Challenges facing the open data for resilience community

The following report draws from the expertise of leaders from the fields of Open Data and Disaster & Climate Resilience from around the world to identify the key challenges facing the Open Data for disaster and climate resilience community.

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1. 1 Challenges facing the open data for resilience community In order to build resilient societies, policy-makers and the public must have access to the right data and information to inform good decisions. Decisions such as where and how to build safer schools, how to insure farmers against drought, and how to protect coastal cities against future climate impacts. Sharing data and creating open systems hold promise to promote transparency, accountability, and ensure a wide range of actors are able to participate in the challenge of building resilience. The promise of Open Data, however also has challenges. The following report draws from the expertise of leaders from the fields of Open Data and Disaster & Climate Resilience from around the world to identify the key challenges facing the Open Data for disaster and climate resilience community. These challenges were collected through a series of interviews and two workshops. The first workshop with 50 participants was held in Washington D.C. in March 2014. The second with 100+ participants was held in London at the Understanding Risk conference. The participants of the interviews and workshops included; government agencies, the private sector, development agencies and multilaterals.
2. 2 To facilitate this process a lightweight framework was used, (please see Figure 1). This Figure reflects the cycle of data to action in a real-world context. Data generated from human behaviour and world conditions are collected and interpreted. Insights gained from the interpretation are implemented, and new behaviours encouraged. This, in turn, creates new data, which forms the basis for further analysis, monitoring, and evaluation. Figure 1: Open DRI Framework Clearly defined challenges provide a broad rallying point for members of the community and also a practical tool to guide day to day work. There remains room to refine, validate, and better understand them, but the below is a good start. The bigger task is to begin to meet these challenges. Ideally partnerships like OpenDRI will help us—and inspire us—to advance.
3. 3 OPEN DATA ALONE DOES NOT EQUAL IMPACT. In thinking about how to ensure that data have a positive impact, we need to keep in mind all phases of the data cycle (see Figure 1). Stated differently, open data does not equal impact. A positive impact arises from a considered process that broadly understands how the data will influence decisions and thus behavior. BEHAVIOR IS THE BLIND SPOT. While workshop participants covered a wide portion of the framework, their experience and expertise was heavily skewed toward the technical sections of data generation and interpretation. Yet as many of the workshop discussions acknowledged, the real challenges for the community have become less technical and more human-centered. Needs finding and definition are critical but underdeveloped skills, and greater insight into behavior change would also be helpful. The tools and data that technical experts create will be more useful if they fit into existing decision-making workflows. EFFECTIVE COMMUNICATION IS A KEY IN CHANGING BEHAVIOR. Participants agreed that a lot of work goes into “doing things,” while less goes into effectively communicating. This failure to invest and build capacity in effective communication is a significant limiting factor in changing behavior. Better communication would especially be beneficial to local communities who could benefit from these data. CHALLENGES
4. 4 THE CASE FOR OPEN DATA STILL NEEDS TO BE MADE. One of the central communication challenges is the case for open data. The thought of using open data for a new or unanticipated purpose for some still inspires anxiety. This is in contrast to the central value proposition of open data, which is the unexpected value from usage. To be sure, there are a variety of reasons, good and bad, why people resist open data, including effective business models to support the quality of data, the perception that information is power, and even embarrassment over the quality of data. These objections need to be better understood and addressed in order to encourage data sharing. ACCESS TO DATA IS A SIGNIFICANT CHALLENGE, BUT NOT SO MUCH A TECHNICAL CHALLENGE ANYMORE. Over the last several years, tools for sharing data have significantly increased. What continues to limit data sharing is the remaining social and regulatory challenges, which range from issues involved in sharing data between agencies (within governments, between governments, and between multilateral agencies), to the challenge of getting the data to communities, the requirement for national and local-governments agencies to sell their collected data to recoup costs, to the legal and regulatory frameworks that may complicate or hinder sharing. SOMETIMES THINGS ARE TOO EASY - THE PROLIFERATION OF DATA PLATFORMS AND TOOLS. As building and deploying digital tools has grown easier, tools (of various quality) and data portals have proliferated. In most cases this growth works to fragment effort and decrease the benefits of collaboration and scale. It also takes energy away from creating higher-value products.
5. 5 SOMETIMES THINGS ARE TOO HARD - THE COMPLEXITY OF CRITICAL DISASTER TOOLS AND OUTPUTS. While building tools has grown easier, using tools is often not straightforward. The complexity of certain disaster tools and products is a critical challenge facing the community. Complexity might be intrinsic—for example, poor handling of uncertainty in data products, or the propagation of that uncertainty in the calculations. Or it might be a function of poor design or communication about fitness for use. This put the sustainability of projects which rely on them at severe risk. THERE ARE KEY DATA STILL MISSING. Models are very sensitive to data quality. If the wrong or insufficient resolution data are used, the model outputs will be unreliable and form a poor basis for decision making; users of the data will wrongly believe that they understand a situation when in fact they do not. Some of the most critical data include the following: • Fundamental data sets, such as high-resolution imagery and elevation data with license to create derivative works that may be shared. • Global databases for assets (human, ecosystem, infrastructure, administrative boundaries etc.) that can be used to understand, quantify, and manage climate and disaster risks. • Comprehensive historical records of past extreme hazards events including the damage from those event. These data are key to communicate impact and validate models. WE MAY BE COLLECTING THE WRONG DATA. The challenge of collecting all useful data is overwhelming. There is still little consensus on which data sets are most useful in supporting actual decision-making. We need to understand users and their needs in order to identify high-value data sets and inform the design of tools that use that data.
6. 6 DATA ARE NOT ENOUGH! Metadata—the descriptive data about the data of interest—greatly increase data’s usability and accessibility. A risk map, for example, has little use by itself; but once the critical descriptive metadata (such as return period) are available, the map becomes much more valuable. Even linking a descriptive report (if one exists) to data has important value. Additionally, metadata that support attribution and provenance expand the usage of data as it increases trust. DATA BEYOND THE SENSOR ARE IMPORTANT, TOO. While physically sensed and modelled data such as risk maps and elevation are critical, there is a whole class of data that is currently undervalued and not often integrated into decision-making. This is the experience of local communities. There is a need to better understand, systematically collect, and integrate these data to create deeper insights. IT IS INSUFFICIENT TO SIMPLY INVEST IN DATA; WE NEED TO INVEST IN PEOPLE To ensure that data have a positive impact, expertise and experience must exist at all points within the data cycle at the local level. Said another way, in addition to investing in data, we need to invest in people to collect, design, analyze, communicate, and interpret that data. Without these key competencies we will never fully benefit from the promise of open data. WE ALSO NEED TO INVEST IN LEARNING. There is a rapidly growing body of experience and expertise within the broader Open Data movement and more specifically the Open Data for resilience community. One essential challenge is to effectively learn as a community so that we can benefit from past success and failure.
7. 7 HOW DO WE KNOW WHEN WE GET THERE? To make a positive impact, the ability to measure that impact is crucial. Thus clear objectives and meaningful measures of achievement are critical for the success of this community and its work. Participants stressed that part of measuring impact has to do with managing expectations, since success moves at different paces around the cycle. Creating data and providing access to it are relatively quick processes, while developing insight takes more time, and behavior change even longer. These differences need to be kept in mind when impact is measured. RESOURCES ARE AVAILABLE FOR WELL-DEFINED PROBLEMS. Several different resources—both human and technical—were available among participants and were ready to deploy. Holding them back was the lack of clear direction and understanding of how or where to contribute. Ideally, as the community develops a shared vision of its role and the challenges it faces, situations of this sort will become less common. THERE IS MORE TO BEING OPEN THEN JUST BEING OPEN. Open does not just mean accessible. Openness enables participation. If we are to truly benefit from open data, we need to embrace collaboration and increase participation by involving key stakeholders at every step of the process. This approach increases stakeholders’ sense of ownership and promotes understanding between participants, which leads to better outcomes.