A Data Governance lens on active research data management

Ms Dianne Brown1,Ms Anitha Kannan1, Mr Chris Mac Manus1, Mr Andrew Runting1, Mat Ishac1, Jo Dalvean1

1Monash University, Australia

Research Data Management (RDM) and Data Governance are two key issues in the research landscape.  Done well, it provides confidence to participants while not creating impediments for researchers.  Much of the discussion around RDM is focussed on what happens to research data at the end of the lifecycle.  Similarly, data governance has been focussed on issues around who sets the rules about data, rather than extending to questions of what those rules should be and how they are monitored.

Michael Porter introduced the concept of a value chain in the 1980s.  A value chain “disaggregates an organisation into its strategically relevant activities”.  It has been used extensively since, to provide a systematic way of examining activities to improve management practice.  This value chain approach was applied to the research process – the “active” phase – to provide a structured framework for decision making in how research data is managed.

Seven value chain steps were identified that encompassed all the activities involved in managing data during research, from the initial activities of deciding why the data was being collected through to the final reporting of the data.  Governance rules were then developed within this framework.  This has been applied across a variety of research and used to develop tools such as a data dictionary template and to describe the capability of services to collect, store, analyse and share data.

A value chain approach to research data activities can be an effective way of improving data practice and hence its governance within the research environment.


To come


Oct 15 2021


2:20 pm - 2:40 pm

Local Time

  • Timezone: America/New_York
  • Date: Oct 14 2021
  • Time: 11:20 pm - 11:40 pm