Explore the Problem

Understanding the problem(s) your early childhood education collaboration is aiming to solve begins with the discovery process. Collecting and analyzing data while getting to know your community is at the foundation of this process. Numbers and stories reveal a great deal about how the current system works. Data also helps the community see resource gaps, opportunities for change, and other community system building needs. Discovery helps us slow down and avoid jumping to a conclusion quickly. 

To explore a problem, you must:

  1. Collect Data
  2. Make Sense of Data
  3. Take a Deep Dive to the Root of the Problem

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Collecting Data

Getting to know your community through a data collection discovery process will reveal opportunities, resources, and unmet needs. By collecting data from a variety of sources, a collaboration should be able to adequately answer: “What does the early childhood care and education landscape in our community look like today?” Community stakeholders can also be a great source of local data. Stories about high-need families, for example, can provide highly highly-useful qualitative data.

There are many ways to gather such information, including individual interviews, story collection, surveys, community listening sessions, or focus groups. Many collaborations employ methods learned through the Art of Hosting, including parent cafés and open space technology.

Examples of Data to Collect: 

  • Number of available early learning slots
  • Current enrollment data
  • Geographic income data
  • Head Start/Early Head Start data
  • CCAP Data
  • WIC Data
  • Human/social/community service agencies
  • K-12 data

Making Sense of Data

Analysis is the process of finding meaning in data to get to the heart of a problem. Data analysis helps the community see the system from a ”big picture” perspective. It also draws focus to key stakeholders, goals, and outcomes. Data analysis starts a conversation about effective solutions and feasible opportunities. As in all stages of collaboration work, it’s essential to engage a full range of voices in these data “sense-making” conversations. 

Here are some ideas for processes that can help guide conversations about data:

Take a Deep Dive to the Root of the Problem

We have a tendency to jump right in, eager to create change for families, without exploring root causes of the problems we are trying to solve. Solutions are often designed based on assumptions, or symptoms. Over time, if we have failed to carefully examine and understand root causes, the program or effort is likely to prove ineffective.

root-cause analysis provides more understanding of the current state of the world. A root cause asks “why?” several times until the problem’s basic reason is understood. Understanding the root will influence the development of strategies needed to solve the problem, rather than treat the symptoms, ultimately resulting in more successful outcomes.


Problem: Children are not enrolled in home-visiting programs

  • Why? Because the families aren’t interested in the service.
  • Why? Because the families don’t want someone coming into their home.
  • Why? Because the parents are concerned that the home visitor will call DCFS.
  • Why? Because the family is living in poor conditions.
  • Why? Because the family is doubled-up to afford rent.

In this scenario, now that there is a better understanding of the real issue, the collaboration can begin to design effective strategies that will make families feel comfortable with home-visiting by addressing the identified barriers.

For example, it is possible that a home visit could be conducted in a location other than the family's home. Imagine if the collaboration stopped asking “Why?” after finding out the family isn’t interested in the service: the solutions would be completely different and likely not move the needle. 

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