Eric A. Vance and Kim Love

Locally Powered, Data-Driven Development

We consider development, at a very broad level, to be sustainable actions that affect society positively. We believe that three components lead to such development: scientific and research innovations; creating jobs for sustainable activities; and implementing effective policies. Statistics, as a discipline and a practice, enables and accelerates all aspects of data-driven research, business, and policy. Data are key to knowledge, and statistics and data science are the bridge to understanding those data.

Understanding data allows people to make scientifically sound decisions, thereby answering questions, solving problems, and generating development benefits—often at a deeply meaningful level. This is especially relevant for developing countries, which often have pressing development needs for which solutions can be guided through both producing and analyzing data.

Local challenges require local solutions. Solutions provided by a consultant from a highly developed country may not apply in a developing country. The best people to collect or produce data are local experts who understand the context of the activity. The people best positioned to analyze those data are local statisticians and data scientists who understand the local context of how the data were produced. The best people to craft and implement policy innovations are locals with the lived experience of those to be affected by changes in policy.

In other words, it is local researchers, businesses, and policy-makers who actually solve local challenges.

Policy-making, as well as other types of decision-making, often ignores the need for rigorous statistical analyses. The simplistic perception is that only two actors are necessary for data-based decision-making: those who collect or produce the data and those who review the data and make policy or other decisions. In many cases, the data producers and the data decision-makers exist in the same organization or are the same individual, such as a business that collects data about its customers and uses the results to make a business decision or an academic researcher who conducts an experiment to answer a scientific question.

As collaborative statisticians who have worked on hundreds of projects and supervised thousands more, we are keenly aware that, in fact, at least four important components are required to make informed data-driven decisions: the domain expertise required to ask the right questions; high-quality, relevant data; appropriate, nuanced statistical analyses; and the power to make and implement a decision. Assuming that data producers and data decision-makers have such domain expertise, this requires the addition of a third actor—the statistician or data scientist—who has specialized skills in data analysis and interpretation.

Figure 1 shows the intersections of these three actors. Sustainable development requires all three actors operating at full capacity.

Consequently, the suggested strategy for engaging in locally powered, data-driven development starts with acknowledging four potential gaps:

• Gap 1: Local expertise to frame locally relevant development questions.

• Gap 2: Consistent production of high-quality data through carefully designed experiments, studies, or surveys at the international, national, and local levels.

• Gap 3: Technical ability in statistics and data science to design studies or experiments and appropriately model, analyze, and interpret data.

• Gap 4: Evidence transformed from data into action for development.

A New Model for Building Statistics and Data Science Capacity

One of the International Statistical Institute’s four strategic priorities is building statistical capacity in developing countries. Historically, this has focused on building the capacity of developing countries to produce state-sponsored data; for example, to conduct accurate population censuses, consumer price index surveys, etc. While the state-sponsored production of data is foundationally important for development, it addresses only one of the potential gaps in data-driven development.

Even though the data produced may be of high quality, they might not provide the information or evidence needed to answer the questions raised by the data decision-makers, may be interpreted incorrectly, or may be insufficient alone for taking action for development.

A new model for building statistics and data science capacity could help statisticians and data scientists in developing countries build their own capacity to engage in data-driven development.

It is important to keep in mind that working in academic isolation will not create positive development outcomes. To create real-world impact, statisticians must take steps to apply theories and methods to help transform academic evidence into action for the benefit of society. For example, statisticians and data scientists at the University of Calcutta, India, created a model to forecast the solar energy output of solar farms and then collaborated with decision-makers to use the model to optimize the production of sustainable solar energy.

Transforming technical statistical methods into positive action for society requires statisticians and data scientists to be skilled collaborators as well as skilled methodologists and analysts. In other words, these statisticians must have the skill to work in the intersections of Figure 1 in addition to the skills needed to analyze and interpret data. They must be able to understand the data and projects they are working on at both a deep and broad level, and be able to communicate the results of statistical methods and analytical work in ways that provide actionable evidence to those who can use it for a positive impact on society.

This model for building capacity is meant to create statistics and data science collaboration laboratories (“stat labs”) that work at the intersections of data-driven development by collaborating with data producers and decision-makers to transform evidence into action. It supports the capacity of statistics and data science partners in developing countries by helping them engage in data-driven development. Increased collaborations between statisticians and development actors in developing countries can contribute to improved infrastructure, business development, education, agricultural growth, and human rights, among many other areas in urgent need of progress. Especially in developing countries, it is essential that local statisticians and data scientists interact with local researchers, businesses, and policy-makers to develop sustainable solutions to local challenges.

The foundation of this capacity building model is the statistics and data science collaboration laboratory. The Laboratory for Interdisciplinary Statistical Analysis (LISA) 2020 Network supports these labs around the world. These statistics and data science capacity-building efforts have shown impressive results.

Stat Labs as Engines for Development

Statistics and data science collaboration laboratories—stat labs——provide a mechanism for increasing collaboration between statisticians and researchers, business professionals, and development policy actors. In this context, these labs are housed in research institutions in developing countries (generally universities), and have three main objectives:

• Train statisticians to have a collaborative, evidence-to-action mindset.

• Teach researchers, business professionals, and development policy actors to become more capable of using data and more aware of the power of statistical analysis to inform decisions.

• Provide a collaborative space for statisticians and data scientists to work with those individuals to create data-driven innovations and solutions that lead to widespread development impacts and outcomes.

Stat labs are not rooms full of computers. Rather, a stat lab is a team of statisticians and data scientists empowered to collaborate with domain experts to ask relevant questions, produce high-quality data, analyze and interpret data to create knowledge and evidence, and transform that evidence into action for development.

These stat labs can be viewed as “engines for development.” Stat labs initially focus on training students, faculty, and staff to become effective interdisciplinary researchers, with the technical and collaborative skills necessary to accelerate research and transform it into solutions to development challenges. They reach out to the local community of researchers, business leaders, government agencies, and non-governmental organizations, both to provide training and to provide tailored statistical support. As a result, the community becomes more aware of the ability (and indeed, the necessity) of the stat labs to provide this type of assistance.

As this awareness grows, the community makes more requests to the stat lab for this type of assistance, which in turn provides more opportunities for capacity development within the lab. Initial successes create a positive feedback loop in which more development actors want to collaborate with the stat labs and become “data-capable,” and more statistics students, faculty, and staff wish to work in the stat labs.

Stat labs that focus on using projects as opportunities to train students rise to the challenge of building capacity quickly to complete a higher number of projects. When projects come with funding, senior students and faculty can be compensated to both work on projects and mentor junior students.

Successful, high-profile projects also engender support for stat labs from administrators in their universities, potentially loosening restrictive rules and removing institutional barriers to success, and attract more students to study statistics and data science. This self-reinforcing cycle of using experience on projects to build capacity to work on more projects is key to the long-term success of stat labs (Vance. 2015).

Over time, these stat labs provide a supply of experienced statisticians with a collaborative, evidence-to-action mindset and a cadre of more-capable development actors who recognize the power of statistics and data science to help solve their development challenges. The stat labs also provide a physical and intellectual place for statisticians/data scientists and development actors to collaborate on projects to transform evidence to action and produce data-driven innovations to solve development challenges. These innovations lead to widespread, substantial, and sustainable development impacts because a well-trained collaborative statistician or data scientist can enable and accelerate 10 or more development projects per year, and those projects can have a positive impact on thousands upon thousands of people (Vance and Smith. 2019).

Through all of these mechanisms, stat labs not only fulfill the role of data analyzer, as shown in Figure 1, but serve as a conduit that leads to development work in the intersections of the various actors. When collaborative statisticians are able to participate in and even initiate projects that involve policy and decision-makers, they can translate the policy-makers’ questions and desired information into quantitative questions that can be answered through the production of data. When collaborative statisticians are trained directly to work with data producers, they can help design experiments or studies that result in high-quality data, which they can then analyze in a way that can be relied on.

When collaborative statisticians can connect with both the data decision-makers and the data producers, they can ensure that data production occurs in a way that is appropriate for answering the questions posed by the decision-makers, and analyze and interpret the resulting high-quality data to provide the information required for decision-makers to make recommendations and implement policies. This three-way intersection, for which stat labs can be the catalyst, provides the most-desirable setting for data-driven development.

The LISA 2020 Network of Stat Labs

While serving as director of LISA at Virginia Tech, Eric Vance recognized the power of the LISA stat lab to build the technical and collaborative capacity of students while collaborating on projects to benefit researchers, businesses, and policy-makers. The LISA students were, in fact, learning more statistics and how to translate their technical work into useful results by collaborating on these projects. The more projects they worked on, the more effective they became and the better they were at training and mentoring junior students in LISA.

When it became clear that the LISA model, when adapted to local conditions, could help statisticians in developing countries build their own capacity to do similar work to solve local challenges, Vance created the LISA 2020 program in 2012 to build statistical analysis and data science capacity in developing countries, with a goal of creating a network of at least 20 stat labs by the year 2020.

In 2016, with five newly established stat labs as members of the LISA 2020 Network, Vance moved LISA from Virginia Tech to the University of Colorado Boulder. In collaboration with the International Statistical Institute, word of the benefits of the LISA 2020 Network spread widely, with particularly strong uptake and adoption in Africa, South Asia, and Brazil.

As of August 2021, the LISA 2020 Network consisted of 34 stat labs in 10 low- and middle-income countries, with 14 in the process of becoming full members of the network (see Figure 2).

The overall purpose of the network is to facilitate the process of individual stat labs becoming engines for development. This not only allows for faster capacity growth at each individual lab, but also increases the potential for the stat labs to have a worldwide impact in the network through lab- and country-level collaborations. This begins with the creation of labs.

Although two labs that joined the network had already been established, the vast majority are created and grown through the LISA 2020 Network. A stat lab follows a seven-step process to become a member of the network:

1. Identify a potential director or coordinator of the stat lab and a mentor from the LISA 2020 Network who will help them receive sufficient training and guidance in the non-technical skills needed to move between theory and practice to apply statistics and data science to solving real problems, as well as to learn from other stat labs and share best practices.
2. Gather and document support from the department and throughout the university in the form of letters from senior university officials supporting the official creation of such a stat lab.
3. Complete and submit the Full Lab Plan/Proposal via email to become a Proposed Member. The proposal documents the purpose/mission of the stat lab, a name for it, and a physical location with enough space to meet with domain experts. It also includes detailed statements about the lab’s Context/Environment/Leadership; Mission, Goals, and Objectives; Activities; Personnel; Budget; Expected Outcomes (metrics); and Desired Impacts (metrics), as well as how the lab plans to stay connected with the network.
4. Respond to a review committee’s feedback on the Full Lab Plan/Proposal. If the response to the feedback is satisfactory, the lab will
   become a Transitional Member of the network.
5. Open the stat lab: a) Train students and staff; b) Provide research infrastructure for local domain experts; c) Teach short courses/workshops to improve statistical skills and data literacy widely; d) Report on the stat lab’s activities, outcomes, and impacts (metrics).
6. Stay connected with the network via semi-monthly Zoom meetings, annual symposia, quarterly reports of stat lab activities and numbers, and other channels.
7. Report a full quarter of metrics and present about the lab to the network.

Typically, labs will complete steps 1-4 before step 5, although some of the steps may be taken out of order. For example, a stat lab can open before completing step 3. Labs are encouraged to become connected with the LISA 2020 Network (step 6) at early stages of the process. In general, labs that complete steps 1–3 are considered Proposed Members.

After becoming a Transitional Member, the next step is to begin operation of the stat lab. If accepted by a two-thirds majority vote after reporting a full quarter of metrics and introducing the lab to the LISA 2020 Network at a Zoom meeting, a lab becomes a Full Member.

Extending Connections

A further purpose of the LISA 2020 Network is to enable continued connections among the stat labs at various stages of development. This allows them to share progress and learn from one another, and collaborate on projects on an international scale. More-established stat labs share their cultivated best practices with newer labs; the new labs then innovate to fit these practices to their particular circumstances and in turn, share new successes and challenges with the network.

The LISA 2020 Network facilitates these exchanges of information through regular emails and newsletters, twice-monthly Zoom meetings (including more-extended presentations by member labs), and annual symposia.

Finally, the network is a united organization that can act as a venue to provide funding and connect stat labs to decision- and policy-makers for data-driven development. As an example of the utility of the network, the U.S. Agency for International Development signed a cooperative agreement with the University of Colorado Boulder in 2018 for the LISA 2020 Network to provide funding for several stat labs to engage in pilot projects with data decision-makers. The Transforming Evidence to Action (TEA) fund enables the selected stat labs to collaborate in the intersections with data producers and data decision-makers.

TEA fund projects currently underway include the following.

• The University of Ibadan in Nigeria partnered with the Independent National Electoral Commission (INEC) to assess the quality of the country’s Continuous Voter Registration exercise, examine the effectiveness of the electoral process for voters, and make recommendations about the quality of the voter register and future election-related activities (Olubusoye, et al. 2021).

• Wolkite University in Ethiopia is partnering with the Gurage Zone Vital Events Registration Agency to improve the current vital events (e.g., births, deaths) registration system through design and analysis of resident surveys, database creation, and training agency workers in data management and analysis.

• The Federal University of Rio Grande do Norte (UFRN) in Brazil is partnering with the Department of Public Policy at UFRN and União dos Dirigentes Municipais de Educação to address educational inequalities in the state of Rio Grande do Norte by analyzing data obtained from the Brazilian Ministry of Education and producing models to determine relationships among school infrastructure, students’ social background, and students’ performance in standardized tests.

• The African Center for Education Development in Nigeria is partnering with the Nigerian National Bureau of Statistics to study the impact of COVID-19 on small-scale business enterprises in northern Nigeria by designing and analyzing a survey of small-business owners, and using the results to assist in developing a road map for implementing economic intervention for such businesses.

• The Kwame Nkrumah University of Science and Technology (KNUST) in Ghana conducted a two-part workshop for 26 female scientists in government positions on data analysis for decision-making (part one) and methods for policy analysis, planning, evaluation, and leadership (part two).

As shown in Figure 1, these projects allow stat labs to work in the intersections between data analyzers, data producers, and policy-makers (i.e., data decision-makers). In several cases, the data producer and data decision-maker is the same entity (e.g., INEC). The stat lab provides collaborative statistics and data science expertise to their partner to ask development-relevant questions, produce high-quality data, analyze the data, and formulate policy recommendations, fully operating within that three-way intersection.

Building Statistics and Data Science Capacity

The LISA 2020 Network currently uses multiple indicators, collected quarterly from all labs in the network, to evaluate its accomplishments, as well as promote learning and sharing of information among the network members (see Table 1). These metrics have been collected since January 2019, when there were 10 members of the LISA 2020 Network in a position to collect them.

As of March 31, 2021, with 34 labs reporting metrics, the labs have reported a total of 752 projects and 1,552 statisticians trained to be collaborative statisticians, 70 percent of whom are students (graduate and undergraduate) and 36 percent of whom are female. The stat labs have offered a total of 220 workshops, with 8,728 attendees, of whom 43 percent are female. Their work has resulted in a total of 75 peer-reviewed publications with a total of 226 authors, including both stat lab collaborators and researchers (of whom 36 percent are female).

In addition to those from all labs, the network collects indicators from labs that undertake funded development-oriented projects. These include the number of program and policy changes made by public sector, private sector, or other development actors that are influenced by lab-funded research results or related scientific activities; number of convenings held to disseminate research for use and/or develop policy recommendations; and publications specifically related to the results of these projects. The majority of these projects have only recently begun and have not yet produced any results.

Stat labs are also encouraged to collect and report customized metrics unique to their labs, which may be determined by a lab’s unique stakeholders. So far, this has included the number of events to promote the lab to potential collaborators, undergraduate lectures, and theses and dissertations assisted by the lab.

Nine Lessons Learned (So Far)

Through creating and growing stat labs, measuring and evaluating the progress of those labs through metrics, and assisting with the implementation and administration of the TEA fund projects, the LISA 2020 Network has learned nine important lessons about the ways in which stat labs can function productively in their role as data analyzers, while facilitating the intersections of all three actors in data-driven development.

Lesson 1. Stat labs should attain a stronger role in state-sponsored data production and analysis of those data. Stat labs are skilled at helping data producers produce high-quality data. For example, the LISA stat lab at the University of Ibadan was able to assist the INEC in designing sampling plans and initiating sampling of the voting population and the voting register to answer questions about the quality of the voter register and the strength of Nigerian democracy.

However, there is a divide between academic statisticians (including stat labs) and the national producers of data. Therefore, stat labs should work toward bridging this gap to attain a more-prominent role in the data production process, because those who produce data at a national level are often not aware of the benefits of working with skilled statisticians and data scientists during the planning stage of data production.

The production of state-sponsored data is also often separate from the analysis or use of those data. Expert statistical analyses by stat labs of state-sponsored data could produce useful evidence that leads to action for development. To facilitate future interactions and to strengthen the data production system, stat labs should focus on placing more of their graduating students in jobs in national statistics offices.

Lesson 2. Stat labs are skilled in training data decision-makers to become statistically aware and data literate and should focus more effort on training policy-makers. Training is a strong point of the LISA 2020 Network and opens the door for future collaborations. In just over two years, even with the COVID-19 pandemic making it more difficult, LISA 2020 stat labs have taught 220 workshops to 8,728 attendees, the vast majority of whom were research-focused data decision-makers (i.e., university staff and students). An opportunity for stat labs to increase their impact is to focus training outside their universities to business and policy decision-makers, as well as to state-sponsored data producers.

An example of a stat lab expanding its scope for training was the TEA fund project at KNUST in Ghana to build data analysis and interpretation capacity for policy decision-making and strategic planning, develop leadership capabilities, and provide a mentoring platform for mid-career female scientists in government positions. The impacts of these workshops have been nothing short of amazing: 11 job promotions and acceptances into funded PhD programs, five research grants funded, five scientific publications relying on statistics learned in the workshop, and one participant (a virologist) who has become a leading voice in Ghana in the fight against COVID-19.

Lesson 3. Projects building capacity in the three-way intersection of data-driven development actors have the most potential for impact. When the data producer is also positioned to be a policy decision-maker, a stat lab can provide statistical expertise in all phases of the project to frame development-relevant questions, produce high-quality data, analyze the data, and make policy recommendations. Combining state-sponsored data production and local-level data production from individual researchers, businesses, non-governmental organizations, or local policy-makers with thorough analyses and interpretation of data will help development actors move beyond the common practice of thinking that data alone are sufficient for making decisions.

The network should focus its capacity-building efforts on helping stat labs to work in this intersection, which will build their and their country’s capacity for producing data at the international, national, and local levels; analyzing that data; and using that data for development. The network should redouble its efforts to provide training for statisticians to collaborate with the data producers and the data decision-makers to transform evidence into action.

Lesson 4. Projects in the intersection of the research, business, and policy domains also have high potential for impact. Since the labs are primarily centered at universities, they naturally focus on supporting researchers by helping them design experiments and studies to produce data and/or analyze the data to make decisions about scientific research questions. Disseminating the findings of a collaboration between a stat lab and an academic researcher through a journal, however, is often insufficient for influencing policy decisions.

If a project’s goal is to influence development decisions, policy-makers should become involved in the project and stat labs must deliberately reach out to them. Similarly, involving the local business community in academic research projects can increase the potential development impacts of those projects. By reaching out early as skilled collaborators, stat labs can ensure that they are helping to answer questions of interest to policy- or business decision-makers. The highest-impact projects will involve all three of the research, business, and policy domains.

Lesson 5. TEA requires a mindset shift uncommon in statisticians and data scientists. Even when statisticians collaborate with other development actors, the “traditional” end of the statistics or data science cycle (i.e., a timely, cogent, well-motivated, and contextualized analysis with easily digestible findings, conclusions, and recommendations) is still only part-way toward development action. Even when working directly with policy actors who can make data-based decisions, it is difficult to follow project outcomes until they result in verifiable policy changes. This is partly because policy change is often a long and complex process, and the network’s statisticians feel compelled to move on to the next project.

Statisticians must adopt a TEA mindset to see a project all the way to its end for evidence to be transformed into action for the benefit of society.

Lesson 6. LISA 2020 stat labs are becoming engines for development. In a wide variety of contexts around the world in 10 developing countries, the network’s stat labs are succeeding in carrying out their missions. They are training their own staff members and students, and providing them with projects to further enable their growing capacity in collaborative statistics and data science. They are conducting short courses and workshops for broad audiences, and engaging deeply in research projects, as evidenced by many co-authored publications. The stat labs are establishing themselves as local infrastructure to enable and accelerate data-driven development.

Lesson 7. LISA 2020 stat labs can adapt in adverse circumstances. In 2020, navigating the COVID-19 pandemic required operational changes for individuals and organizations at a global scale. This unexpected crisis also forced the stat labs to adopt novel approaches to their training, teaching, and collaborative activities.

As one example, the lab at UFRN in Brazil transferred its student collaborator training activities to an online environment, providing lessons as well as group discussion; the initial semester created the opportunity to record videos of the lessons, allowing for asynchronous lessons in future semesters where this approach is necessary. Afe Babalola University (ABUAD) offered workshops online—still available to internal participants at the university—throughout pandemic restrictions.

The technology at many of the stat labs is adequate to provide alternatives when in-person gatherings are not possible; the motivation of the lab personnel is sufficient to prioritize lab activities even when they cannot be done in the typical setting of the lab.

Lesson 8. LISA 2020 stat labs have many opportunities for improvement. The network lacks gender equality, with fewer women represented in every area in which gender is recorded—including collaborative statisticians trained, workshop attendees, and publication authors. While gender issues are not specific to stat labs, but rather stem from larger societal issues in the countries where the labs are located, lab directors are enthusiastic about achieving gender equality. Each lab that is approved as a full member of the network provides a plan for including females in the administration of the lab and working toward future gender equality in its activities.

Another area challenging stat labs is measuring longer-term impacts of their activities. For example, although the number of short courses, workshops, and attendees is recorded for all of the labs, only KNUST has implemented a longer-term follow-up evaluation of their attendees to learn how they incorporated the training received into their work or research. KNUST allocated part of its TEA fund budget to document the impact of its workshop. The LISA 2020 Network as a whole can learn from this bright spot in documenting a stat lab’s impact.

Lesson 9. Sustainable funding for stat labs is a challenge. Some labs have obtained ongoing funding from their universities, but most rely on volunteer efforts from statisticians and data scientists, as well as support from individual researchers and workshop fees, to continue their operations. Some labs have received initial funding to work with organizations and often continue working with them because the organizations recognize the value of skilled statistical collaborators and are willing to provide funding for statistical support on future projects. This helps to ensure the continued existence of these labs as they strengthen their statistics and data science capacity while enabling and accelerating data-driven development.

Conclusion

The LISA 2020 Network began almost a decade ago with the idea that collaborative statisticians in developing countries could create stat labs to build statistics and data science capacity. Based on the collective experiences of more than 30 newly created stat labs since then, the network is being transformed by the idea that such statistics and data science capacity can be built by focusing research, education, and outreach efforts on the intersections of data-driven development.

The current and near-future focus of the network is on improving the quality and sustainability of the individual stat labs and strengthening connections between them. It will be exciting to discover what the next decade holds for using data-driven development to build statistics and data science capacity.

Further Reading

Olubusoye, O.E., Akintande, O.J., and Vance, E.A. 2021. Transforming Evidence to Action: The Case of Election Participation in Nigeria. CHANCE 34(3), 13–23.

Vance, E. 2012. International Experiences in Statistics. Amstat News 420, 17–19.

Vance, E.A., and Smith, H. 2019. The ASCCR Frame for Learning Essential Collaboration Skills. Journal of Statistics Education 27:3, 265–274.

Vance, E.A. 2015. Recent Developments and Their Implications for the Future of Academic Statistical Consulting Centers. The American Statistician 69:2, 127–137.

About the Authors

Eric A. Vance is an associate professor of applied mathematics and global director of the LISA 2020 Network at the University of Colorado Boulder. He researches the macro-theory of collaboration—what institutions can do to promote interdisciplinary collaboration between domain experts, statisticians, and data scientists. He is an Elected Member of the International Statistical Institute and a member of its Statistical Capacity Building Taskforce. He has traveled through 85 countries to support collaboration on building capacity for sustainable development.

Kim Love is a private statistical collaborator at K.R. Love QCC and the program monitoring specialist for the LISA 2020 Network. She has an undergraduate degree in mathematics from the University of Virginia and a PhD in statistics from Virginia Tech. She aims to spread an appreciation of statistics at all levels through her collaborative work, participation in professional organizations, workshops about statistical methods, and the LISA 2020 Network.

Back to Top