1.4 Mapping Data Flows in an Organization

Managing information responsibly and efficiently starts with understanding how data moves through an organization. Mapping data flows is a simple but powerful exercise that helps teams see where data comes from, how it is processed, where it is stored, who accesses it, and how it is used.

Without a clear understanding of data flows, organizations risk data fragmentation, duplication, delays, loss of important information, or breaches of confidentiality.

1.4.1 What is a Data Flow Map?

A data flow map is a visual or structured way of representing the journey of information from the moment it is collected to its final use or storage.

It shows:

• Who collects the data (e.g., field staff, enumerators, partners).

• What type of data is collected (e.g., participant data, service tracking, feedback).

• How the data is transmitted (e.g., mobile apps, paper forms, emails).

• Where the data is stored (e.g., databases, spreadsheets, cloud systems).

• Who accesses and uses the data (e.g., program managers, MEAL teams, donors).

• How and when the data is shared (e.g., reports, dashboards, cluster submissions).

Mapping this flow allows teams to identify gaps, bottlenecks, risks, and opportunities for better information management.

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1.4.2 Why Mapping Data Flows Matters

• Improves Efficiency: Streamlines how data supports service delivery — ensuring that participant registration, service tracking, and referrals are not duplicated or delayed due to fragmented data processes.

• Enhances Data Quality: Helps ensure that data remains accurate, complete, and up-to-date as it moves through the system.

• Strengthens Accountability: Clarifies who is responsible for managing, protecting, and updating data at each stage.

• Supports Data Protection: Identifies where sensitive data is handled and where privacy or security measures are needed.

• Facilitates Better Decision-Making: Ensures that clean, reliable data reaches decision-makers when they need it.

• Better Service Delivery Organisation: Enables smoother coordination between teams and services by clarifying data dependencies, timelines, and responsibilities — reducing missed steps or overlaps in assistance.

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1.4.3 Key Elements to Consider When Mapping Data Flows

When you begin mapping your organization’s data flows, consider:

• Sources of Data: Where and how is data generated? (Surveys, registrations, case management, needs assessments.)

• Data Transmission: How does the data move from collection points to storage systems? (Manual entry, automated sync, email attachments.)

• Storage and Processing: Where is the data kept? How is it cleaned, validated, and organized? (Shared drives, database servers, CRM systems.)

• Access and Use: Who needs access to the data? What level of access is appropriate? (Data sharing protocols, role-based access control.)

• Archiving or Deletion: How long is data retained? When and how is it archived or securely deleted?

Two Simple Example of a Programme Data Flow

Example 1: This example shows how data is used not just for reporting but to guide operational decisions and enable service delivery — such as screening households, identifying priority needs, triggering assistance, and adjusting programming in real time.

1. Field Teams conduct household assessments(e.g., KoboCollect or Survey123)
   Data collected includes household demographics, vulnerabilities, and priority needs
                         ↓
2. Data is synced to a central platform (e.g., ONA, CommCare, PostgreSQL)
   Supervisors or IM staff check incoming data and flag anomalies
                         ↓
3. IM or Programme Staff clean and validate the dataset 
   (e.g., scoring vulnerability, removing duplicates, geotagging entries)
                         ↓
4. Programme Teams use the data to design or adjust the response 
   (e.g., define targeting criteria, prioritize areas, select modalities)
                         ↓
5. Targeted assistance is delivered to eligible participants 
   (e.g., inclusion in cash distribution lists or referral systems)
                         ↓
6. Post-implementation feedback is collected and reviewed 
   (e.g., Post-Distribution Monitoring or Satisfaction Surveys)

This example highlights how IM supports frontline programming — not just for compliance or reporting, but as an enabler of timely, evidence-based assistance.

Example 2: This example illustrates a basic data flow within a humanitarian programme — from initial collection to final archiving. Each step highlights who is involved, what kind of data is handled, and where it moves, helping to identify potential risks and responsibilities.

1. Field Staff collect participant data using mobile tools (e.g., KoboCollect)
                     ↓
2. Data is synced to a centralized database (e.g., PostgreSQL server)
                     ↓
3. IM Officer conducts data cleaning and quality checks
                     ↓
4. Data Analysts or Programme IM staff generate visualizations (e.g., in Power BI or Tableau)
                     ↓
5. Information is used in reports for Programme Management, Donors, and Clusters
                     ↓
6. Programme data is archived securely according to retention policies

A Data Flow Map visualizes this process in more detail — showing who collects and manages data, what type of data flows through the system, how it is processed, and where it is stored and shared. (See 5.1 Data Workflows & Typology for guidance on mapping data flows.)

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REFERENCES & FURTHER READINGS:

• IASC (2023). Operational Guidance on Data Responsibility in Humanitarian Action.

• IFRC (2023). Data Playbook Toolkit - Module 5: Making data useful, useable and shareable.

• IFRC (2023). Data Playbook Toolkit - Module 9: Making decisions with data.