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Analysis

More productively learning at scale

Knowledge-sharing and collaborative learning between social sector organizations

This characteristic speaks to alignment with others through monitoring, evaluation, and learning; it is about getting better at learning from and with other actors—about the good, bad, and inconclusive—to better match the scale and complexity of today’s social and environmental problems. Explore more in this section of the Re-imagining Measurement Toolkit.

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Re-imagining measurement toolkit

The Re-imagining measurement toolkit includes a range of innovation materials for getting to a better future for monitoring, evaluation, and learning.

This section provides information about the elements of the third characteristic for a better future: more productively learning at scale.

Re-imagining measurement strategic learning toolkit

[brief description of this piece's place in the toolkit and the toolkit overall]

More productively learning at scale: The open data movement

More productively learning at scale encompasses the interrelated but distinct ideas of knowledge-sharing and collaborative learning. Knowledge-sharing involves individual programs and organizations offering what they are learning to others: the good, the bad, and the ugly. Collaborative learning refers to cross-program or cross-organizational efforts to collectively create data and information that everyone can use.

Knowledge-sharing allows the social sector to marshal its resources effectively by avoiding duplication of effort in articulating social problems, developing potential solutions, and determining what works in what contexts. Through knowledge-sharing, organizations can build on what has come before them rather than recreating knowledge for individual use or replicating solutions and strategies that have previously been found insufficient.

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More productively learning at scale

A better future for more productively learning at scale is one where:

Data, learning, and knowledge are shared openly and widely

Foundations and nonprofits use monitoring, evaluation, and learning efforts to build evidence, and they share the results regardless of strategy or program success. They share information as openly and widely as possible while still respecting ethical considerations. Prior to sharing, organizations invest in translating their information so that it is helpful knowledge for other actors.

Knowledge gaps and learning agendas are collaboratively undertaken

There is greater focus on working collaboratively with actors in the system and learning together as a field. Organizations within issue areas consistently work together, perhaps via formalized communities of practice, to identify knowledge gaps, collaborate on learning agendas, and build collective evidence.

Data is integrated at the scale needed to assess social impact

Data is integrated across organizations through common indicators or data interoperability. Shared data systems are common and developed using open source, open standards, and open innovation principles and practices. Integrated data systems overcome data silos and facilitate issue-level learning at the scale of the problems that organizations seek to address.

Evaluation synthesis, replication, and meta-evaluation are supported

Evaluation synthesis, replication, and meta-evaluation become standard practice. Investments in shared infrastructure enable organizations to access higher quality information and synthesize, replicate, and learn from that information. Synthesis, replication, and meta-evaluation enable foundations and nonprofits to make effective strategic choices based on that information.

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To learn more about productively learning at scale, including bright spots, opportunities, and calls to action, explore the PDF.

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