Behavioural Data Governance

Program Lead: Professor Arthur H.M. ter Hofstede  

Data governance is essential for any company that depends on data for decision-making and ensuring behavioural adoption and adherence of policies is crucial. This program is concerned with the practical implementation of abstract high-level data governance concepts. Our program uses a wide range of methods ranging from machine learning, process mining, and data modelling, mixed-method and gamification to qualitative and quantitative methods.

  • What are practical initiatives for policy-makers, business and the community to improve behavioural data governance and trust?
  • How can we make data fit for purpose for strategic decision making?
  • How can the ramifications of data quality problems be understood and quantified?
  • How can data quality problems be identified and resolved?

The Behavioural Data Governance program at BEST will be approached through three core themes: (1) Decision Governance; (2) Data Governance for Public Sector Transparency; (3) Trust in Data.

Theme 1: Decision Governance

Lead: Dr Kenan Degirmenci

Decision-making is a critical component for organisations, where action items are selected among alternative options to successfully achieve business goals (Mukherjee, 2022). Decision theory helps organisations to make decisions effectively, where decision-making is classified under two categories: descriptive decision-making (how and why people decide the way they do), and normative decision-making (how people should decide with logical consistency) (Tang et al., 2018).

In this theme, we build on decision theory and propose decision governance as a new business capability to provide value from organisational decisions. While data governance is a framework of accountabilities and processes for making decisions and monitoring the execution of data management (Ladley, 2019), decision governance provides a framework of rules, standards, and policies to discover, define, apply, and monitor organisational decisions.

Theme 2: Data Governance for Public Sector Transparency

Lead: Dr Rehan Syed

Governments in the twenty-first century face a variety of new difficulties, including climate change, technological revolution, rising urbanisation, and an ageing population, all of which make innovation more crucial than ever (Jurisch et al., 2013). It’s no wonder that governments have begun to explore advanced computational power and automation technologies to overcome chronic productivity, efficiency, and transparency issues (Abbas et al., 2021). The concept of algorithms is central to this, and the use of algorithms in government – and more specifically, algorithmic decision-making is gaining popularity globally; albeit, facing many challenges associated with adoption, resource optimisation, governance, and benefits realisation of advanced automation technologies (Coglianese & Lehr, 2019; Ljungholm, 2018; The World Bank, 2018).

Theme 3: Trust in Data

Lead: Dr Kanika Goel

Data is recognised as a strategic asset by organisations around the world. There is a growing need to understand how to maintain and use voluminous data for decision making, improving business operations, and performance. This brings forth the significance of data governance. Data governance is the planning, oversight, and control over management of data and data-related resources (Mosley et al. 2010). It aims at implementing a corporate wide agenda and maximising the value of data assets (Mosley et al. 2010) and is recognised as a new capability for organisations to further their goals (Ladley 2019). The DAMA international framework advocates management of the lifecycle of data creation, transformation, and transmission, to ensure that resulting information meets the needs of the data consumers in the organisation. Data governance entails trust in data, which is essential for people to be able to convincingly use data insights for strategic decision making. Trust in data is the willingness to make oneself vulnerable to another’s actions based on beliefs about the data’s trustworthiness (Bohnet 2010). Despite the importance of data governance, it remains an elusive concept. This theme provides researchers and practitioners an understanding necessary to succeed in implementation of data governance for trust in data at varied organisations.