During our upcoming Learning Community, partnerships in the Integration Initiative will leverage local data to drive decisions. Two New York Times articles set the stage for that conversation.

Over the last few months, the Collective Impact team at Living Cities has been preparing for the 13th Integration Initiative Learning Community. On October 13-14, eight partnerships in the Integration Initiative (TII), representing communities from across the country, will convene and continue their journey to improve results for low-income people. Learning Communities are opportunities to deepen practitioners’ expertise around practices we believe are critical to creating a new urban practice that gets dramatically better results for low-income people, faster. They also enable partnerships to learn from one-another, and spend time problem solving and working with their teams. Some of the key elements that TII partnerships are incorporating into their work include a cross-sector “table” to lead the work, the use of both grants and debt financing through a Community Development Financial Institution (CDFI), involvement of philanthropy, and engagement of the public sector.

In 2014, The Integration Initiative entered its second round and over the last year, partnerships have embarked upon a planning phase, implementing Results-Based Accountability; applying a race, equity and inclusion framework; and deepening their expertise in adaptive leadership. Recognizing the importance of data and the readiness of communities, during the upcoming Learning Community, partnerships in TII will be leveraging their local data to help drive decisions. We believe this is an important element of their journey towards results.

In a session at the Integration Initiative June 2015 Learning Community, participants discuss results.

In a session at the Integration Initiative June 2015 Learning Community, participants discuss results.

Charting the Map

On our path to planning the Learning Community, we had to test drive a few ideas about data. What, exactly, do we hope partnerships in TII will walk away with after our two days together? What values do we hold regarding data? And how do we expect partnerships to use data starting with planning all the way to understanding if strategies they are implementing are actually yielding results they expect? After identifying our learning questions for the Learning Community, we like to provide framing material for the participants, including pre-reading. We scoured the internet and reflected on articles we had read in the past to identify the right information to set the stage for the conversation around data.

In preparation for the event, I thought you might enjoy our reflections on the two articles we have selected for the Learning Community pre-reading. I hope these articles spark ideas around the importance of data and the role data can play in improving processes, changing systems and improving outcomes. I invite you to share any reflections you have after reading the articles, and encourage you to follow us over the next few weeks as we share real time learning from the event and reflect on the communities’ journey. Please follow us on Twitter during the event with #TIILearn.

Disaggregated data provides a more nuanced understanding of the problem we are trying to solve.

A few weeks ago, I was reading an op-ed in the New York Times by Susan Dynarski. Lately, there has been a lot of interest and press about student loan debt, mostly about the exorbitant amount of debt students are taking on and the number of students who are defaulting on their loans. For a while, all we knew were these population-level numbers: total amount of student debt and the number of students taking on debt. This is all well and good, but we didn’t know much more. Imagine trying to develop strategies to reduce the number of students who default on their student loans or policies that could potentially affect student with the most debt, where would you even start? Having some data is certainly better than no data, but the ability to disaggregate data by race, ethnicity, neighborhood, school, socioeconomic status, gender, age etc. provides you with a much more nuanced understanding of the problem and helps you identify the best type of strategy to implement.

Participants in the Integration Initiative June Learning Community engage in conversation with their peers.

Three participants in the Integration Initiative June 2015 learning community engage in conversation with peers.

Data should be used continuously to improve processes, change systems and achieve meaningful outcomes.

In 2009, The New York Times Magazine published an article titled “Making Health Care Better.” Unlike the previous article, this is a more in-depth study of how to use data in a continuous and on-going manner to improve processes, systems and ultimately outcomes. The transition from using data to identify and understand the problem to using data to understand if the strategies implemented are having the desired impact is noteworthy and why we selected this article for the Learning Community. One thing we know about decision-making is that our intuition is often incorrect. Even after reviewing data and understanding the problem in more detail, we might not always devise the best interventions. Our cognitive functioning is influenced by biases such an availability heuristic, where we place undue value up examples that are most readily available in our minds. There are countless other examples of biases we have when making decisions. This article explores how Dr. Brent James and his team in Salt Lake City have reduced variation in outcomes by creating iterative processes and reviewing data on a continuous basis to ensure patients receive the best care possible.

It is true that both of these articles focus on topics rich with data, like education and healthcare. Still, we believe partnerships in TII, which are focused on topics ranging from education to housing can benefit from the way data has been used in other fields. And that applying these lessons will be critical to improving results for low-income people.