About the Assessment
data.org’s DMA helps organizations prioritize strategies for and investment in data and AI. By providing a snapshot view of strengths and opportunities, the DMA enables self-service connections to tools and resources to become a “Data Led” organization. The AI section offers a separate score, focused on AI maturity and application of AI tools readiness, and capabilities in AI application to mission and operations. The tool was built and enhanced based on extensive research and in consultation with social impact organizations tackling these topics around the world.
The assessment takes just over 12 minutes to complete and delivers a clear results summary to inspire thoughtful discussion and inform meaningful action.
In addition to providing a view of data and AI maturity, we match assessment results to relevant materials in our Resource Library. This library includes resources from a range of social and private sector organizations, yet remains agnostic on software and methodologies.
Categories
The data.org DMA provides a framework for assessing organizational data maturity within three categories.
Strategy:
How data-driven is your organization’s strategy?
Strategy is about how your organization uses data to define goals, make decisions, measure success, and identify areas for change.
Application:
How much does your organization apply data to better understand different areas of work, across programs and internal operations?
Application is about whether your organization uses data to better understand different areas of work, including program evaluation, market research, organizational efficiency, and more.
Analysis:
What types of data analysis techniques does your organization take advantage of?
Analysis is about your organization’s ability to use data to understand the past, predict the future, compile information across data sources, make decisions, and automate processes.
Quality:
What is the quality level of the data that your organization maintains?
Quality is about how trustworthy and therefore useful the organization’s data is currently, and how this trustworthiness of the data is maintained and improved.
Security:
How secure is the personal, private, or confidential data that your organization maintains?
Security is about ensuring that data is stored in accordance with regulations and in a manner that protects individuals.
Responsible Use:
Does your organization employ responsible data science practices in its data use?
Responsible use is about evaluating the appropriateness of decisions and practices regarding data and models, including data collection, storage, training, and use.
Infrastructure:
Is your organization able to select, purchase (as needed), maintain, and use technology tools and external data sources to maximize the use of data?
Infrastructure is about the funding, technology systems, and data that the organization has access to in order to build and sustain their data use.
Leadership:
Is your organization’s leadership prepared to lead the organization towards maximizing the use of data?
Leadership is about the buy-in, expertise, and support of those at the top of the organization to strategically use data for decision making and invest in expanding the use of data.
Talent:
Does your organization have access to people with the skills needed to effectively use, store, and interpret data?
Talent is about ensuring the data skills of staff and consultants are sufficient to maximize the use of data.
Culture:
How much is data embedding into the culture of your organization?
Culture is about the ability of staff across the organization to communicate about data, interact with data, share data across teams, ask difficult questions with data, and collaborate in data use.