Lessons on evidence for scale-up from Kidogo

Kidogo is a social enterprise based in Kenya that is dedicated to improving access to quality, affordable Early Childhood Care & Education in low-income communities across East Africa. Kidogo’s model empowers caregivers (“Mamaprenuers”) with training and mentorship to increase the quality and profitability of the daycares they run. Kidogo reached over 11,000 children in 2021 via these daycares. IDinsight has been working with Kidogo to bolster its monitoring and evaluation systems as they seek to assess implementation progress ahead of future impact evaluations and pave the way for rapid scale-up. 

As part of our series on evidence to scale, we wanted to more deeply understand how Kidogo’s leaders see data and evidence informing its model, now and in the future as it grows. We also wanted to hear from its team members what advice it has for other organizations at this stage in their work.

IDinsight: Can you share a bit about Kidogo’s journey with incorporating data and evidence into its work? How and why did evidence become a priority for the organization?

Chris Swanson, Country Director of Kidogo: Prior to working with IDinsight, our data systems were decentralized among various teams, inconsistently designed and managed, with too little coordination or integration across the systems. In 2021 our program grew more than 10-fold, from 47 franchisees to more than 500. At this scale, the weaknesses of our decentralized data systems become clear. Even the most basic data points – like our total number of franchisees – became difficult to monitor and communicate accurately across the various teams that rely on that information. It quickly became clear we needed to build a comprehensive, centralized data system that would accurately reflect key data points from our program, and be accessible and reliable for any team needing accurate operational data.

IDinsight: Part of your project with IDinsight is building a monitoring system and dashboard. What have you learned throughout this process?

Chris: Even if you think you understand your key performance metrics well, by zooming out and re-evaluating your theory of change, there is a high likelihood that you will identify entirely new research questions and key metrics that you should be monitoring to ensure you’re achieving desired outcomes (like reduced malnutrition) and overall success like program growth.

IDinsight: Do you think this monitoring system will affect the way you scale in the future? How would this compare to not having one as you look to scale?

Chris: I expect our new data and monitoring system to be extremely helpful in informing how we continue to scale our program. We plan to integrate a review of our Data Dashboard into our monthly management team meetings to help us monitor performance at a more granular level and identify specific areas or opportunities for improvement. Without our new system, we would continue to rely on teams to report on their own activities and performance, without clear data evidence to back up their assessments. This would not be a viable way for us to improve our program and continue our rapid growth.

IDinsight: For other organizations of similar size looking to scale up in the future, what advice do you have about how and where to invest in data and evidence systems?

Chris: For any organization looking to overhaul its data systems, I think it’s critical to begin by zooming out and first examining the theory of change in great detail. From there, you can identify the most important outcomes you are trying to achieve and the links within the theory of change that are most critical to achieving the desired outcomes. Your data system should focus on those links in particular, with associated metrics and KPIs that can focus your teams’ activities on strengthening those links to ensure success.

IDinsight: For donors looking to support organizations on their journey to scaling up, what suggestions do you have?

Chris: For organizations looking to scale to new heights, investing in solid data systems and processes is incredibly valuable. When operating at a small scale, organizations can get by with poorly designed systems that rely heavily on manual processes with inconsistent or mediocre data quality. But at a certain size and complexity of the program, these systems will break and fail to provide the evidence and guidance needed to improve program outcomes, optimize processes, etc. 

Donors and investors should understand that the process of improving data systems will take time. This is especially the case if previous systems provided data of questionable quality or accuracy, it will take time to build up a store of high-quality data, and until then, even the best data system will have only limited value for any organization.

This project was supported by the Conrad N. Hilton Foundation.