Today, we face twin challenges: improving impact while navigating a growing funding crisis. I believe reimagining how organizations generate, manage, and use data can help meet both.
This is not a magic bullet, but it is a path toward more effective and efficient programming.
After years working in traditional Monitoring & Evaluation (under every possible acronym), I’ve reached a clear conclusion: M&E has served organizations well for accountability, but its methods and tools are not sufficient to drive real-time, adaptive management for impact.
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While traditional M&E fulfills basic compliance, it consistently fails at its higher ambitions: understanding impact (what works, where and under what conditions). enabling evidence-based decision-making, and driving continuous learning and improvement..
The solution is not to improve M&E; it is a shift from M&E to Data, Analytics, and Learning (DAL). This is more than a change in acronyms. DAL’s purpose is to move organizations from compliance to decision-making, from static reporting to dynamic learning.
It demands new skills, technologies, tools, and an organizational culture that encourages candor and continuous learning. DAL not only supports improved program effectiveness, but it drives improved organizational performance and efficiency. .
1. The Impetus for Change
Adaptive management is key to responsive and effective programs that impact the lives of those we serve. It transforms data into insight and insight into action, and this enables organizations to improve performance, program design and delivery through iterative cycles of learning.
Putting adaptive management into practice requires timely, granular data to create insights into program implementation, quality, and impact and then make targeted and timely adjustments based on evidence. Traditional M&E cannot support this level of agility.
The Limits of Traditional M&E
Monitoring tracks routine program implementation to ensure quality, timeliness and proper resource use, while evaluation measures effectiveness and impact.
The problem is monitoring is ongoing during the program, while evaluation is periodic, often only conducted at the end of it. As a result, it can take years before organizations know whether a program is achieving its intended impact. Even then, evaluations fail to support precise and targeted adjustments, as they typically average results across broad regions, masking important local differences.
Evaluations struggle to capture contextual shocks such as conflict, disasters, or economic crises that affect outcomes. Cost and complexity often prevent the inclusion of comparison groups, limiting usability and confidence in their results.
Qualitative data faces similar challenges of timeliness, cost, and external validity.
The Data Revolution
M&E was developed in the mid-20th century to improve the performance, impact and accountability of the rapidly growing social sector.
This was a world of data scarcity, where information was rare, slow, and costly to collect. Where data existed, it sat in paper files, static spreadsheets, or aggregated reports. It was often too late or too coarse to inform decisions.
Organizations stretched survey data beyond credibility and built parallel reporting systems that overburdened staff to deal with this challenge.
Today, we live in an age of data abundance. Continuous, low-cost data streams from:
- digital systems,
- remote sensing,
- mobile devices,
- financial transactions,
- social media,
- agricultural sensors,
- administrative systems,
- organizational platforms.
This data is fast, granular, and automatically generated. This data revolution creates new, low cost opportunities to better understand programs, their context and how they affect the people they serve.
Contextual data from satellite imagery, conflict events, population movements, market activity, social perceptions, and climate trends are now continuously updated and available at fine geographic scales.
This data illuminates rapidly changing field conditions. It helps organizations plan with greater precision, respond faster to emerging challenges, and understand how shifting contexts shape their results.
Simultaneously, digital administrative systems such as electronic medical records, education databases, and civil registration and vital statistics (CRVS) are expanding rapidly.
These systems track individuals over time, transforming how services are delivered and how performance is measured. They create new opportunities to strengthen case management, improve service quality, and generate more precise, timely insights into outcomes.
Traditional M&E lacks the tools, technologies and skills to capitalize on this data revolution, but DAL is built to handle it.
2. Introducing Data, Analytics, and Learning (DAL)
The Data, Analytics, and Learning (DAL) team transforms how an organization generates, manages, analyzes and uses data. It is not just a technical unit; it is the engine that powers real-time, evidence-based decision-making across the whole organization.
The Team:
The Data team manages the organization’s data to ensure it is comprehensive, timely, and accessible. They define standards, build and integrate systems, enforce governance, and ensure compliance with security and privacy requirements.
The Data team enables staff to access reliable, unified data across the whole organization. Working closely with technology teams, they also support organizational digital transformation to create the data foundation.
The Analytics team turns raw data into actionable insight. They automate routine reporting, create dashboards to highlight trends and anomalies, and use advanced analysis to inform decisions. By connecting data to outcomes, they enable adaptive management, allowing programs to innovate, test assumptions, and redesign interventions based on real-time results.
The Learning team ensures that insights are translated into action. They strengthen the organization’s data literacy, foster collaboration across departments, and embed a culture of continuous improvement.
By synthesizing lessons across programs, they preserve institutional knowledge and promote iterative learning so that evidence drives decisions at all levels.
How it works:
DAL is not a M&E rebrand; it is a reimagining of the organization and its operations.
Unlike traditional M&E, which mainly supports the program teams, DAL serves the entire organization. It breaks down silos and creates a shared source of truth by connecting data from finance, HR, supply chains, and field operations.
By integrating systems and linking operational and program data, DAL gives teams and leaders real-time visibility from the field to the headquarters. It enables cross-functional analytics and decision-making across the organization.
However, the greatest shift is cultural. DAL succeeds only when staff use data to plan, act, and learn, rather than just to report.
The learning team helps build a culture of data literacy and collaboration. In it, leaders model data-driven decision-making and staff have the skills and confidence to interpret insights. Everyone is encouraged to question, experiment responsibly, and freely exchange ideas.
DAL transforms the organization into an learning one. From design to review meetings to field operations, DAL embeds evidence-based decision-making everywhere, so the organization can move beyond reporting results to actively improving them.
Finally, in this growing funding crisis, DAL is not simply an IT upgrade or an added cost.
Through re-imagining the organization’s system and operations, it creates the opportunity to save costs while increasing organizational efficiency and effectiveness.
3. Digital Transformation
Digital transformation is the starting point of DAL. It creates systems, digitizes workflows, and generates data that make adaptive management and continuous learning possible. With these foundations, organizations can turn data into insight and insight into better performance across every level of their work.
The DAL team works with the technology teams to strategize, design and develop digital infrastructure.
Enterprise Data System
For many organizations, information still lives in separate systems—finance in one place, human resources in another, field data in spreadsheets, and project results in a portal. This fragmentation slows decisions and hides the connection between resources, activities, and results.
Working with ICT, DAL begins by creating a shared digital backbone for the organization: an enterprise data system.
Whether operations, finance, human resources, program or external data, the enterprise data system creates a platform where all the data used by the organization is collected, managed and stored under common definitions and governance.
Rather than replacing existing systems, the enterprise system connects them through shared identifiers and automated data pipelines, allowing information to flow seamlessly and securely across departments.
When data integrated and consolidated, it becomes possible to see how the organization actually works as a whole. Managers can link spending to goods, activities and outputs, HR can track staff deployment against program needs, and leadership can trace results back to the resources that enabled them.
It connects cost to impact, operations to outcomes, and evidence to action.
The enterprise data system is the infrastructure that enables both analytics and learning. It ensures that the data needed for decision-making are available, so every team, from field offices to headquarters, can work from the same reliable source of truth.
The Organization’s Data
Whereas traditional M&E digitizes program’s data collection, DAL digitizes the organization itself. Digital transformation extends beyond surveys and checklists to the daily processes that run programs and operations.
From procurement to HR onboarding to financial transactions to travel approvals, routine operations and processes are digitized and stored by the enterprise data system. Digital transformation reduces labor, while simultaneously generating structured, time stamped data.
This data powers organizational improvement while improving transparency, compliance, and accountability.
For programs, digitized workflows transform field work. Whether it is supervising health facilities, delivering community training, or conducting household visits. These tools ensure consistency and quality while automatically producing usable data for program improvement and impact.
For example, a health supervisor using a digital supervision kit, not only improves the quality of the visit (and hopefully the clinic’s services) but also creates a real-time record of service gaps, staff availability, and supply shortages.
Digital workflows simplifies staff work and speeds up the feedback loop at all levels, so organizations learn faster and improve both efficiency and impact.
Expanding Data Horizons
Beyond the organization’s own systems, DAL and its enterprise data system prepares the organization to harness the data revolution.
As these global, national, and personal data streams grow, DAL helps organizations use them safely and effectively. It can link internal performance data to external context and outcome data to gain a fuller understanding of program’s and their impact.
This drastically expands the organization’s usable data without costly and time-consuming data collection.
The Digital Organization
Digital transformation turns everyday work into multi-use data and fragmented systems into a single, intelligent one. It creates the foundation for improved program effectiveness and organizational efficiency.
When coupled with analytics and embedded into organizational learning processes, digital transformation changes from an IT upgrade into a strategic capability.
4. Improving the Organizational Performance
The development of the enterprise data system and wider digital transformation create an opportunity to re-imagine the organization and it operations, while delivering tangible savings.
Digital Transformation for Efficiency
Developing the enterprise data platform streamlines the organization’s patchwork of applications into an integrated system. Outdated or duplicative tools can be updated, formalized, or consolidated.
This ensured a planned system that is more robust, flexible and suited to whole organization.
This consolidation creates cost efficiencies in technology and staffing. The system consists of fewer software licenses and vendor contracts. It’s less effort to maintain and troubleshoot than multiple bespoke ones, so instead of departments maintaining their own separate systems, technology team and DAL share this responsibility and reduce overhead across the organization.
A centralized cloud service eliminates the need for local infrastructure, further lowering operational costs while maintaining access from any location.
By standardizing data and automating routine processes—from approvals and procurement to HR onboarding—DAL streamlines workflows and reduces duplication of effort across teams.
The platform enhances collaboration and accountability by making information easily accessible across departments and country offices. It provides clear task tracking and supports compliance-ready, auditable records, and this helps teams work more efficiently while strengthening oversight.
The result: staff spend less time on mundane tasks and can focus continuous improvement and strategic decision-making.
Automating Reporting
Traditional M&E reporting meets accountability needs but often ties up staff across every level, from field teams compiling spreadsheets to HQ staff reconciling inconsistencies. And its support usually stops with program data, leaving operations to manage their own parallel systems.
DAL simplifies and accelerates this process. Through data governance, automated pipelines, and integrated dashboards, reporting becomes continuous and organization wide.
This shift transforms reporting from a periodic burden into a steady flow of insight. Staff spend less time preparing reports and more time using them to guide operational and strategic decisions.
The Data-Driven Organization
With the organization’s data integrated and centralized, DAL enables deep operational insights.
Leaders can see how the organization truly works—how resources move, where bottlenecks form, and where improvement will have the greatest effect. Organizations can calculate the true cost of operations, identify hidden cost drivers, and align spending with results.
They can measure staff workload and match skills to actual tasks, ensuring people are deployed where they add the most value. Scenario planning can be driven by integrated HR, finance and operations data to improve needs forecasting in staffing, logistics or program delivery
Data governance creates clear data definitions and quality standards across the whole organization. From headquarters to field offices, teams can compare performance across countries, identify successful practices, and target specific areas for improvement.
Data no longer sits in silos; it moves through the organization as a shared learning resource.
This is what it means to be a data-driven organization: using information not only to account for performance but to improve it, turning analytics into a core capability for efficiency, strategy, and learning.
5. Serving Better
International development and social sector organizations are not defined by the profits they create for themselves; instead, we exist to improve the lives of those we serve.
Those who work in the sector are dedicated and sincere, but we also need to be honest and clear-sighted. Does our work positively impact these populations? How do we improve our impact? How do we do this across time and context?
Traditional M&E was developed to answer these questions, but it has struggle to do so. DAL can do better.
Performance Monitoring:
Performance monitoring uses the organization’s planning and implementation data to track whether programs are delivered as planned, reaching the right people, and maintaining quality and efficiency.
DAL turns this from a reporting exercise into a real-time management tool for all staff. Instead of waiting for quarterly reports, managers can see how programs are performing as work happens and make quick, informed decisions. Field staff receive targeted real-time feedback and support to improve their activities.
DAL’s ability to draw on both organizational and external data stream provides the organization with a fuller operational picture.
Managers rely on analyzed, granular data to make informed decisions about performance, implementation quality, and resource allocation.
Using both organizational implementation data and available external data can turn performance management into targeted problem-solving and continuous improvement. For an education manager, individual level school attendance and learning data can highlight at-risk students and broader population challenges.
It helps to design targeted interventions and observe results in near real-time. Real-time performance management lets managers, at all levels, monitor service delivery as it happens to quickly spot delays, gaps, or underperformance and then take corrective action.
For frontline workers, DAL supports improved service delivery and tailored case management. Digital records help track individuals over time. When a client misses a health visit or a child falls behind in schools, digital systems can prompt and record follow-up.
This makes it easier for equitable delivery and to close gaps in care, learning, or service delivery.
With improved data and analytics, performance monitoring becomes the driver of daily learning and improvement to keep programs responsive and accountable.
Real-Time Data Drives Real-Time Learning
Traditional M&E struggles link program activities (monitoring) to program outcomes and impact (evaluation) in a timely manner. Without clear and timely evidence of impact from activities, continuous learning to improve impact is hampered.
With its data infrastructure and analytics and learning capabilities, DAL can help to connect program activities to real-world results in a timely manner to answer essential questions:: What is working? Where? For whom? Under what conditions?
To achieve this, DAL blends digitized organizational data and external data streams to build analytical models that mirror a program’s theory of change. This provides continuous information on inputs and outcomes, so teams can use these insights to guide learning feedback loops.
Working cross-functionally, they can develop targeted strategies, test hypothesis and interpret emerging patterns. Rather than waiting years for evaluation results, managers and teams can adjust programs based on continuous evidence.
Integrating contextual data on demographics, economics, environmental, and conflicts reveals the conditions under which programs succeed or struggle. These contextual insights are especially crucial in humanitarian settings, where conditions evolve rapidly. Managers and team can truly understand their program and their impacts.
For example, an immunization program using mobile reminders can assess whether these efforts are effective? Combining electronic health records with program activity data, the team can determine whether the strategy works with high-risk populations. What about in different settings: Conflicts? Urban slums? Or nomadic settings?
Organization’s can close the gap between performance monitoring and evaluation, turning data into evidence, and evidence into smarter, faster decisions.
Predictive and Adaptive Management
With predictive analytics, DAL allows organizations to anticipate change, not just react to it. Predictive analytics uses historical and contextual patterns to estimate what is likely to happen and under what conditions. It turns data from hindsight into foresight.
These models can flag risks and opportunities before they surface. Developing risk profiles, frontline workers might alerts about students likely to drop out or patients at risk of missing treatment, allowing for timely, targeted interventions.
Managers can forecast service bottlenecks, such as supply shortages or staffing gaps, and act before disruptions occur. They can develop scenario tests with greater detail and then update models with new results.
Over time, predictive analytics transforms adaptive management into a continuous, proactive process
DAL turns prediction into preparation. It enables organizations to allocate resources strategically, safeguard performance, and sustain impact even in complex and changing environments.
6. A More Resilient Organization & Workforce
As an added bonus, shifting from M&E to DAL could improve organizational resilience and its workforce
M&E was always only a social sector function; whereas, adopting DAL would immediately better align with the private sector. It would facilitate greater learning between the two. Knowledge and skills would be more transferable, and DAL staff could move more fluidly between the two.
DAL can create a shared language and set of skills between the two sectors, even if we use them for different ends.
7. What Happens to M&E?
Monitoring and Evaluation activities do not disappear, but traditional M&E as a separate department should. While these activities are crucial to accountability and improvement, their design and implementation need to be updated
Yet there is an essential M&E skill that I haven’t explicitly covered: Measurement. Measurement defines what matters, what to track, and how to interpret results.
Indicator development, survey design, baseline and endline analysis, and qualitative assessment—these were traditional M&E skills and they remain vital. But we need to develop new ones too.
Skills like causal inference, predictive analytics, longitudinal analysis, AI / machine learning, rapid-cycle learning, and dynamic frameworks allow organizations to harness the data revolution, improve organizational performance and power adaptive management for greater impact.
Measurement skills should no longer be locked away in single department (M&E); rather, they must be embedded across programs, operations, and leadership to ensure decisions are evidence-based.
This supports comprehensive measurement and improvement strategies for the whole organization, like KPIs, Balanced Scorecards, or Organizational Results Frameworks. It enables teams and leaders to track effectiveness and efficiency and improve overall organizational performance.
To achieve this, DAL needs to build measurement and data literacy throughout the organization.
Whether it’s Analysts working with technical specialists to define data and indicators or Learning specialist helping teams to interpret their data and make evidence-based decisions, DAL needs to build this capacity and make data and measurement a shared responsibility as opposed to a siloed function.
8. Where to Now?
Moving from M&E to DAL shifts an organization from compliance and reporting to learning and continuous improvement. DAL enables organizations to understand what works, adapt accordingly, and improve results in real time.
DAL is not an instant fix; it takes time. It requires rethinking how the organization works, investing in modern systems and skills, and building a culture of candor, curiosity, and evidence-driven action.
But with these in place, organizations become more resilient, more efficient, and better equipped to improve the lives of those they serve.
By Craig M Arnold, Performance Monitoring and Evaluation Director, SoCha
