Create a Learning Culture as You Scale
How can we create a culture that drives learning and experimentation — and not just accountability?
As we wrote in Scaling Pathways’ Pivoting to Impact, “reaching systems change and transformative scale involves disrupting the status quo, which is not a linear process but entails pivots along the way.” If you are a leader of a social enterprise wanting to achieve impact at scale, you must create an adaptive learning culture that uses data to question, improve, iterate, and pivot. Without this, staff will likely default to accountability mode — driving toward targets but missing key insights, avoiding experimentation for fear of failure and, most importantly, missing opportunities to achieve greater scale. This learning culture is captured by VisionSpring Board Chair, Reade Fahs, who advised, “Don’t fall in love with your ‘pretty little solution.’ Ask yourself: Is it really working or is it not going to achieve your goals? If not, keep pivoting back to solutions that will be able to achieve impact at scale.”
IN ACTION: Health Leads, shifting from a march toward targets to learning from data
After focusing for a number of years on meeting the targets for its operational and performance metrics, Health Leads made an intentional shift in its data approach to fuel its next phase of scaling. A new leader began to ask questions beyond the targets, trying to understand variation within the program and using that to ask questions. For example, why was enrollment so different across cities, and what could they learn from that?
Health Leads created a quality improvement position and conducted a “listening tour” to understand how people in the organization were thinking about program and quality improvement. It realized that the culture often defaulted to “ready, aim, fire,” with little room or time to understand and learn from data.
“Most of our staff had been exposed to data for accountability — that’s the way most of us use data on a day-to-day basis. [But,] using data for accountability makes it difficult to authentically ask why something is the way it is.” — Sara Standish, former VP for Strategy, Learning, and Impact at Health Leads
Recognizing that in order to expedite impact at scale it was necessary to surface the root causes of challenges versus defaulting to the easiest answer, Health Leads leadership worked to slow down the ready-aim-fire culture and make room for bringing others to the table (including front-line workers) to understand context around the data and position themselves to learn from it. As Standish explained, “It is important to be sure you are presenting many different storylines that could explain the data versus the one that just validates your current thinking.” For example, early on in the Collaborative to Advance Social Health Integration, many improvement teams from primary care clinics were reporting unusually high scores on measures of health confidence among their patients (i.e., “How confident are you that you can manage or control your health problems?”). An accountability approach would celebrate this as an achievement and move on, but the learning approach uncovered that patients might not feel comfortable reporting their true health confidence when first getting to know community health workers; some feared that low scores might invite coercive or punitive actions of some sort. Understanding this required creating physical and virtual space for frontline staff to safely share data and stories with one another without fear that it would lead to loss of funding or low performance ratings. In implementing such learning approaches, Health Leads has had to accept that the answers to some of these questions could require significant shifts in strategy — which have since occurred — creating sometimes major pivots but keeping the organization scaling effectively.
Top tips in creating a learning culture
- Ensure your data is keeping you accountable to your clients.
To effectively use data to scale impact, a shift in the purpose of data from accountability (e.g., are we hitting our target number of people served?) to learning (e.g., why are we seeing fewer women engage in our program?) is critical. As leaders did in the Health Leads example above, ask yourself: “Are the questions we are asking and data we are collecting driving only toward accountability or toward learning and improvement?” - Routinely interrogate your data to move beyond convenient narratives.
While new insights can be disruptive to a program and require slowing down to create space for discourse, it is critical to routinely interrogate your data to push beyond existing or convenient narratives. As Harambee expanded into Rwanda, the data indicated that the program was meeting all targets and thus was successful. When interrogating the data further, and questioning whom the organization was serving, Harambee realized that it had met its targets by serving youth with fewer gaps to fill, versus the more vulnerable and hard-to-place youth it also wanted to serve. By interrogating the seemingly positive data further, Harambee was able to tweak and amplify its scale efforts to reach a broader population of youth. For Pratham, routinely interrogating data involves monthly review meetings where regional teams share field experience and data, synthesize data into common themes, and develop action plans to address any concerns. - Model learning from “bad results.”
Individuals and teams can be disincentivized to share data reflecting less than stellar performance if they fear punitive action. This reluctance stifles learning and improvement. Last Mile Health, an NGO that partners with governments to build and sustain community health systems, recognized this dynamic as it brought together county health teams, national government representatives, donors, and partners to regularly review data on the national community health worker program in Liberia. “Understandably, people are worried about being penalized for ‘bad data,’ since this type of data has traditionally been used as an audit mechanism. People of course fear having their funding and/or autonomy taken away,” shares Last Mile Health President and COO Lisha McCormick. To ensure that the data was used for quality improvement, Last Mile Health, the Liberia Ministry of Health, and partners “encouraged county health teams to showcase their challenges, as it would not be held as a mark against them,” as McCormick said. - Embrace continued experimentation and the data that guides you.
Knowing that scaling will require continued experimentation and pivots, data and a learning mindset become critical to guide the journey. Each year, One Acre Fund runs more than 40 experiments to test scaling approaches through its Scale Innovations Team. The data from these experiments is used to assess whether a new innovation — such as increasing the farmer to field officer ratio — can support One Acre Fund’s scaling ambitions by either increasing impact per client or increasing the number of customers One Acre Fund can cost-effectively serve.
Read next: Collect Data that can Drive Action, Collect Data with Scale in Mind or return to see all articles in Data for Scale.
This article was written by Erin Worsham, Kimberly Langsam, and Ellen Martin, and released in June 2020.
