By Peggy Chang 


There are two types of people when it comes to data: the type that gets excited, delves into details, and eagerly plows through spreadsheets, and the type that groans and goes to pour another cup of coffee to make it through the meeting.

It is completely natural for us to have these reactions. Our brains make associations from our experiences and when we see something that looks familiar, we make the leap into what we think it means. For some, the topic of data conjures up excitement; others assume it will be dry or challenging.

For most of our lives, this works well. I see someone looking at a map and assume they are lost. I hear ambulance sirens and assume there’s an emergency. Easy enough.

But, assumptions can also lead us down the wrong path. In the social sector, this can translate into, “I know what families in my community need,” and “I know where I need to allocate my resources to have the most impact.” While these assumptions can often be later validated by data, sometimes surprises emerge.

In a recent client engagement, four organizations decided to partner to improve the conditions of children and families in their county. Most in the group believed they knew what particular county zip code was in the most need of services and interventions. Nevertheless, since they were beginning a new partnership, they agreed that the first step should be to get a common understanding of the county’s service landscape. VIVA was brought in to compile data by zip code to guide the development of their new collective impact effort.

The results were unexpected. The map clearly showed two zip codes with high needs that were significantly underserved, but that had previously been assumed to be low-risk areas. Thanks to the data mapping, the partners working together were able to shift their thinking and better target their efforts.

Through this and other examples, we’ve learned:

  1. Data doesn’t have to be perfect—but it should be the best you can get. In this example, data was provided by each partner agency and was the best they could collect at the time.
  2. Data doesn’t have to be scary. There are ways to package information so that people with all backgrounds can easily understand and make use of the data set.
  3. You don’t have to be a data whiz to make it happen. In fact, keeping the data simple can help bridge the gap between the numbers on the page and the real world.

There will never be a day when people do not make assumptions or have strong thoughts about actions that need to be taken. Especially during these times, it is important to utilize data to test assumptions, better inform planning, and support fact-based decision-making. If it means we are able to target our efforts for greater impact, then I think we can all come together—cup of coffee in hand—and dig into those spreadsheets.