Published on January 23rd, 2017 | by EJC0
Failing Better In A Complex World: Improving Decision Making With Real-Time Data
If you fail, keep trying again and again.
Doesn’t it seem like too often philanthropists and development practitioners are trapped by adages like this?
In today’s digital era, development has reached its highest level of complexity. To make an impact, a comparable level of flexibility is required: host governments must be able to adjust their leadership vision so that it aligns better with citizens’ well-being; philanthropists must be flexible to adopt novelty funding schemes that are not tied to log-frame results, checkboxes and baseless indicators; implementing partners must be ready to design responsive solutions, matching contextual problems with innovative models that are scalable, sustainable and in collaboration with private sector partners. To achieve these goals, development practitioners must embrace an alternative adage. As Samuel Beckett puts it, “Ever tried. Ever failed. No matter. Try Again. Fail again. Fail better.”
In recent years, an increasing number of organizations and initiatives around the globe have recognized this latter approach of failing better. They’ve incorporated the role of ‘learning’ in their projects and have embraced adaptability. Researchers, policy makers and practitioners are introducing learning as a central theme to addressing complex problems. These initiatives include but are not limited to:
- Collaborating, Learning, and Adapting (CLA) – USAID’s framework and internal change efforts for incorporating collaboration, learning, and adaptation at its missions and implementing partners;
- Smart Rules (Better Programme Delivery) – DFID’s internal operating framework for programs, emphasizing how the agency adapts to and influences local context;
- Doing Development Differently (DDD) – A community of researchers and practitioners convened by the Overseas Development Institute (ODI) and Harvard’s Kennedy School;
- Analysis Driven Agile Programming Techniques (ADAPT) – A Mercy Corps and International Rescue Committee (IRC) initiative to identify, develop and spread the use of adaptive management approaches in complex aid and development projects;
- Problem-Driven Iterative Adaptation (PDIA) – An approach by Harvard’s Kennedy School on how to do development in complex environment/situations; and
- Feedback Labs/Summits – An organization that promotes the role of a citizen’s/beneficiary’s feedback in doing almost any kind of development.
The central tenant of all these initiatives is “Learning to Adapt.”
At mSTAR, (Mobile Solutions Technology and Assistance Research), a project funded by USAID and led by FHI 360, we have been following this movement closely for over a year. To organize and better understand these initiatives, we’ve grouped them into the following components:
As the graphic shows, these initiatives comprise the foundation of a learning agenda enabling environment. They include the personnel that implement ideas, the raw materials that form those ideas, and the tools that connect the two together. However, there is a noticeable gap. None of these initiatives fully realize the importance of quality data. The use of timely, accurate, and representative data is starkly absent.
This noticeable gap raises a pertinent question: how can quality data augment learning and assist in decision making for flexible and adaptive development?
At mSTAR, we are exploring what types of quality data help in decision making. We are specifically researching real-time data systems, which bring together processes for collection, storage and transfer, analysis, interpretation, sharing and use of data on a wide range of development issues, from disease tracking to citizen accountability, and from school performance to commodity price monitoring.
We are still discovering to what extent real-time data systems can effect development. How can real-time data (RTD) help a health facility in Rwanda or Indonesia determine the estimated number of deliveries needed for the following week to develop a working schedule/shifts for clinicians? Or, how can RTD support a district warehouse in Uganda plan better for stock outs of medicines and vaccines? What RTD tools can help personnel perform a trend analysis, predict droughts/outbreaks or find the main roadblocks towards achieving higher yields in the agriculture (rice cultivation) system of Colombia? Can RTD be used to better integrate citizens’ feedback in Nigeria?
At mSTAR, we’ve realized that mobile technology can be used to collect real-time data in fast, reliable, and convenient ways, leading to better decision making. Increasingly more people have perceived its potential and have made this connection. Adaptive programming experts talk about adaptive management but circumvent the use and the power of technology. This is a mistake. Digital tools offer a spectrum of uses and sources. Development practitioners can use mobile Call Detail Records (CDR), frontline workers can use cheap technology mechanisms, such as SMS, Interactive Voice Record (IVR), Unstructured Supplementary Service Data (USSD), and smartphones to report health outcomes at the community level, and sensors can gather data through the Internet of Things (IoT).
In fact, mobile technology can be used for adaptive management in several ways, such as the following:
- Scope and Quality Improvement: Every year, a number of outreach publications provide health awareness services at the global and field level. The traditional approach for an “Early Pregnancy Awareness Campaign,” for example, includes three key components: 1.) The development of content. 2.) Agreement with local radio broadcasting institutions. 3.) Scheduling the broadcast and production of leaflets.
The adaptive method would be more flexible and user centric. It would include the same components as the traditional approach with the addition of an RTD opinion poll about the content and subsequent refinement in the content. Feedback from an RTD opinion poll and the following refinements in content can be reiterated until the final product or solution is perfected. Through the adaptive management model, development practitioners can gain more granular understanding of the people and culture. In the process, they can fine-tune a specific approach for future initiatives.
- Complexity/Trials: Let’s use an example of a complex issue such as HIV/AIDS. Development practitioners have found that individuals are more likely to get tested for HIV when they are convinced to do so by their peers. One way RTD can be used to incentivize this practice is to provide phone-based incentives to any individual who convinces a peer to receive HIV/AIDS testing. In this approach, the peer provides the phone number of an educator to a friend and would be incentivized accordingly. This method can be utilized for a number of adaptive RTD learning purposes, including testing existing ideas and planning for new interventions.
- Constituents Involvement and Evaluation: RTD can be used to augment staff training. For example, the adaptive way to handle a month-long staff training could consist of four steps. 1.) At the end of the first week, the trainer arranges a set of IVR or SMS questions for attendees. 2.) The trainer broadcasts the questions to attendees to test baseline knowledge. 3.) The trainer revises the curriculum accordingly. 4.) At the end of the second week, the trainer sends another set of questions for the knowledge test, and lastly, decides whether to continue, revise or cancel the training.
RTD for adaptive management has real potential uses. So why hasn’t the concept of using real-time data for adaptive management stuck? Despite knowing the benefits of RTD, why aren’t practitioners able to use RTD as a “right hand” for decision making?
A lot of practitioners argue that RTD can tell you about the what but not the why — perhaps that’s why it remains barely-utilized. To address this incongruity, the Development Informatics (DevInfo) team of USAID’s Global Development Lab and mSTAR, in collaboration with a research consortium (IDS, ODI, Reboot, and Feedback Labs) is working to answer this very question. Specifically, when, where and how real-time data can enable adaptive management. This research activity plans to produce a conceptual framework by mid-2017. This framework is an evolving synthesis of theoretical and practical foundations, exploratory research and pilot projects, intended to share findings of real-time learning for development interventions. In addition to the conceptual framework, the overall activity also includes the development of an applied toolbox that focuses on how to adapt development programs by using digital real-time data to improve program delivery and more effectively meet development objectives.
To learn more about these research projects, contact Abdul Bari Farahi, mSTAR Technical Advisor, at [email protected].
About the Author:
Image: Internews Europe.