Daunting Data

This post is authored by Ryan Ayala, MSW, LCFC Program Assistant


I’m writing this on an eight-seater plane (well 10 if you count the pilots up front). We are flying down to Alamosa for a training with an organization on strengthening and evolving their capacity and sustainability. To distract me from the lingering thought of impending doom as we take off, I’ll discuss data.

We know that data talks in the modern-day funding world. But we also know funders often struggle to make impact through their grants— while also requiring large swaths of data. This training asked foundations to look at data as a living, evolving, and intentional process; rather than an item listed on grant objectives and reports.

The challenge: how do we learn from data?
Organizations track two types of data—physical and metaphysical data. For example, physical data is the number of times a client comes to groups, attends training, etcetera. Metaphysical is the data tracking ambivalence, fears, safety concerns, mental health improvements, and social determinants, to name a few. Both are important, but there needs to be a purpose to collecting data.

What is the question we are wanting to investigate?
The trainer, Andrew, discussed the two types of directions data can be taken: causal and predictive. Causal questions ask for the reasons behind what data shows, while predictive ask for action to be taken based on the data. Often foundations and organizations tend to focus more on causal outcomes rather than predictive outcomes; meaning we are left with lots of “interesting” afterthoughts with no evolution in programing, thereby regurgitating the same design, outcomes and even the same data year after year. Looking at that approach differently, as a program assistant with the Latino Community Foundation of Colorado, I want to utilize data holistically and dynamically, and evolve our programs with data, along with our grantees.

Creating a Data Community
We know that data collection is time-consuming: It’s cumbersome and for many daunting. We also know that when data is used correctly and effectively, data can create programs and impact change in ways that address the needs of a community and initiate evidence-based projects that will improve communities.

In order to ease the anxiety around data, a movement of shareable data and methodology stops the redundancy between organizations collecting from the same populations and allows us to move into predictive steps rather than casual steps. Utilizing opens source software and universal data formatting are simple ways to share data across platforms and organizations. Our lives exist now in both the physical and metaphysical, just as data does.

Let’s begin sharing it like we do our lives with those around us working towards a more equitable just society.