Tackling childcare problems with data-driven decisions | EY


This increasing dialogue, reflected by startling cost statistics mentioned earlier, underscores the need for more affordable, efficient and actionable childcare solutions. By diving deeper into these issues and recognizing the real-life implications of poor childcare programs, state agencies are often left with no choice but to think strategically and explore options for a more informed, data-driven approach to respond to childcare challenges.

By assessing wide ranging datasets and using advanced predictive analytic methods and scenario planning, agencies can gain insights into pivotal factors such as understanding unmet care needs, identifying gaps in service accessibility, assessing quality of care and determining financial implications. Armed with these insights, agencies, policymakers, providers and families can make educated decisions, develop responsive strategies, and create supportive frameworks that positively affect childcare workers and families who use their services. Comparable to finding that missing piece of the puzzle, using data can bring clarity and direction in the initiative to increase access to and the quality of childcare.

States agencies are required to consistently collect and update data on childcare. They then report data to the federal government as part of their obligations under the Child Care and Development Fund (CCDF), a primary source of federal funding for childcare subsidies. This includes data on the children and families receiving subsidies, the types of care they use, the cost and duration of subsidized care, and the providers of such care.

The data is used by the Office of Child Care in the Administration for Children and Families (ACF), U.S. Department of Health and Human Services (HHS) for Congressional reporting, budgeting, and the development of research and policy studies, as well as by states for management and evaluation of their programs.

Furthermore, states are also encouraged to collect additional data to gain a more comprehensive understanding of childcare needs, availability and quality within their jurisdiction to inform policy decisions. This data may include the number of childcare centers and the number of children they serve. In this data collection, there is a gap in the ability to analyze and cross-reference that data that create meaningful funding allocation opportunities. With an influx of data, there needs to be an efficient way to track data-informed decisions to assess their impacts and effectiveness in the long term.

Often data analysis, which could be beneficial in real time, can take weeks or even months due to complexity and volume. Regrettably, much of the data analyzed is frequently outdated and, consequently, not useful in constructing an effective strategy. It becomes challenging to identify where essential workers reside for targeted provider support or to determine which regions are lacking in childcare spaces or those that are oversupplied. Using data to predict rising childcare needs due to population growth or decreasing needs as children age out of care is difficult.

In addition, the inability to track the outcomes of the funding allocated to centers is a major concern. It becomes nearly impossible to identify areas where centers are closing despite funding or where the demand for slots continues to outpace the supply.

In this landscape, states resort to the options available to them for funding and maintaining quality childcare centers without being able to translate the data they have to make the best possible decision. By integrating the available data and coupling it with up-to-date data tracing and analysis tools, agencies can do more than just comply with childcare data collection regulations. They can begin to use the data to explore solutions to help make impactful decisions regarding childcare provision. This level of detail and timeliness in data can also significantly influence decision-making, potentially leading to tailored and improved outcomes.


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