COREDemo videos

CORE / Data Quality & Traceability

Better data starts
where data is created.

CORE improves data quality upstream — through structured daily programme operations — reducing the cleaning, reconciliation and reconstruction required downstream.

Completeness and consistency checked at the point of data collection.

Reassurance for MEAL and reporting teams

Cleaner operational data.
Less reconstruction later.

CORE helps reduce the manual cleaning and reconciliation often required downstream. Unique identifiers, duplicate detection, controlled reference data and traceable operational history all contribute to more trustworthy datasets.

Deduplication supportTraceabilityValidationAuditability

This is where the reporting benefits become tangible: data quality improves because the operational foundation is stronger.

01 / Quality at source

Prevent problems before reporting.

Standardised formats, entry controls, required fields, controlled reference data and deduplication improve data quality at source. Teams prevent inconsistencies during daily work instead of repeatedly correcting them at reporting time.

Illustrated common data model used by CORE.
Consistency across the model. Shared entities and references reduce ambiguity and make aggregation more dependable.
01

Unique CORE ID

Give every record a stable identifier while retaining external IDs where needed.

02

Duplicate detection

Reduce multiple records for the same participant and strengthen unique participant counting.

03

Validation

Use required fields, controlled options and form logic to improve consistency.

04

Reference data

Reuse locations, PCodes, sectors and other shared values instead of recreating them in every dataset.

Review potential duplicates through side-by-side record comparison, an overall matching score and field-level similarities.
Review potential duplicates through side-by-side record comparison, an overall matching score and field-level similarities.

02 / Context stays attached

Trace the operational history.

Authorised users can follow how information connects across participants, Activity Implementations, Forms & Assessments, referrals, locations, Projects and Activities. The relationships needed for analysis are created during delivery rather than reconstructed afterwards.

Illustrated participant interface with linked records and tabs.
Traceability in context. Records, linked entities, services, forms and referrals remain connected rather than scattered across files.
ParticipantActivity ImplementationForms & AssessmentsReferralLocationProject & Activity

An audit trail that lasts beyond the operation.

CORE logs all actions with timestamps. Referenced participants, Activity Implementations, distributed items and supporting records let authorised colleagues reconstruct what happened years later, subject to retention rules. This supports audit work as well as Finance and Supply reviews.

03 / Count what actually happened

More reliable participant and output figures.

CORE can distinguish participants who were identified, selected and actually reached. Combined with unique participant identification and Activity Implementation history, this supports more meaningful counting and reduces double counting.

Identified
→
Selected
→
Reached
→
Completed / Handover

CORE includes a default counting algorithm based on commonly used population-counting and reporting rules. Teams can also configure custom counting rules to match specific institutional indicators or donor requirements, keeping calculations consistent with the intended measure.

Actual CORE service delivery screenshot showing participants inside an activity implementation.
Participant selection and delivery records provide the operational basis for counting identified, selected and reached populations.

AI readiness starts with data quality

AI cannot compensate for missing context.

Stable identifiers, deduplicated records, consistent reference data and traceable relationships make operational information more reliable for people today — and more usable by automated and AI-enabled systems tomorrow.

For IM & MEAL

Less data wrangling. More quality assurance and analysis.

CORE is primarily used to support programme delivery, but reporting is designed into the model. Better structured data at source means less manual cleaning, mapping, deduplication and consolidation — and more opportunities to automate downstream work.

04 / The flow

Frontline value first. Downstream benefits follow.

Illustration linking frontline data collection, structured data, reporting and AI readiness.
Quality is created upstream. Better reporting quality begins with better data creation conditions for operational teams.
Daily programme work
→
Quality data at source
→
Aggregation & automation
→
Reliable analysis & reporting