalDNA) employed at Unisa.



How to Cite

Archer, E., & Barnes, G. (2017). Revisiting Sensemaking: The case of the Digital Decision Network Application (DigitalDNA). The International Review of Research in Open and Distributed Learning, 18(5).



Research Articles