Summary
Your organization, operating in Social Services, Urban Development, and Economic Development with a small team (under 10 people) and budget (under $500k), demonstrates a well-established and impactful data practice. Data informs your mission across strategy and programming and AI is used to support specific tasks, reflecting a commitment to data-driven decision-making. You've successfully embedded data across roles, fostering growing leadership buy-in and staff data skills. Your organization also manages data securely, responsibly, and regularly adapts processes. Continued optimization and measurement are crucial for scaling results and proactively staying ahead of emerging needs and opportunities. Given your use of AI, further exploration of ethical and responsible AI practices, including addressing potential biases and ensuring transparency, will be important.
The summary text was edited by AI to enhance clarity and cohesion.
Your data maturity status is:
Data Driven
Overall score 7.8 out of 10
Your data practice is well established, with clearly observed and reported impact. Continue optimizing and measuring so you can scale results.
Overall score
7.8
Out of 10
Purpose
10.0
Application 10.0 / 10
Analysis 10.0 / 10
Strategy 10.0 / 10
Practice
8.5
Quality 10.0 / 10
Security 9.0 / 10
Responsible Use 10.0 / 10
Infrastructure 6.0 / 10
People
5.8
Leadership 7.5 / 10
Talent 2.5 / 10
Culture 7.5 / 10
Your AI maturity status is:
Operational
Score 6.3 out of 10
AI is being used to support specific tasks or functions. Your organization has identified use cases, taken steps to prepare your data, and is beginning to apply AI in more intentional ways.
Data: Purpose
How your organization maximizes strategy, application, and analysis to apply data in service of your mission.
Your organization effectively uses data to drive its mission-aligned impact through strategic and programmatic applications. Continue to explore resources that will strengthen the organization's data-informed purpose.
Recommended next steps:
- Explore complexity science approaches for data visualization projects.
- Utilize datasets to evaluate the fairness and robustness of AI models.
- Consult the Climate Data Guide for reliable information on climate data tools.
10.0
Application 10.0 / 10
Analysis 10.0 / 10
Strategy 10.0 / 10
Suggestions from the data.org resource library based on your results.
Application
Visualization Through Interdisciplinary Creativity
This resource discussed using Complexity Science, an interdisciplinary approach, to data visualization projects.
Application
Introducing Casual Conversations v2: A More Inclusive Dataset to Measure Fairness
This dataset enables researchers to better evaluate the fairness and robustness of certain types of AI models.
Analysis
Climate Data Guide
The Climate Data Guide provides concise and reliable information on the strengths and limitations of the key observational data sets, tools and methods used to evaluate models and to understand the climate system.
Data: Practice
Where the data hits the road — the support, tools, and processes your organization needs to responsibly collect and manage data.
Your practice is robust and responsive to emerging needs and opportunities. Focus on tools that can help the organization continue to manage and secure data.
Recommended next steps:
- Evaluate your organization’s technology adoption practices and policies.
- Review the guide for developing effective and secure data collaboratives.
- Utilize data impact assessments to maximize benefits and minimize risks in operational data management.
8.5
Quality 10.0 / 10
Security 9.0 / 10
Responsible Use 10.0 / 10
Infrastructure 6.0 / 10
Suggestions from the data.org resource library based on your results.
Infrastructure
Tech Accelerate Tool
This is a free assessment tool to evaluate an organization’s technology adoption, practices, and policies.
Infrastructure
Data Collaboratives Canvas
This guide details the phases needed for organizations to develop effective and secure data collaboratives across sectors.
Security
Data Impact Assessments
This guide explains Data Impact Assessments, which can help maximize benefits and minimize risks in operational data management.
Data: People
Talent, culture and leadership. This section looks at organizational skills as well as culture and buy-in.
There is meaningful engagement with data across multiple roles with growing leadership buy-in. Continue to foster data skills development and data literacy throughout the organization.
Recommended next steps:
- Utilize the guide to support organizations in building data literacy among staff.
- Reference the resource offering tips on hiring data analysts.
- Implement recommendations from the guide on leveraging technology equitably.
5.8
Leadership 7.5 / 10
Talent 2.5 / 10
Culture 7.5 / 10
Suggestions from the data.org resource library based on your results.
Talent
How to improve staff data literacy
Given organizations are more data-centric than ever, organizational data literacy is a foundational element of driving a data culture. This guide is designed to support organizations building data literacy among staff.
Talent
How to Hire a Data Analyst
This resource offers general tips on hiring data analysts, including sample required and preferred qualifications.
Leadership
Equity Guide for Nonprofit Technology
This guide provides a comprehensive overview and recommendations for organizations to leverage technology equitably.
AI Maturity
How your organization understands, adopts, and integrates artificial intelligence to advance your mission, whether through tools, strategy, or experimentation.
AI is used to support specific functions, demonstrating the organization's intentional approach. Identify opportunities to refine the data to ensure the right use of AI.
Recommended next steps:
- Identify how Generative AI can help improve organizational productivity.
- Research generative AI for better data exploration and understanding.
- Outline leadership capabilities in an AI-driven world, focusing on ethical data use.
Suggestions from the data.org resource library based on your results.
AI
How to improve productivity by optimizing usage of Gen AI tools within your organization
This guide has been developed to identify ways in which Gen AI tools can help organizations improve their productivity.
AI
Building an AI Analyst for Government and Nonprofits: Early Lessons in Working with GenAI for Social Good
This resource talks about using generative AI to help users better explore and understand the insights held within their data.
AI
Funding the Future: Grantmaker Strategies in AI Investment
A report examining how philanthropic funders are evaluating and approaching AI investment, with practical recommendations for the field.