1.1 IM vs MEAL, KM and ICT
When working in humanitarian or development programs, terms like Information Management (IM), Monitoring, Evaluation, Accountability and Learning (MEAL), Knowledge Management (KM), and Information and Communication Technology (ICT) are often used. At first, they can sound confusing or overlapping — but understanding the differences (and connections) between them is critical for working effectively with information.
1.1.1 Understanding Each Domain — From a Programme Perspective
Programme delivery is the core business of humanitarian and development work — and the ultimate reason why IM, MEAL, KM, and ICT exist as distinct yet complementary functions.
These four supporting functions serve their purpose when they support programme teams, particularly Heads of Programme (HoPs), Project Managers, and technical coordinators, to design, implement, adapt, and deliver services responsibly and efficiently, in line with organizational and community priorities.
This chapter breaks down how each domain contributes to the goal of effective programme delivery, and how they connect to create a supportive data ecosystem. We begin by placing programme teams at the center -as the drivers of data needs- and then clarify how the supporting functions contribute.
Programme Teams at the Center
Programme teams are the ones planning and delivering assistance. They are directly responsible for identifying participants, registering and verifying eligibility, managing service delivery, and monitoring progress. These core activities generate and rely on data — such as participant records, delivery tracking, eligibility scoring, case management records, or referrals — which must be well-managed to ensure quality implementation.
As such, their needs must drive how information is managed across the entire data lifecycle and programme teams are at the center of the data ecosystem.
They:
Define what data is needed to deliver and manage activities (e.g. registration, service tracking, delivery monitoring, referrals).
Operate the tools and workflows at the front lines of data intake and management, often through registration forms, case management tools, or activity trackers.
Rely on structured and reliable information to make day-to-day decisions — such as how to allocate resources, which individuals are eligible, or whether coverage targets are being met.
Have a direct duty of care toward communities, including the responsibility to manage participant data ethically and securely.
They are not just end users of information systems, but the primary actors that drive data flows — from intake to delivery to follow-up. The design and operation of IM systems must start from their needs and be tailored to support their work, not the other way around.
Information Management
IM is the structured process of turning data into actionable information to support humanitarian decision-making, coordination, learning, and accountability.
While it includes the technical handling of data such as collection, cleaning, storage, and protection (what some organizations also refer to as data management), IM goes further: it ensures that data is transformed into timely, structured, and usable information that supports programme implementation, service delivery planning, participant management, and operational coordination across sectors.
This includes enabling field teams to:
Identify and register eligible participants
Track assistance delivery across locations and projects
Manage referral and follow-up workflows
Coordinate activities between sectors and partners
Maintain up-to-date service records and ensure continuity across the response
Although IM contributes to reporting, adaptation, and accountability, these are secondary benefits that build on a strong foundation of programme data systems.
IM enables teams to work with data in a consistent, secure, and usable way across the programme cycle. It is both a technical and strategic function — one that connects field-level tools and systems to broader programme goals and responsibilities. IM ensures that critical data needed for service delivery, coordination, and follow-up is available when and where it’s needed.
IM teams also play a central role in structuring programme data systems to ensure smooth, reliable flows of information across tools, teams, and timeframes. This includes preparing standardized formats, defining data typologies, and enabling automated transfers between forms and platforms. These foundations ensure that data is interoperable, can be reused across projects, and supports both ongoing delivery and broader coordination or reporting needs.
Monitoring, Evaluation, Accountability and Learning
MEAL focuses on how we use data to assess performance, improve programming, and remain accountable to affected populations. It designs indicators, monitors progress, gathers community feedback, supports adaptive learning, and ensures accountability to affected populations.
Monitoring tracks programme activities and outputs in real time — and increasingly includes emerging outcomes, participant satisfaction, and service quality.
Evaluation assesses whether objectives were achieved, and examines the relevance, efficiency, sustainability, and impact of interventions.
Accountability ensures that communities can give feedback, raise concerns, and meaningfully influence decisions in all stages of the project.
Learning involves analyzing data and experiences to improve current programming and inform future strategies.
MEAL also plays a key role in defining what data should be collected — including learning questions, indicators, and reporting priorities. However, it relies heavily on strong Information Management systems to ensure that this data is collected ethically, stored securely, and accessible for analysis and action.
IM vs MEAL in Practice
Defining data needs
IM supports programme teams, particularly Programme Managers (PMs) and Coordinators, in translating their service delivery requirements (e.g., registration, eligibility screening, case management, activity tracking) into structured and feasible data systems. IM ensures that these needs are technically possible, standardized, and aligned across projects.
MEAL defines the overall data framework for learning, accountability, and results measurement including indicators, reporting requirements, and learning questions, and ensures that programme-level data needs feed into these broader structures.
Supporting programme teams to define data needs
IM helps PMs and Coordinators articulate their operational data needs in concrete formats, tools, and systems — ensuring technical feasibility and alignment with existing platforms.
MEAL helps ensure those data needs are also aligned with quality assurance, accountability, and learning objectives.
Designing methodology
IM does not define methodology, but provides input on digital feasibility (e.g., which tools support sampling, longitudinal tracking, etc.).
MEAL leads on methodology design — including defining the purpose, audience, sample size, frequency, and data analysis methods.
Selecting data collection tools and platforms
IM selects and manages tools (e.g., Kobo, ODK, CommCare) based on context, connectivity, and interoperability.
MEAL defines the functional needs of the tools (e.g., what kind of survey or checklist is needed), but relies on IM to select and configure platforms.
Building mobile/data collection forms
IM builds, tests, and deploys digital forms, ensuring data is collected in standard formats, securely and efficiently.
MEAL defines the form content (indicators, questions, skip logic) and validates the form for quality and relevance.
Training teams on data tools
IM trains staff on how to use digital tools and follow data workflows securely and consistently.
MEAL trains on the purpose of data collection, ethical standards, and how to ask questions appropriately.
Ensuring data quality
IM performs data cleaning, validation, and documentation to ensure data is reliable, complete, and ready for use.
MEAL defines quality standards and oversees the interpretation of data for learning, decision support, and accountability.
Storing and protecting data
IM owns the setup and governance of secure data storage: encryption, access management, and server/cloud infrastructure.
MEAL helps ensure the protection standards are applied during collection and reporting, and verifies informed consent processes.
Analyzing and visualizing data
IM supports by preparing datasets, coding variables, and building dashboards, maps, or other data products.
MEAL conducts the actual analysis and draws conclusions to inform programme improvements or learning.
Sharing information
IM prepares formats and outputs (e.g., dashboards, cleaned datasets, maps) that can be shared with internal and external stakeholders.
MEAL uses these outputs to prepare reports and disseminate findings to donors, coordination groups, and communities.
Enabling learning and adaptation
IM ensures data is archived, well-structured, and accessible for trend analysis or multi-year comparisons.
MEAL facilitates learning exercises, after-action reviews, and reflection processes to drive adaptation and change.
Knowledge Management
Knowledge Management (KM) focuses on preserving, organizing, and sharing institutional knowledge across projects, teams, and time. While MEAL captures learning from specific programmes — such as what worked, what didn’t, and why — KM ensures that this learning is retained, structured, and made accessible for future use.
KM transforms individual or project-based insights into organizational knowledge. It helps prevent knowledge loss during staff turnover, supports cross-country learning, and strengthens strategic planning by ensuring that past experiences inform future decisions.
Key KM activities include:
Organizing and archiving lessons learned, evaluations, and good practices
Creating and curating knowledge products such as briefs, playbooks, and how-to guides
Facilitating knowledge exchange across teams, sectors, and countries
Managing repositories and platforms to store and retrieve institutional knowledge
KM complements MEAL by ensuring that learning is not just captured, but also preserved, contextualized, and used across the organization — turning short-term data into long-term institutional memory.
MEAL generates learnings that feed KM systems; KM preserve and structure those learnings so they’re accessible later.
Information and Communication Technology
ICT provides the infrastructure and digital tools that enable data to be collected, transmitted, stored, and used across humanitarian and development programmes. It supports both field operations and headquarters through technology that ensures connectivity, system functionality, and data security.
While ICT does not define what data is collected or how it is used, it plays a critical enabling role in the implementation of IM, MEAL, and KM systems.
Key ICT responsibilities include:
Setting up and maintaining digital tools and platforms (e.g., mobile devices, servers, cloud systems, shared drives)
Ensuring network and power connectivity, especially in remote or emergency settings
Supporting tthe technical configuration and deployment of data collection platforms (e.g., setting up servers, installing applications, configuring user roles and permissions, enabling integrations for tools like KoboToolbox, ODK, and CommCare)
Maintaining information security and access control systems
Managing user support, hardware, licenses, and troubleshooting
ICT teams collaborate closely with IM, MEAL, and KM teams to ensure that their tools are functional, secure, and appropriately configured for operational needs. Without reliable ICT systems, even the best-designed IM or MEAL frameworks cannot function effectively.
ICT is not about the content of data or information — it’s about making the systems work that allow data to flow and be used securely.
1.1.2 How They Work Together
Although each of these functions has a distinct role, they are deeply interconnected and rely on each other to ensure that data leads to effective, ethical, and accountable action.
IM supports programme teams by ensuring that programme data is structured, complete, accessible, and responsibly managed. This includes managing the systems, formats, workflows, and protections that allow data to move smoothly from registration and eligibility screening to service tracking and beyond. IM ensures that programme teams can work with data in a timely, coherent, and secure manner throughout the project cycle. Together with MEAL, IM helps improve the overall quality and reliability of programme data, ensuring that what is collected in the field can be confidently used for coordination, adaptation, and learning.
MEAL defines specific data requirements for monitoring, evaluation, learning, and accountability such as indicators or feedback mechanisms. IM supports these needs by implementing the technical systems, building tools, and ensuring the quality of underlying data. However, MEAL does not define programme data systems overall. That responsibility, especially for data used in implementation, lies with IM and the programme teams themselves..
KM builds on the outputs of both IM and MEAL, transforming lessons learned, evaluations, and good practices into institutional knowledge. KM ensures that key insights are captured, accessible, and used across projects and time.
ICT underpins all three by ensuring the digital infrastructure — from data collection apps to secure servers and cloud-based knowledge repositories — is functional, secure, and accessible even in constrained settings.
When these functions work together with a shared understanding of roles and priorities, they form a strong, responsible, and efficient data ecosystem — one that supports effective programme implementation first and foremost.
To better understand how these domains interact, refer to the diagram below (Adapted from CartONG, 2020)
Each function has its own responsibilities but also overlaps significantly with the others, requiring coordination and shared understanding.
Mapping IM, MEAL, KM, and ICT onto the DIKW Pyramid
Another useful lens for understanding how these domains relate is the DIKW Pyramid — a model that illustrates how raw Data becomes Information, which builds into Knowledge and informs Wisdom (strategic action).
Each function aligns with different levels of this pyramid:
IM supports the Data → Information transition — ensuring data is collected, structured, and made usable.
MEAL works with Information and Knowledge — using data to evaluate, learn, and improve programme performance.
KM operates at the Knowledge → Wisdom level — organizing and synthesizing insights to inform long-term learning and decision-making.
ICT enables every level by providing the tools and infrastructure that allow data to be securely collected, stored, analyzed, and shared.
Wisdom
Strategic insight and sound decision-making derived from synthesized learning.
Knowledge Management (KM)
Enables data storage and knowledge sharing platforms
Knowledge
Learning and understanding gained from patterns in information across projects and time.
MEAL, Knowledge Management (KM)
Supports learning systems and documentation tools
Information
Structured, contextualized data used to understand, monitor, and improve programming.
Information Management (IM), MEAL
Facilitates data processing, dashboards, reports
Data
Raw facts and figures collected through surveys, systems, or monitoring tools.
Information Management (IM)
Provides tools for data collection and storage
1.1.4 Why This Matters
Throughout this handbook, you will often see links between IM, MEAL, KM, and ICT. Understanding their roles, especially, understanding how IM connects to everything else, will help you work more effectively with program data, contribute to stronger programming, and avoid common pitfalls like data fragmentation or ethical risks.
REFERENCES & FURTHER READINGS:
Last updated