Data Innovation for Policy Makers Conference 2014 3

When I listened to Aditya Dev Sood, Center for Knowledge Societies, talking about health services in India, I remember Rama Tobuhu, a health volunteer in Desa Mootilango, Gorontalo, Indonesia.

Data Innovation for Policy Makers Conference 2014 3

Transforming Information into Action
Tips for Processing Quality Frontline Service Delivery Data

Mellyana Frederika for Data Innovation for Policy Makers Conference 2014

When I listened to Aditya Dev Sood, Center for Knowledge Societies, talking about health services in India, I remember Rama Tobuhu, a health volunteer in Desa Mootilango, Gorontalo, Indonesia. Once per month, on the 6th, she walks through the village to check the health status of babies and their mothers. She likes being a volunteer and does not hesitate to ‘go the extra mile’. Her main challenge is filling in the many district health office forms and writing a report. “I am an elementary school graduate. It is too hard for me to fill in the forms. I cried and thought about resigning,” she said.  

Stories similar to Rama’s were told at the Data Innovation for Policy Makers conference, which took place in Bali on 26-27 November 2014. Rama is not alone in her struggles. Health workers, teachers, village administrators, police officers and staff of one-stop-service offices share the same challenges, being at the frontline of public service delivery. They are asked to provide services and collect data about their work. They have to write reports in specific formats required by the government system. This data is required for invoice and reimbursement purposes, stocking up on supplies, and logistics. This is all done manually and the use of technology is still limited.

Here are the top tips for maximizing the use of data innovation and information for frontline service delivery.

1.  Do not collect data for the sake of data collection: Experience in India with maternal and child health services shows that data for the sake of data is meaningless. Data give a snapshot of a situation, they can help predict future needs, and they give warnings in emergency situations. Good quality data comes when people are aware of why they are collecting it.
2.  Use data to make policy adjustments and drive reforms: 
Data have great potential beyond the government planning and budgeting cycle. The interpretation of data provides insights into what works and what does not work in policies and programs. Data can help in suggesting new directions or pushing for new services and/or reforms. 

3.  Do not create complicated data collection formats: Frontline service providers such as Rama Tobuhu want to provide a service that is not only needed, but is also of good quality. This means having ample time to interact with beneficiaries. It is not easy to focus on beneficiaries and provide counseling while having to fill out long forms. Frontline workers need simple ways to record their work and a way to generate tangible, visual and real data, such as data artifact.

4.  Collect and analyze feedback from beneficiaries: Numbers are not the only way to get information. Qualitative data can be very useful to complement quantitative information. Service providers such as health workers can ask about the quality of service received by beneficiaries. This practice is common in the private sector, but rarely seen in the public sector. 

5.  Use and reuse data: Data can be analyzed for various purposes. The education district office could use data from the health district office. Data collected from one-stop-service offices can inform regional planning by local planning agencies. End-line data from completed programs can be used as baseline data for a new program. We need to be creative with data and allow cross-sharing. 

6.  Social media is a great source of data and information, but remember to validate:  Tweets can be used as a powerful source of information. People share a lot on Twitter and it is possible to verify information. Tweets can be validated against each other and against another dataset, for example, public sector data as done by 

7.  Open up data: Make data open to the public so that it can be used for different purposes by different actors. But what data should be made public? Making health outcomes visible could trigger ownership on the ground, not only for beneficiaries but also frontline service providers. This allows for participatory decision-making to improve service quality at the frontline. Policies on frontline service delivery should not be decided just on national interests, but on local conditions, challenges and opportunities.

8.  Listen to the beneficiaries: Maximize the benefit by systematically collecting feedback on public services. This may require a change in attitude of service providers and beneficiaries. Mechanisms could be developed by government to create incentives for providing and analyzing constructive feedback regularly. LAPOR, developed by Indonesia’s Presidential Delivery Unit, is one example. Services are delivered for citizens, therefore they are the main priority when developing data systems.

9.  Think about technology that allows interactive feedback mechanisms: We need an interactive mechanism that gives citizens room to provide feedback, but also allows government to take action; a mechanism that simultaneously links citizens’ reactions and queries to government action. This new approach could lead to a data-driven government.

10.  Do not let frustrations get in the way: An application to map out flood locations in Jakarta was developed out of frustration. Innovation and frustration can go hand-in-hand. It is necessary to find a strategy that looks at as many ideas as possible, then turns them into pilots, helping to improve services at the frontline. Examples include Socialcops and Bihar Innovation Lab.

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