| name | data-management |
| description | Use when a proposal covers data collection, quality, governance, MIS, surveys, protection, retention, or migration. Route outcome measurement to monitoring-and-evaluation; this skill governs the data lifecycle and controls. |
| metadata | {"portable":true,"compatible_with":["claude-code","codex"]} |
Data Management
Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, +256 784 464178.
Use When
- Use this skill when the assignment explicitly needs data-management, governance, privacy, or information-system content.
- Load it when another proposal section needs this domain expertise.
Do Not Use When
- The task only needs formatting or proposer-profile selection.
- Another supporting skill is a closer fit for the assignment.
Required Inputs
| Artefact | Source | Required? | If absent |
|---|
| Data purpose, sources, subjects, flows, and outputs | ToR and system owners | required | Stop detailed design and issue a data-discovery request. |
| Law, consent, retention, access, and quality evidence | Authoritative rules and client controls | conditional | Mark compliance and quality controls unassessed. |
Workflow
Stop or block the workflow when a required input, permission, or acceptance basis is missing. Recover by revising the scope, obtaining evidence, or returning the narrowest qualified draft before proceeding.
- Identify where data collection, governance, quality, or protection matters in the assignment.
- Read the local references only where they materially improve the output.
- Convert the guidance into proposal-ready controls, activities, and ownership logic.
- Integrate the result into the target section and check for consistency.
Quality Standards
- Translate theory into practical proposal language, outputs, and safeguards.
- Keep the approach specific to the client context and implementation reality.
- Preserve compatibility with existing repository workflows and file paths.
Anti-Patterns
- Naming governance or privacy frameworks without operational controls. Fix: name sources, owners, flows, access, and outputs.
- Collecting data without a decision use. Fix: map every dataset to a stated purpose.
- Promising compliance from memory. Fix: verify the jurisdiction and client policy.
- Treating missing values as zero. Fix: define validation, exception, and remediation rules.
- Migrating before reconciliation. Fix: profile, map, test, reconcile, and approve before cutover.
Outputs
| Artefact | Consumer | Acceptance condition |
|---|
| Data management plan | Evaluator, data owner, delivery team | Covers flows, ownership, quality, protection, retention, access, migration, and acceptance. |
Evidence Produced
| Evidence | Consumer | Acceptance condition |
|---|
| Data inventory and quality-control matrix | Traceable register | Every material source has owner, purpose, checks, exceptions, and disposition. |
Capability and Permission Boundaries
Read and search are required; any edit or external action remains within the explicit authority and permission boundary stated below.
Default to read-only inspection. Editing requires authority; personal or client data access must be least-privilege. Do not move, delete, publish, or certify data without explicit approval.
Degraded Mode
Without datasets, system access, or verified law, return a data-discovery and control plan with unassessed items. Never report unavailable quality or compliance checks as passed.
Decision Rules
| Condition | Action | Risk avoided |
|---|
| Existing data may answer the question | Profile and assess fitness first | Unnecessary collection |
| Decision needs new primary data | Design proportionate collection and consent | Data without purpose |
| Migration changes records | Reconcile samples and require owner sign-off | Silent loss or duplication |
Worked Example
For a beneficiary MIS, inventory enrolment, service, and outcome data; assign stewards; validate identifiers and dates; and reconcile migrated totals before acceptance.
SaaS Data Implementation Plan
For SaaS lifecycle-communications scope and for any SaaS implementation that depends on segmentation, behavioural triggers, identity resolution, and operating-rule governance, scope and price a dedicated Data Implementation Plan workstream:
- Identify schema: customer, account, user, role, segment, lifecycle stage, lifecycle program flags.
- Event schema: signup, activation, in-product actions, billing events, support events, customer-success events.
- Custom fields: vertical-specific fields (insurance: policy count; banking: customer count, transaction volume; healthcare: facility, provider category).
- Identity resolution: linking the same user across product, marketing automation, CRM, support.
- Consent and preference management: lawful basis, opt-in records, channel preferences, frequency caps.
- Segmentation: standard segments at launch (by tier, lifecycle stage, health, industry).
- Data hygiene: bounce handling, suppression, decay, re-verification.
This is engineering-grade work — price it as such, not inside a bundled "email setup" line.
References
Assignments involving system implementations, M&E frameworks, surveys, or institutional assessments all require a data management component. Proposals that demonstrate a structured approach to data collection, storage, quality, governance, and protection score higher — particularly with donors who have been burned by projects that produced unusable data.
When to Read This Skill
- The assignment involves designing or implementing a management information system (MIS)
- The ToR mentions "data collection", "data management", "data quality", "data governance", or "data protection"
- The methodology includes surveys, assessments, or monitoring activities that generate data
- The assignment must comply with data protection legislation
- When the ToR asks for a standalone data management plan
Data Collection
Method Selection
| Method | Best For | Tools |
|---|
| Mobile data collection | Field surveys, facility assessments, beneficiary registration | ODK, KoBoToolbox, SurveyCTO |
| Paper-based forms | Low-connectivity environments, sensitive data, legal requirements | Standardised forms with clear coding |
| System-generated data | Routine monitoring, transaction records, service delivery | MIS, ERP, HMIS, EMIS |
| Administrative records | Baseline data, historical trends, coverage statistics | Government databases, reports |
| Key informant interviews | Qualitative data, expert perspectives | Interview guides, recording equipment |
Data Collection Standards
- Standardised tools: all data collection instruments piloted before deployment
- Enumerator training: minimum two days of training including field practice
- Quality checks: real-time validation in mobile tools, supervisor spot-checks for paper-based
- Consent: informed consent obtained and documented for all respondent data
- Unique identifiers: every record has a unique ID; no reliance on names alone
Data Quality Framework
The Five Dimensions of Data Quality
| Dimension | Definition | How to Verify |
|---|
| Validity | Data measures what it is intended to measure | Review instrument design, pilot testing |
| Reliability | Consistent results across enumerators and time | Inter-rater reliability tests, re-interviews |
| Completeness | All required fields populated, no missing records | Automated completeness checks, dashboard monitoring |
| Timeliness | Data available when needed for decision-making | Submission deadlines, real-time dashboards |
| Integrity | Data protected from unauthorised alteration | Access controls, audit trails, version control |
Data Quality Assurance Activities
- Pre-collection: instrument design review, pilot testing, enumerator certification
- During collection: daily data quality reports, supervisor verification, back-checks on 10% of submissions
- Post-collection: cleaning protocols (outlier detection, consistency checks, duplicate removal), validation against secondary sources
Data Governance
For system implementation and institutional assignments, propose a data governance structure:
- Data owner: the client department or unit responsible for each data set
- Data steward: the person responsible for data quality within each unit
- Access control: role-based access — who can view, edit, approve, and export
- Data dictionary: standardised definitions for all fields and indicators
- Retention and archival: how long data is kept, in what format, and when it is archived or destroyed
Data Protection Compliance
East African Data Protection Legislation
| Country | Legislation | Key Requirements |
|---|
| Uganda | Data Protection and Privacy Act, 2019 | Registration with PDPO, consent for processing, data minimisation, cross-border transfer restrictions |
| Kenya | Data Protection Act, 2019 | Registration with ODPC, consent, data subject rights, breach notification within 72 hours |
| Tanzania | Personal Data Protection Act (under development) | Follow emerging requirements; apply Kenya/Uganda standards as minimum |
| Rwanda | Law Relating to the Protection of Personal Data and Privacy, 2021 | Consent, purpose limitation, data subject rights, cross-border transfer restrictions |
Proposal Commitments
When processing personal data, the proposal should commit to:
- Data collection limited to what is necessary for the assignment (data minimisation)
- Informed consent obtained from all data subjects
- Data stored securely (encrypted at rest and in transit)
- Access restricted to authorised project personnel
- Data not transferred outside the country without appropriate safeguards
- Data retained only for the duration necessary, then securely destroyed
- Compliance with the applicable national data protection law
Generating a Standalone Section
When the ToR asks for a dedicated data management plan, generate a document covering:
- Data management approach and principles
- Data collection methods and tools
- Data quality assurance framework
- Data governance structure (roles, access controls, data dictionary)
- Data storage and security measures
- Data protection compliance (applicable legislation, consent, retention, destruction)
- Data analysis and reporting plan
- Data handover and sustainability
Follow east-african-english standards throughout.
