When Policy and Technology Meet

It’s increasingly clear to me that those working in policy and technology need to understand one another and work together more effectively. Our ability to improve government programs and services depends on it.

I’m a policy analyst and researcher, a nerd and an optimist. I’ve built my career on the belief that evidence, collective knowledge and thoughtfulness can make programs and services better for low income individuals, families, and communities. Until recently, I had given very little thought to the role technology plays in the policy and programmatic areas where I work, despite my nearly 15 years working in the non-profit and government sectors.

This year, I’ve been consulting on a project with Connecticut’s Office of Early Childhood (OEC) to help them modernize and upgrade the way they use technology to advance their mission. Given my knowledge of federal programs, grant making, and general bureaucracy, I was asked to join the team as a bit of a translator and subject matter expert, advising on program and policy background, purpose, and implementation. In the past, my own myopic lens was focused on identifying non-legislative policy levers to pull (issuing guidance or identifying evidence-based practices).Through this project, I gained an opportunity to fully engage with technology and technologists. These experiences helped me realize how I’ve underestimated the importance of technology in building, supporting, evaluating, and improving policy and programs.

Here’s some of what I’ve learned so far:

In many cases, technological decisions impact and drive how a policy or program is implemented.

The decisions made in structuring digital systems can shape who has access to a program and how eligibility criteria are integrated in a data system. Unfortunately, without careful attention to impact, this can create unintended bias within systems. Perhaps even more concerning is that the consequences and effects of this bias could endure and become more institutionalized as digital systems grow and expand to new programs and services. Technology can drive what data we have available about those programs and enrollees. So for example, if we collect the location of programs, we can know if services are delivered where they are needed. For my evidence-loving self, it’s important to understand that technology decisions can drive if we’re able to understand if a program is working or not. It can also help us discern the impacts of an intervention, but generally only if we’ve structured our data collection and retention to include this longitudinal data. It’s important to note too that the opposite is true — poorly implemented technology can limit the reach of a program. It might also mean quality data are unavailable for evaluation, potentially leading to a program’s failure.

Too often, our policy and technology teams are isolated from each other.

I believe that both technology and policy teams could benefit from greater collaboration. On this project, my experience with policy, research, and bureaucracy meant that when we were tasked with understanding how data and technology could improve service delivery for families experiencing homelessness, I could quickly bring the team up to speed on evidence-based programs, data and research, and policy decisions that shape the Connecticut experience. We could then more quickly identify the pain points to focus our work. With my knowledge of federal grants, I was able to explain the rationale behind the grant funding our work as well as its policy and program goals. I also served as a translator as the team began exploring linking agency data to support a planned program evaluation. This helped build bridges between policy research and the technology and systems. My hope is that these experiences and others enabled our team to work more efficiently and effectively.

Technology teams benefit from having a policy perspective, but it’s also true that policy can benefit from a technology perspective.

As I prepare to return to a role in government research and policy, I’ll be thinking about how to integrate what I’ve learned from my colleagues working in technology. Some key practices I will carry with me include: working transparently, iterating on deliverables that are intuitive to users, and truly integrating technology (and technologists) earlier in the process, as a partner in development and not an afterthought.

For those of you doing the important work of modernizing technology and systems in service to the public, I leave you with a few parting words of advice:

• Make sure outcomes, rather than outputs, are at the center of your work. A helpful tool used in policy and research is a logic model, sometimes called a theory of change, illustrating the progression of resources into activities, resulting in outputs that hopefully lead to outcomes.
• We’re not that different after all! Many policy analysts have training in both qualitative and quantitative methods. They may not identify as a data scientist or user researcher, but if you chat with them, you may find out they are pretty familiar with these methods. They just aren’t familiar with them in your context.
• At their best, those working on policy and technology are trying to find solutions to problems, sometimes the same problem. Silo busting is a worthwhile goal. Although we talk about this across funding streams or programs, we should also be thinking of doing this across disciplines, with the hope of having longer lasting and deeper outcomes.

Carli Wulff served as a program and policy advisor with Bloom Works. Carli previously worked for the U.S. Department of Health and Human Services as a policy analyst and researcher and will soon work on Canadian housing research from her new-ish hometown of Ottawa.

Pollinator is an open space for sharing lessons learned and insights curated by Bloom Works. Have practical wisdom to share with other changemakers in this space? We’d love to learn more. Drop us a line at — pollinator@bloomworks.digital