Make A Donation
Read The Data
Make A Donation
Guiding Principles for Data Use
Data as a Flashlight
We believe data is used effectively when it is a tool for illuminating and learning to better support students’ success along the cradle-to-career continuum. Using data in this way while authentically and intentionally including community expertise and lived experience, empowers the community, students, families, and service providers to take action for change.
Everyone in our community shares in the responsibility of ensuring children and young people can achieve their full potential. Facilitating effective and safe use of data within and across sectors, with leadership and on-the-ground staff, promotes shared accountability for advancing equity and long-term impact. Further, we believe data on results is never indicative of the success or failure of any one program, organization, sector, or group of people.
We believe being clear about why and how we are using data is important for building trust with community and partners. Additionally, partners should be informed of what kinds of data is being shared and with who to ensure that data is used in alignment with the goals and mission of C2C.
Data and Equity
Our partnership tracks data that shows where our children and young people are on eight community-wide indicators, and we provided up to date access to this data on our Data Dashboard. This data is important to track not because it shows that our children are failing or that hard work is not being done by our community, but that the systems currently in place are still not working equitably for all. Our Black, Latinx, and Native American students are experiencing these inequities at higher rates than their white peers.
Data is a powerful tool in this work, but without an equity lens it can do more harm than good.
As you explore this data, we encourage you to keep these insights in mind:
Talent is Equally Distributed, Opportunity is Not
Student-level data provides important information to support continuous improvement and track progress over time. However, this data is the result of systemic, structural, and institutional inequities.
Getting to Root Causes
Data can help tell us what is, but not as easily why it is. Assumptions are often made as to why the data looks the way it does, and solutions developed without including children, youth, and families with lived experiences. Our partnership seeks to draw on this rich qualitative data to idenitfy root causes of inequities.
Shifting Power Dynamics
True systems transformation requires that community members most marginalized by current systems, including those who do not hold formal authority, are at the table to co-design solutions that truly work for the communities we serve.
What are systems-level indicators?
Systems indicators are measurements, both qualitative and quantitative, that help us acknowledge and reveal inequitable distribution of resources, decision-making power, and opportunities within systems, organizations, and institutions.
Why look at systems-level indicators?
Individual-level outcome data is important, but it is not sufficient for transforming systems for children and youth of color. Outcome data must be situated in the appropriate social, historical and political contexts to truly understand disparities and identify ways to assess and disrupt inequities.
Systems-level indicators are meant to put the responsibility of outcomes on those with power rather than those without.
What to keep in mind as you explore our community’s data?
We encourage you to look at the student-level outcomes in the context and history of larger systems – the lack of affordable and reliable internet to all households, inequitable access to high-quality childcare, the under-resourcing of public education.
Only when the conditions that perpetuate inequity are disrupted will the opportunity for economic mobility no longer be out of reach for so many children with regard to race, income, and zip codes. Often those conditions are engrained at the systems level and contribute to the disparities seen in individual-level student data.