What type of IM capacities exist — and what are they good for?
Understanding the range of technical skillsets that underpin effective Information Management (IM) is critical for planning, recruiting, and resourcing IM capacity at country level and the organisation at large. This chapter broadly outlines the IM competency requirements, describing what each competency encompasses and why it is important, giving practical examples.
This competency-based approach allows Heads of Programmes to:
Identify gaps in their current IM team’s capacity
Recruit or train staff with the right mix of skills
Design organisational structures that align with the IM lifecycle
It should be noted that competencies outlined in this chapter can but do not necessarily need to be centralized in one position. Depending on the operational context and available resources, they might be more or less covered and spread across several people in a team. Chapter 3.2 explores the distribution of roles and responsibilities in more detail, building on different scenarios.
The ability to identify information needs and define data requirements to guide ethical and purposeful data collection and use.
What it encompasses:
Identifying and mapping available data (internally and externally).
Collaborating with project managers and field teams to identify the information required to plan, deliver, and monitor programme activities effectively (e.g., eligibility criteria, service tracking, follow-up workflows).
Coordinating with senior management, technical leads, project managers, MEAL and partners to identify information need and agree on priorities.
Engaging technical coordinators and CC leads to translate SOPs and technical guidance into concrete data needs (e.g., WASH facility tracking, education attendance, ICLA case status updates).
Distinguishing between core and optional data points, helping programme teams prioritize essential information for delivery over “nice-to-have” data that may burden field staff or participants.
Translating programme goals into questions that data can answer.
Supporting risk-benefit or data protection impact assessments (DPIAs).
Why it’s important:
Clear definition of data needs ensures that data collection is purposeful, relevant, and not excessive.
Example:
The IM focal point facilitates a workshop with programme and MEAL teams to identify data needs for a new multisectoral project and maps those needs against available data.
Design and Plan
The ability to design data collection plans and tools that are context-specific and aligned with data responsibility principles and international or organisation-specific data tandards.
Designing surveys and assessment tools (e.g., XLSForm for Kobo and SurveCTO)
Reviewing and validating draft data collection tools proposed by PMs to ensure technical feasibility and adherence to data standards
Supporting programme teams to design intake forms, service tracking sheets, and referral tools aligned with delivery workflows
Setting up access roles and collection timelines
Ensuring tools reflect language, literacy, connectivity, and operational context constraints
Coordinating across sectors and teams to harmonise tools when multiple actors are involved
Why it's important:
Strong planning ensures ethical data practices are built in from the start, with tools tailored to operational needs and user capacities. Proper design also assures data quality and minimizes data cleaning and transformation later on.
Example:
An IM Officer supports the Shelter team in designing a digital intake form for participants registering for shelter kits. They adapt the form to include eligibility scoring logic (based on household size, shelter condition, and vulnerability factors), ensure offline compatibility for remote areas, and set up role-based access so that field staff, programme managers, and partners can each access the relevant layers of data securely and efficiently.
Collect Data
The ability to implement secure, ethical, and high-quality data collection using appropriate digital or analogue tools.
What it encompasses:
Training and supporting field teams
Coordinating data collection efforts with local authorities
Deploying and troubleshooting data tools
Monitoring data quality during collection
Why it's important:
Poorly implemented data collection wastes resources and risks generating inaccurate or harmful information. Respectful and inclusive data collection efforts build trust with communities and produce better-quality data.
Example:
An IM Officer trains enumerators on the data collection tools, informed consent and confidentiality and uses metadata from a post-distribution monitoring survey to identify anomalies while data collection is ongoing, prompting field teams to adjust data collection practices.
Clean and Validate
The ability to review and correct data to improve accuracy, completeness, and reliability before analysis and dissemination.
What it encompasses:
Identifying and correcting errors (e.g. duplicates, outliers, missing fields)
Using tools like Excel or Python scripts for automated cleaning
Documenting validation and cleaning processes and assumptions
Applying anonymization measures when needed
Master advanced anonymisation methods, especially when preparing data for sharing or long-term archiving
Why it's important:
Raw data is rarely clean. Without validation and cleaning, programme delivery may be disrupted by incomplete records, duplication, or misleading information. Clean and reliable data is essential for ensuring that services reach the right people, resources are accurately tracked, and programme teams can implement activities efficiently and accountably.
Example:
An IM Officer cleans data from a protection assessment, removing incomplete responses, outliers and duplicates and tracks changes in a cleaning log.
Store and Control Access
The ability to organise, store, and secure collected data in a structured, consistent, and retrievable manner.
What it encompasses:
Setting up and maintaining data storage systems (e.g. Kobo, SharePoint, MongoDB)
Why it's important:
Well-managed data reduces risk, improves usability, and enables interoperability across teams and systems. It also safeguards institutional memory and enhances accountability.
Example:
An IM Officer establishes a database to centralize information from project participants, facilitating effectiveness through deduplication and adherence to data protection policies by configuring user permissions.
Analyze and Use Data
The ability to structure, interpret, and communicate programme data to improve service delivery, adapt implementation, and ensure inclusive, timely, and accountable response.
What it encompasses:
Operational trend analysis: Identifying shifts in service uptake, access barriers, or delivery bottlenecks (e.g. fluctuating attendance at education sites, variations in assistance coverage by location).
Programme targeting and prioritization: Interpreting registration and service data to refine participant lists, triage caseloads, or adjust delivery schedules.
Use of geospatial analysis: Mapping service points and overlaying with risk or vulnerability data to optimize field operations (e.g. shelter coverage vs. flood zones).
Designing programme-focused information products: Dashboards, delivery tracking summaries, or activity-level completion reports tailored for field and coordination teams.
Integrating feedback into delivery logic: Aligning real-time community feedback with operational data to identify issues and adjust workflows.
Supporting coordinated delivery: Aggregating internal data to support inter-agency 3W/5W analysis or collective coverage mapping.
Why it's important:
Effective programme delivery relies on the ability to interpret data in real time. Whether it’s identifying delays in service rollout, comparing targeting coverage across locations, or adjusting resource allocations mid-cycle, data use must directly inform implementation decisions. Analyzing and using programme data ensures that services are delivered to the right people, in the right way, and at the right time — improving both efficiency and accountability on the ground.
Example:
A Livelihoods Project Manager, supported by an IM Officer, reviews service data from three area offices to assess the dropout rates from vocational training programmes. By linking attendance logs with registration and exit survey data, the team identifies that most dropouts occurred in sessions held after 2:00 PM in rural areas with no public transport. Based on this, the schedule is revised, and transport stipends are introduced. The IM Officer visualizes these insights in a weekly implementation dashboard that tracks attendance in real time, helping field teams monitor whether the changes are having the desired effect.
Share Data Responsibly
The ability to prepare and disseminate information ethically and effectively for internal and external audiences, adhering to data responsibility principles.
Preparing and sharing information products (e.g. SitReps, maps, dashboards)
Applying design principles and tailoring visuals to different audiences
Why it's important:
Timely and accurate dissemination of information enhances decision-making, facilitates coordination, avoids duplication, and improves accountability to affected populations and donors. However, data sharing must not expose individuals or communities to harm.
Example:
An international NGO is working with a local partner to deliver shelter assistance in flood-affected areas. The iNGO’s IM Officer supports the local partner in collecting and digitizing beneficiary registration data using a shared Kobo platform. To ensure responsible data sharing, the IM Officer helps co-develop a Data Sharing Agreement (DSA) that outlines:
What data will be shared (e.g. aggregated beneficiary demographics and shelter needs)
The format and frequency of sharing
Security measures for file exchange (e.g. password-protected folders, no personal data)
The specific purpose of use by each party
The local partner shares anonymized service data weekly, which the iNGO integrates into a regional dashboard to monitor coverage and gaps. This collaboration enables real-time coordination, avoids duplication in targeting, and strengthens the partner’s role in response leadership — all while maintaining ethical standards of data use.
Archive and Delete
The ability to archive valuable data securely or delete it in line with data retention policies.
What it encompasses:
Classifying data by retention duration and purpose
Archiving datasets with metadata and access conditions
Why it's important:
Archiving data ensures accountability and supports audits and quality controls. Respecting retention limits assures the protection risks are minimized.
Example:
An IM Officer keeps a data management repository for each project, providing an overview of archived, facilitating audits and assuring timely deletion that aligns with international or donor-specific retention periods.
In addition to these core competencies, cross-cutting competencies should be for recruitment and the composition of teams. Amongst others, those might include knowledge of data protection protocols and practices, experience with stakeholder engagement, and capacity strengthening skills. As IM needs become more complex, organizations may also require specialized technical capacities, for instance with regards to GIS, remote sensing, and advanced statistical analysis. These competencies may be brought in on the basis of needs through dedicated roles, surge support, or targeted training, and should be planned for accordingly.
3.1.2 Assessing and Mapping Competencies
In line with Chapter 2.3 of this Handbook, understanding existing IM competencies is a good starting for identifying where additional support, mentoring, or recruitment may be needed. It also helps organisations plan capacity development more strategically. The IFRC’s IM Technical Competency Framework provides a structured overview of IM competencies across various levels of proficiency, which is a useful fundament for assessing individual skills across core IM domains.
It might also be useful to build an office- or organization-specific database of IM competencies, to organize skill transfer through exchange formats, like Communities of Practice, and build a pool of experts that can respond to surge requests. CARE has build a simply self-assessment tool for MEAL that serves a similar purpose.
3.1.3 Developing Competencies
The IFRC has conducted a comprehensive mapping of available training and learning materials for IM, which is a useful databased for anyone who seeks to develop or strengthen specific IM competencies. The database is available here.
CartONG's Learning Corner also encompasses learning materials for specific IM competencies, such as data management, analysis and visualisation, as well as learning materials for specific tools, such as Excel and SurveyCTO. The Learning Corner is available here.
