The first step to using data for collective impact is getting buy-in and agreement on what data your partnership will track. These resources will help you do just that.

Without continuously tracking and managing progress with data, it is highly unlikely that a collective impact initiative, or any large-scale change initiative, can achieve its goals. That’s why we launched our Data and Collective Impact series to help leaders better use data to achieve a shared result.

Through our collective impact portfolio, we’ve seen there are five steps to using data for collective impact. I outlined these five steps in the first blog in this series, and today we’re going to dig deeper into the first step: Agree on the Data. In this post, you’ll find stories to illustrate lessons learned on using data, as well as free resources to help you implement these lessons learned in your own work. Sign-up for updates on Data and Collective Impact to make sure you don’t miss any part of the series.

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Step 1: Agree on the Data

Before you do anything with data for collective impact, you need to know what data you and your partners care about. There’s a lot of data out there, and you want to be sure you’re focused on the data that actually speaks to the impact you are trying to create in your community.

This first requires setting and agreeing to a shared result for your initiative. This shared result is the “north star” of your initiative and should be ambitious but attainable, such as reducing unemployment by 10% in 10 years. (For more on how to create a share result, check out this free guide.)

With this shared result in place, you’ll then want to build out your data-driven feedback loop with population metrics that track to that shared result, as well as program-specific metrics to help you understand how your programs are contributing to those changes. You can track shorter-term measures that don’t speak directly to impact, as long as you understand how these short-term measures directly link to overall goal of your initiative. You can find examples of these types of metrics from our partners the Network for Economic Opportunity and All Hands Raised.

Resource Document: Data-Driven Feedback Loop Examples
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In our experience supporting leaders to use data for collective impact, we’ve seen there are four different things a collective impact initiative needs to agree on data: Facilitation skills; research skills; a framework for continuous improvement; and a data inventory.

Facilitation Skills

Someone involved with your initiative has to be a strong facilitator to get all partners to agree on the initiative’s shared result, and the right measures to track that shared result. The buy-in at the beginning of this process lays the foundation for success in your work going forward. There is a very real skill-set required for this (you literally need someone to arrange and conduct the meeting in which you agree to measures), but it also requires on-going facilitation and relationship building. For the actual meeting part, you can bring in an outside facilitator to help, but securing true buy-in requires lots of work on the part of the collective impact leaders to ensure that partners are fully committed to the work of the initiative.

We saw first hand the importance of facilitating buy-in through The Integration Initiative in Detroit. The Detroit collective impact work went through many different iterations, but ultimately the partners were unable to fully agree and commit to an overarching data framework for their initiative. They had measures, but in some ways they tried to do too much: At one point they had a 16 page theory of change. The partners went back and forth on committing to a collective impact approach, and after five years, Detroit decided to end its involvement with The Integration Initiative.

Research Skills

To get agreement on data, you’ll need to start with an initial list of measures that have been tested and verified in the focus areas of your work, such as obesity rates or high school graduation. Using these initial measures as a starting point can help the facilitation of agreement discussed above. Many initiative partners already have a sense of what measures make sense, but some basic research skills are needed to find and analyze a reasonable list of initial measures.

The capacity to find what measures make sense for your initiative does not have to be contained within the backbone organization, and can come from the partners themselves. The Seattle/King County member of The Integration Initiative, Communities of Opportunity, has created a data committee made up of staff from partner organizations that have research and data analysis experience. This committee was pulled together after the initiative agreed on an initial framework for their data-driven feedback loop, but the members have been able to go deeper in this framework to revise the measures as needed. (Setting up a data committee is actually a great way to help your initiative complete all steps in the use of data process.)

An Approach to Continuous Improvement

The science of continuous improvement is a huge part of using data in collective impact. There are many different tools and approaches to help you do continuous improvement. None are better than others—you just need to find the one that’s right for you. With an approach to continuous improvement in place, the remaining steps in this process will be a lot easier.

We use Results Based Accountability (RBA) with our Integration Initiative sites, and most have found it a helpful way to develop shared measures and tracking those measures at a population level. One thing sites are struggling with currently is using RBA as a performance management framework to improve their programs.

Our Prepare Learning Circle sites have used the A3 tool for their continuous improvement processes. The challenge with the A3 has been connecting the population-level metrics to the programmatic metrics.

Some initiatives have used Six Sigma, such as the Strive Partnership. This approach has been popular in large corporations, particularly those focused on manufacturing and industry, but can require a lot of quantitative capacity to implement successfully.

A Data Inventory

So you have a continuous improvement approach, the capacity to do research and analysis, and your partners have agreed to a set of measures to track. Even with all that, you may find that the data you want to collect doesn’t exist. Our partners in New Orleans, the Network for Economic Opportunity, decided they wanted to track the number of working-aged African American males earning family sustaining wages. As they began to look into collecting data, they realized they couldn’t really track this metric, and are deciding whether or not to pick a new one.

This will likely happen to you. You’ll be stuck at a place in which your partners have agreed to a set of metrics, but you can’t find the data to track these metrics. That’s when you need to inventory your data. Create a list of all the data you need, and how to get it. You may need to engage with different partners, or develop a new survey to collect non-existent data.

We’ve created a “Data Inventory” spreadsheet to start you on your data development process. This tool helps you outline what data you want, whether it’s accessible, and how important it is to get that data. Filling out this Data Inventory also serves as a bridge to get you from Step 1 to Step 2 of the data and collective impact process: Find the Data.

Resource Dataset: Data Inventory
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We’ll dive deeper into “Find the Data” in our next blog post. Subscribe to the series so you don’t miss it!

Resources to Help You Implement Lessons Learned

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