The rapid emergence of new data technology is having a major impact on the public sector. For effective and appropriate use of data technology, it is crucial that insights about data-driven work do not linger with a small group of enthusiasts, but reach the entire organization. How can organizations do this? Albert Meijer and Krista Ettlinger discuss insights from research on the importance of social learning around datafication in public administration.(1)
To create public value and improve public services, government organizations are increasingly going data-driven. This requires a whole set of organizational changes: new expertise, new collaborations between departments, new procedures and, above all, a new mindset. This is why there is increasing talk of digital transformation, a totally new way of working within the context of an entirely new type of organization.
With the sweeping nature of digital transformation, this requires a process of social learning. We see that an increasing number of government organizations are open to insights about the data society from data specialists and consultants - as well as scientists and artists - but they are not yet successful in translating these insights into changing practices. The insights from external parties often remain on the "outside" of the organization, among a small group of enthusiasts. The "inside" of the organization does not change and just keeps doing what they have always done.
To understand what is needed to ensure that not only the "outside" of the organization learns but also the organization at large, we looked specifically at how government organizations can learn from scientific research. As such, this research is a response to our own frustration. Over the years we have noticed that insights about data-driven work from scientific research are warmly received but then have limited impact. Why is this and what can be done to increase the knock-on effect of relevant insights? Based on our action research with these local and regional government organizations, we show what government organizations can do to broaden social learning about data-driven working so that it contributes to digital transformation.
The key insight from our research is that public organizations seem to assume that social learning occurs naturally, when in fact there are strong mechanisms that prevent these learning processes.
A key characteristic of public organizations is bureaucracy. Organizations operate according to a strict set of rules and procedures, where decisions must receive approval at multiple levels of the organization before action can be taken. While this hierarchical system has important functions, for example for the allocation of responsibilities, it slows down the decision-making process and impedes social learning.
Public organizations are judged by many demands. They are held accountable by legislators and citizens alike and are judged not only on their performance, but also on how well they uphold public values such as equality and justice. This leads to a host of conflicting goals for which organizations are held accountable. These conflicting goals also hinder social learning because delicate balances between goals are carefully guarded.
Our research shows that the bureaucratic organization is designed for stability and resists change. Without explicit attention to organizational learning, no new insight will reach the "inside" of the organization.
To truly learn about data-driven work, organizations need to build organizational learning capacity. Translating insights into new ways of working is no easy task: it requires shaping mechanisms that respond to bureaucratic organizations' resistance to change. We mention three points we found in our research.
Horizontal working within the organization with other departments and teams improves an organization's ability to learn. This ability is important to break the slow decision-making process of bureaucratic organizations and to prevent knowledge from getting stuck in silos within the organization. In our research, we saw that in the organizations studied, decisions, especially those related to funding, had to go through several layers of decision-making. However, the Amersfoort municipality was also able to overcome these difficulties by working organization-wide and bringing together people from different sectors with different areas of expertise. This allowed knowledge and experience to be shared more quickly within the organization and circumvented delays in the vertical decision-making process.
Government organizations are under great pressure and face many, often conflicting, goals. Public sector employees often have a high workload. To ensure that all these pressures and goals align with the social learning goals around datafication, organizations need to give ownership of social learning to a specific program or team within the organization. Our research shows how, in Amersfoort, team managers were invited to choose which topics within data-driven work aligned with their team and project goals. This meant that team goals and social learning goals were aligned. This made social learning a means for team leaders to achieve their team goals and took ownership of this process. It also ensured that this social learning did not become another conflicting goal that increased their workload.
A leadership-supported vision of data-driven work helps make time and resources available for these social learning processes. Without a vision from the top of the organization, the knowledge remains with a limited group of employees within the organization. In the municipality of Gouda, we saw the importance of a vision supported by the leadership. This organization had developed a vision of what it wanted to achieve over the next three years in terms of data-driven work. Because this strategic vision was endorsed by the leadership, there was also widespread awareness within the organization. This meant that employees within the organization prioritized participation in social learning about data-driven working, for example, by setting aside time in their schedules to attend workshops and other educational activities on these topics.
In summary, social learning around datafication requires public organizations to build the capacity to learn from insights from the outside world. Social learning about data fication requires three things: working horizontally across the organization, taking ownership of social learning, and communicating a clear vision of data-driven work. These steps are necessary to move from activities of a small group of data-driven work enthusiasts to a digital transformation.
Footnotes
(1) This article is based on recent research that took place within the Datawerkplaats. The Datawerkplaats is a research collaboration between Utrecht University and the municipalities of Gouda, Almere, Amersfoort, and Zuidplas, and the provinces of Utrecht and South Holland. Read more about the Data Workshop here: https://dataschool.nl/samenwerken/datawerkplaats/.
