A guest post by Michael Canares based on the paper “Enhancing Citizen Engagement with Open Government Data”.
As an educator and an adult learning facilitator for more than a decade, this question has been bugging me since I started working as a researcher on open data. The ODDC research projects highlighted the fact that if we want to increase open data use, then we need to build the capacity of intermediaries and users so that they can access and use open data for public benefit.
Currently, those that provide open data training to increase open data demand and uptake have organizations from the private and the non-profit sector as primary audiences. The trainings provided range from basic open data introductory courses, to courses on accessing, using, visualizing, and creating narratives based on open data. The School of Data (run by Open Knowledge), for example, runs trainings on open data fundamentals, data cleaning, data extraction, data exploration, geo coding, working with budgets and spending data, and data journalism. The World Bank have been running Open Data Bootcamps to increase open data literacy in countries with on-going open government initiatives. The Open Data Institute also offers similar trainings on creating narratives from open data, using open data to win public sector business, and finding the value in open data. These courses also range from half day to five days, depending on the level of skills that trainees have and the level of skills they would like to acquire. The Open Data Labs Jakarta of the World Wide Web Foundation on the other hand, do not have specific open data training modules but customize its training program depending on the needs of the audience of capacity building intervention. These models, in theory, focus on action learning with presentation inputs and hands-on learning activities.
But will this really be the kind of things that we would like to do in the long term? Will it significantly address capacity gaps?
With support provided through the World Wide Web Foundation ‘Open Data for Development Fund’ to support the ‘Open Government Partnership Open Data Working Group’ work, through grant 107722 from Canada’s International Development Research Centre, my team at Step Up Consulting Services embarked on an action research project to answer this question. First, we conducted a Training Needs Analysis (TNA) among representatives from CSO, media, business groups in the provinces of Bohol and Negros Oriental to serve as basis for a capacity building program.
Two training programs were developed out of the results of the training needs analysis. One approach was classroom learning/teaching activity, while the other was based on targeted mentoring. For classroom learning, the training was conducted with respondent organizations with a re-entry action plan at the end where trainees were required to plan how they would use skills learned in engaging with government data for two months. This action plan became the basis of monitoring.
The second approach took the form of a mentoring lab. Civil society groups were assisted in identifying the open government data that they would like to work with, teach them the skills on how to access and use the data and come up with a plan of undertaking the open data engagement in two months time. Mentoring support was provided all throughout this phase.
The first method was implemented in Negros Oriental while the second method was implemented in Bohol. A learning workshop was conducted at the end of the two month period to see what has been accomplished, what challenges were met, and what lessons can be learned from the process.
The experiences of CSOs in the provinces of Bohol and Negros Oriental point to important lessons in the design, implementation, and evaluation of capacity building programs on open data, more particularly for civil society organizations in the global South. These lessons are not new or novel – they are successful practices that have been tested in other capacity building interventions other than open data that may have been forgotten or ignored in the desire to fast-track interventions or probably because of inappropriate baseline assumptions. Some of these lessons are presented below:
- Baseline condition should inform capacity building approach.
To start a capacity building intervention, whether it is training or mentoring, assessing baseline conditions of trainees is important. This baseline condition does not only refer to the trainees themselves, as captured through training needs assessment tools, but also to the condition of the organizations that will participate in the trainings. This is particularly true for open data skills trainings, where people’s skills are not only important but also hardware (e.g. computers) and connectivity (e.g. internet). When conducting open data trainings for people from civil society organizations who may be interested to learn skills it is important to note some may still be without the appropriate infrastructure to be able to make use of the trainings.
- Data use is dependent on data supply.
This finding is expected and not surprising. CSOs would like to work on data related to their advocacies or development. As such, trainings or mentoring should be based on the types of data that CSOs are most interested to work with and that they have the capacity to understand. Using examples that are far from the priority advocacies or areas of work of CSOs will be counterproductive. What is helpful is when CSOs will, for theory discussion and practical work, use actual data of interest in the training or mentoring sessions.
- Open data use requires accessible and stable internet connection.
One of the primary challenges of several CSOs in both Negros Oriental and Bohol was the inability to download data from government portals and use online open data tools. This is not true for CSOs based in city centers where internet infrastructure is relatively stable. However, this finding also points out that open data intermediaries may need to engage in offline formats to ensure that information generated from open data will reach people without access to internet.
- Open data skills are important but insufficient.
Most of demand-side capacity building interventions on open data, at least in the Philippines, concentrates on open data and open data skills (e.g. downloading, to scraping, cleaning, and visualization). However, these are just the “what” and “hows” of open data, respectively. Sometimes, these trainings include the “why” of open data. Participants, however, need more information other than these.They need to know the context of open data in the country and in their respective local areas. They want to see specific examples of the benefits of engaging with governments through open data that are not available to them using their customary means of public participation. They need to know what they can do with the data that they were able to visualize, and how they can use it to improve their collaboration with governments. These are contextual, issue-based, and location-sensitive information that provides an overall context of open data as well as its overarching purpose. As such, while open data skills experts are wanting, subject matter experts are also necessary.
- Outcomes, and not just Outputs, Prove Capacity Improvements
Often, capacity building initiatives measure outputs to prove success. In results measurement language, outputs are immediate results of an activity. For example, if open data training is completed, then an immediate result will be number of individuals trained in open data. If we use this as a basis for measurement, then any open data training conducted will be assessed as successful. But outputs are insufficient to gauge capacity improvements. Outcomes, those we consider as higher order results, are more indicative of a capacitated learner, and in this case, a capacitated CSO in open data.
- Training or mentoring is just scratching the surface and building capacity should do more than just these. There were several reasons why CSOs in Bohol and Negros Oriental succeeded or failed in producing concrete products out of an open data capacity building program, but several of these are organizational in nature. For example, several organizations were not able to produce the agreed post-training/mentoring outputs because there are competing priorities within the organization affecting work deadlines. On the other hand, those that were able to produce meaningful outputs were those where leaders were committed to the process, where the open data work provides an enabling mechanism to their core work or business, and those that were able to dedicate resources, people included.
Kaplan (2000) argues that for capacity building interventions to generate results, there is a need to move interventions from the tangible to the intangible. By this he means that while capacity building necessitates development of skills and the acquisition of material resources (the tangible ones), the production of outputs or the changes in organizational practices necessitates changes in organizational attitude, vision and strategy (the intangible ones). Training or mentoring on open data only changes the tangible. Without a corresponding shift in leadership priorities or without having open data add value to CSO’s core business, then actual CSO-led outputs cannot be produced. This is echoed in change management literature, where change readiness is characterized by attitude, conditions, and resources (Pearson, 2011). Attitude refers to organizational and individual motivation while condition refers to mandates, structures and systems. Resources are about people’s knowledge and skills, as well as organizational financial and technical assets. Without strong motivation and a clear mandate and structure, skills learned may not lead to how CSOs view and use data even with the existence of required resources.
The advantage of mentoring processes over training is the closer relationship between mentor and mentee. While providing mentoring support at the workplace of the CSOs, the mentor is able to influence organizational leadership and motivation and people’s attitude. Continuous working with mentee organizations may lead to changes in structure and reallocation of resources. But these results will depend on the quality of the mentor and the amount of time he or she is able to devote to the organization. Training will hardly be able to provide these kinds of support.
So shall we continue conducting trainings on open data to build data use and achieve impact?
Yes, but only when these are context-relevant, strategic, outcome-focused, and comprehensive.
- Context-relevant. The capacity building program needs to be relevant to the condition of the CSOs and the individual needs of learners. It should take into account the needs and assets of organizations and their team, including, among others, the mandate of the organization, the availability and accessibility of technology, the availability of data they are interested in, and their experience in utilizing data in their advocacy and development work. In this case, a good understanding of CSOs and the environment where they operate is critical.
- Strategic. Trainings should be conducted with a long-term view – of ensuring use and actual impact not only on the organization but also on the constituencies that they serve. Short-term, sporadic, one-time buzz trainings, or off-the-shelf training programs will not yield to actual use that will show the economic, political, and social power of open data.
- Outcome- focused. Open data capacity building providers should focus on higher-order results like changes in practices and behavior of organizations and their staff members or the actual production of outputs that benefits citizens and communities. Providers should not be satisfied with outreach, or the number of people trained, but with outcome, or how the capacity building program change the way organizations do things or how they participate in governance.
- Comprehensive. Open data capacity building programs should not only focus on concepts or skills. They should focus also on a whole-of-organization awareness, appreciation, and motivation to use data. Thus, trainings should change attitudes, systems, and not only resources and must facilitate the organization’s collective recognition of the value that open data can bring to the achievement of its vision, fulfillment of its strategies, and the effectiveness, efficiency, and sustainability of its operations.
What do you think? How can we develop training that truly contributes to capacity building. Join the debate in the comments, or in the ‘Capacity Building for All’ action session.