Whether you're a policymaker, philanthropic leader, or nonprofit strategist, you likely sit on a mountain of dashboards, reports, and data—with no shortage of information, but plenty of questions.

By 2025, the volume of digital information generated per year is projected to reach 175 zettabytes—over five times more than 2018 levels. But as American scholar Herbert A. Simon observed, “A wealth of information creates a poverty of attention.” 

More data doesn't guarantee better decisions.

And yet, using data to make smart decisions has never been more important. Official development assistance, down 7% in 2024, faces continued constraints as donors adjust to fiscal and domestic pressures. Globally, more than three billion people live in countries that spend more on debt servicing than on education or health. At the same time, philanthropic giving is becoming more conditional, with funding often awarded to organizations with the strongest performance metrics.

Across sectors, there’s pressure to do more with less, and prove it works. To rise to the moment, organizations need Measurement, Learning, and Evaluation (MLE) systems that not only measure—but guide.

MLE as a Management Tool

A well-designed MLE system is a tool that helps organizations collect and use data strategically to ask better questions, reflect in real time, and make stronger decisions. 

Too often, however, MLE systems become compliance exercises, leading to data overload instead of data-driven impact.

Another common pitfall is letting perfection become the enemy of good—prioritizing complexity and comprehensiveness over simplicity, timeliness, and practical value. Other times, MLE focuses too heavily on measuring what's easy to control rather than what matters.

The ultimate test of an MLE system is its usefulness. Leaders don’t need more data—they need the right data, delivered at the right time, in the right format, and connected to the decisions that matter.

Strong MLE systems enable organizations to turn numbers into knowledge by: ‍

1. Creating high-quality data on the right metrics; data is relevant, timely, and purpose-driven.
2. Collecting, organizing, and storing data systematically, ensuring consistency, integration across sources, and ease of use.
3. Generating focused insights through contextual analysis and interpretation to uncover trends, surface challenges, and identify opportunities.
4. Taking action by incorporating insights into informed decisions and strategies that drive meaningful improvements in outcomes.

This is what transforms a 50-page spreadsheet into a one-page brief that directs attention, reduces cognitive load, and informs action. 

From our work with governments, philanthropies, multilaterals, and nonprofits we’ve identified three principles for building MLE systems that drive decision-making and impact.

3 Principles to Build a Strong MLE System

1. Anchor MLE systems in real decision-making.

The key feature of a sound MLE system is that it serves clear purposes. Organizations typically use MLE in four ways:

• Strategy – To inform decisions and course-correct.
• Storytelling – To demonstrate impact to funders, staff, and the public.
• Accountability – To ensure transparency and stewardship.
• Learning – To enable continuous improvement.

Each metric must earn its place by serving one of these functions. If you can’t answer, “What will we do differently based on this information?” it’s not worth collecting. 

This is especially important in 2025, as many governments and philanthropies are rationalizing systems to reduce costs while increasing impact. Institutions like WHO and the World Bank have shown that prioritizing and simplifying their systems helps sharpen—not reduce—their strategic focus.

But even once a metric has earned its place, its significance needs to be communicated to the broader organization. It’s common to see M&E teams operating in isolation from the rest of the organization—but they shouldn’t be. To be effective, they must deeply understand a metric’s use cases and engage across departments and functions—from communications and fundraising to program officers and strategic decision-makers. MLE should be embedded, not siloed—serving the full spectrum of use cases and enabling smarter decisions at every level.

2. Balance trade-offs with intention.

There are real tensions in designing MLE systems. Common tensions include:

• Rigor vs. practicality – Generally, the more rigorous and comprehensive an MLE system is, the more costly, complex, and difficult it becomes to implement and use—and the reverse is also true. 
• Timeliness vs. depth – Frequent, early indicators may lack depth, while richer data often comes too late. A healthy mix provides timely guidance now and deeper insights later.
• Standardization vs. customization – Common metrics across programs allow for comparison, but may miss project-specific subtleties and relevance. Balance is key—use comparable metrics where they matter for decision-making, and complement them with context-specific indicators when capturing local nuances or unique program features.
• Outputs vs. outcomes – Easy-to-track outputs are often more accessible and timely, making them useful for guiding implementation—but they can fall short in showing whether real change is happening. For instance, tracking the number of trainings delivered, or outputs, doesn’t capture meaningful outcomes such as resulting job placements. A sound MLE system captures and links the two, helping teams test assumptions and adjust in real time.

It’s critical to be intentional when navigating these trade-offs—striking the right balance based on the organization’s learning needs, resources, and capacity.

3. Design for the future, not just the present.

Good MLE systems are built to scale, adapt, and learn over time. Here are six factors to help ensure your MLE system is built to withstand:

• Start with usability – Boost system adoption by ensuring users can easily enter and interpret data and draw actionable insights.
• Plan for scale – Anticipate the capacity, flexibility, and governance your system will need in response to organizational growth. For example, consider how your system will evolve to support new products, programs, or locations.
• Invest in data infrastructure – Build a focused, interoperable backend that can integrate knowledge across multiple sources. As we enter an era built around AI, the future will demand systems that support queries and real-time learning.
• Prioritize continuous improvement – Constantly revisit the system to adapt it to evolving needs and learnings. Building a strong MLE system is not a one-time task—it’s a journey of continuous improvement. 
• Design the system to fit the organization’s capacity – Ensure MLE supports rather than strains organizational capacity. It’s often wiser to start small, then scale as capabilities grow.
• Make results visible – Build organizational buy-in to the MLE system by sharing results. Demonstrating the value of people’s MLE efforts helps them see how their contributions matter and connect to a larger purpose.

Looking Ahead

In a high-stakes, changing global landscape, effective MLE systems can help leaders make sense of complexity, navigate reality, and use data to make evidence-based decisions that deliver impact.

Applying these three principles can help ensure your MLE system is a practical decision-making tool—not just a compliance checklist—and that it delivers clear value across the organization.

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