23 3.3 CONSIDERATIONS FOR DIGITAL DATA COLLECTION The starting point for data collection is how the data will be used. As township administrations play a greater role in people-centered development, township departments may need to collect new data to understand people’s experiences and the challenges they face. As outlined in chapter 2, current data-collection projects occur largely at the behest of administrators at the national or state/region level. However, many of Myanmar’s most pressing development problems are local. Complex local problems require township departments to work collaboratively, using systematic problem-solving approaches such as the SARA model (box F). Once township administrations have collectively identified and prioritized problems, they can consider the available data and where there may be gaps. Data collection can then be locally tailored to bridge these gaps. Higher-level administrators should review existing data-collection requests with the aim of reducing the burden on township officials. The transition to digital data collection is an opportunity for all ministries and departments at every level to reexamine data collection. Digital data collection can be more efficient, but if data has no clear use, it is most efficient to stop collecting it. In reviewing their data-collection requests, higher authorities should consult with township administrations to ensure that, wherever possible, the data can also be of use to township officials and that digital data collection is not overly onerous (box G).44,45,46 Data collection at the township level should be reimagined to include “missing voices,” so that the data collected is inclusive and reflects the diversity of experiences and needs within communities. While governments and officials typically think in terms of what data is available, it is important, when addressing social problems, to consider what data they do not have. “How we collect and understand data, and design solutions to social challenges, is generally framed from the standpoint of the dominant racial, social, and cultural majority.”47 If township administrations are to solve the local problems that matter most to communities and their members, these communities must be heard, especially in the kinds of data that are collected. To be inclusive, data collection must be representative of the different groups within a community, because different groups experience problems in different ways, or have different, sometimes competing priorities. For example, on questions of public safety, men often identify theft as the top priority, whereas women often highlight the dangers of walking home at night—something that decision-makers, typically men, may not have considered. Similar considerations bear on how, when, BOX F The SARA Problem-Solving Model The SARA problem-solving model has been used successfully by police forces and local authorities around the world. This systematic, evidence-based process has four stages: 1. Scanning. Identify and prioritize problems. 2. Analysis. Collect and analyze data to identify the underlying causes of the problem and to narrow the scope of the problem as much as possible. 3. Response. Tailor activities to address the causes of the problem. 4. Assessment. Measure whether the response had the desired effect. Make changes to the response if necessary. This approach works best when government departments and service providers work together to collect, share, and analyze data about problems in their area, and when there is constructive engagement with the public throughout the process. and by whom data is collected. Collecting data at times or in places frequented by just a subset of the community can introduce bias. If the data collector or the data-collection tool appears to be a threat or a security risk, respondents may not answer truthfully. For example, a combination of social norms, mistrust of the justice system, and a lack of comprehensive legislation have caused severe underreporting of data on gender-based violence. Likewise, in conflict-affected areas of Myanmar, government data collection may be met with mistrust if communities do not know how data will be used. Government data collection can also be expanded to include secondary sources like civil society organizations and community groups that have been documenting local issues. Such groups can assist in and even codesign data-collection efforts to make them more inclusive. Digital technology can make local government data collection more effective and efficient. Mobile phone, tablets, and computers can all make data collection