It is a capital mistake to theorize before one has data … one begins to twist facts to suit theories, instead of theories to suit facts. – Sherlock Holmes

Many early childhood community collaborations come together to address this issue: Not all children in the community are prepared to succeed when they enter kindergarten. Stating the problem is easy, but getting to its root is much more complicated.

Start with questions such as: What does it mean to be “kindergarten ready”? Do specific groups of children seem disproportionately unprepared? Where are those children before they enter kindergarten? Why isn’t the system on the whole working as we want? What resources are available in our community for young children and families? Are they being used? Do the resources meet our community needs? Are we duplicating family services?

In order to address a system challenge, the community must first understand the current state of the local early childhood system. This will help the community see the assets, needs, and opportunities by population, location, and more. Unpacking the data helps provide a deeper understanding of your community—and potential solutions.

Getting to know your community is a two-step process:

It is important to engage diverse perspectives as data is collected and processed. Key voices would include: individuals using the early learning system, people who provide services (both formally and informally), and local decision makers.

Getting to know your community through a data collection “discovery process” will reveal opportunities, resources, and unmet needs. Data collection shows the state of the current system by asking: What does our community look like today? Connecting your data collection to the community’s vision will help achieve the optimal solution.

To get started collecting data, Illinois communities should look at the Illinois Early Childhood Asset Map (IECAM). This map provides comprehensive data on the early childhood system, including valuable information on risk factors, demographic theme maps, early care and education options, and more.

IECAM allows communities to sort quantitative data by geographic level, including ZIP code, municipality, township, county, and school district. Geographic Information System (GIS) maps may also be generated to support your research. In Chicago, Chapin Hall collects early care and education program data by community area. Click here for additional data sources.

Community stakeholders can also be a great source of local data. Stories about high need families, for example, can provide highly useful qualitative data. An example about how a family that enrolled the older child in Head Start but did not connect the younger one to Early Head Start gives us an opportunity to ask more questions. This descriptive information reveals important information about how families and children experience the early childhood system.

There are many ways to gather such non-numerical 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.

When you host a meeting, be sure to pose questions that draw from the experiences of families and/or providers. Some questions to consider:

    • Do we have enough Head Start, Preschool for All, or high quality center-based care to serve every 3-to-5 year old whose family is at or below 185% of the Federal Poverty Limit?
    • Given the number of infants and toddlers at or below 185% of the Federal Poverty Limit in our community, do we have the capacity to serve everyone in Home Visiting, High Quality Center-Based Care, early Head Start, or Prevention Initiative programs?
    • Among those currently served, who is actually enrolling? Is it families with very high need, or first-come, first-serve?
    • Are very high need families enrolled in high quality early learning and development programs?
    • Does our community offer enough high quality (Gold, Silver) programs?

A community discovery template is a useful tool to prioritize data collection efforts and organize information. Groups may find it easier to gather certain data points, such as supply and demand of child care, and more difficult to get other data points, such as the number of homeless children aged 0–5 served in a community. IECAM also collects data on risk factors and other variables related to children development.

Analysis is the process of finding meaning in data to get to the heart of a problem. Data helps you check assumptions and biases. Data can answer questions, such as: Do the numbers match what we see in our community? Does the data reveal information we didn’t know about our community?

People tend to make decisions quickly based on available information. If I serve working families in my program, I may make decisions under the assumption that very low income families do not live in my community. Data helps us slow down and ensure people use a wide spectrum of information to broaden their perspective. This helps see a bigger picture for better problem solving.

Analyzing data helps the community see the system from a 20,000-foot perspective. It also draws focus to key stakeholders, goals, and outcomes. Data starts a conversation about effective solution 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 are can help guide conversations about data:

One main goal of the collaboration is to work together to improve outcomes for children. Knowing your community helps you design solutions that match defined problems as identified and quantified through the data collection process. (See the Understand the Problem section for more information).

A root cause analysis, or the Five Whys, takes you beyond symptoms and provides an extremely valuable tool for good problem solving. You can also learn more about using good data and good sense making in the design and evaluation of systems building work in the measure and evaluate section.

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