More effectively putting decision making at the center

Getting to action with monitoring, evaluation, and learning in the social sector

This characteristic is about the purpose of monitoring, evaluation, and learning. In the better future, data collection, analysis, and interpretation activities more effectively put decision making at the center of monitoring, evaluation, and learning efforts. Measurement should aim to inform better strategic, operational, and portfolio decisions among both philanthropic funders and grantee organizations. Explore more in this section of the Re-imagining Measurement toolkit.


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 first characteristic for a better future: more effectively putting decision making at the center.

Re-imagining measurement strategic learning toolkit

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

More effectively putting decision making at the center

More effectively putting decision making at the center is about the “why” of monitoring, evaluation, and learning. It involves both the generation of data-driven insight and its application at an important organizational moment to change behavior. If we focus on the questions we want to answer, their importance for essential decisions about how to allocate resources make strategy adjustments clear:

  • What do we want to accomplish?
  • Are we doing what we said we would do? (Should we be doing something else instead?)
  • How are we doing and what can we do better?
  • What impact are we having?

On the one hand, this is obvious, and data for decision making is an idea that’s widely embraced. However, it is difficult to achieve in practice. Too often the starting point for measurement is understanding reporting requirements and on what metrics and methods to use, rather than on deeper questions about what decision makers need to know to make smarter choices about creating impact. As one expert told us, “Instead of evidence-based decision making, we need decision-based evidence-making.” Strategy and evidence-based decision making require more agile and continuous feedback loops that link decisions with the right data, the development of persuasive analysis, and the integration of these insights into decision-making processes.

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More effectively putting decision making at the center

A better future for putting decision making at the center is one where:

Information for on-the-ground decision making is prioritized

In a better future, data collection, methods, and analytic tools balance the need for rigor with practicality, relevance, and responsiveness. Funders consistently work with grantees to agree on reporting that informs grantee decision making, reduces the grantee reporting burden, and satisfies funder needs. Priority is given to the information needed on the ground by grantees to best serve constituents.

Learning is embedded and continuous

In a better future, reflective practice is embedded in foundation and nonprofit culture. Practitioners no longer say “I want to but I don’t have time” when learning initiatives are mentioned. Continuous learning allows for experimentation to iteratively test approaches.

There is greater investment in monitoring, evaluation, and learning capacity

In a better future, it is standard to have sufficient capacity among nonprofits and foundation staff to analyze data and think evaluatively. Funders prioritize building capacity in ways that benefit the field broadly.

The data and methods needed to inform decisions are available

In a better future, greater investment in the supply chain of data addresses the often insufficient and low-quality data that is currently pervasive in the social sector. With greater investment in data, big data and data analytics tools and techniques like predictive analytics are increasingly helpful in answering key questions. Further, social science and data science methods are integrated so that practitioners can use the best approach (or a hybrid approach) for a given decision.


To learn more about more effectively putting decision making at the center, including bright spots, opportunities, and calls to action, explore the PDF.

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