To turn reporting requirements into evidence you can collect, start with the report your reader needs. Extract each required claim, measure, breakdown, narrative, and method from the funder’s documents and the authoritative standards it names. Connect each requirement to an approved organization measure, then identify the quantitative, qualitative, documentary, and longitudinal evidence that can support it. Record gaps and human decisions. The result is a Reporting-to-Evidence Map that guides collection and helps the team prepare different reports from the same approved evidence. This map is a practical course worksheet, not an external reporting standard.
Your next step in the reporting course
Goal: Connect each report requirement to evidence you can actually obtain.
Start with: Bring the reporting request and your approved metric definitions. Start with a small table: requirement, source, gap and owner.
Carry forward: Leave with an evidence map showing what is available and what is missing. Next, place the missing collection into the moments where the work happens.
This lesson is for nonprofit, foundation, program, MEL, grant, and impact leads who encounter terms such as SDG, IRIS+, GRI, Five Dimensions of Impact, SROI, schema, and data dictionary—but need a practical way to use them without first becoming standards specialists.
What you will produce
- One list of reporting requirements, each linked to its source.
- One approved organization measure or question for each requirement.
- An inventory of usable evidence—numbers, interviews, open text, emails, PDFs, and longitudinal records.
- A visible list of gaps, contradictions, and decisions requiring human review.
How do you turn reporting requirements into evidence?
- Start with the report. Identify the decision, audience, reporting period, required output, and authoritative documents.
- Extract the requirements. Separate required claims, measures, disaggregations, narratives, calculations, and supporting documents.
- Connect each requirement to a measure. Reuse the approved Metric Definition Sheets from the related lesson; never match by label alone.
- Connect each measure to evidence. Identify the source, owner, collection moment, timestamp, consent/access rule, and what is missing.
- Test the output. Confirm that the evidence can produce the required report with citations, visible limitations, and no unsupported claims.
What problem does the shared evidence map solve?
Reporting requirements arrive in different languages and formats, while the evidence lives across different people and systems. A shared evidence map translates both sides — what each audience asks for and which governed evidence can support the answer — and it makes the context explicit: the source requirement, relevant outcome, field definition and approved mapping. A data dictionary can hold this context or link to the associated records. The important distinction is the information and ownership required, not a rule that every team needs a separate document.
The goal is not one universal report. It is one governed evidence base that can produce audience-specific outputs without redefining the evidence each time.
What is a data dictionary in plain language?
A data dictionary is the governed version of your shared evidence language. It records what each item means, how it is measured, where it comes from, who owns it, and which reporting requirements it can support.
You do not need to begin with field IDs or database types. Begin with the confusion the dictionary prevents. For each intended comparison, define what participant, completed, employed and retained at 90 days mean. Local forms and partner documents may use different labels or collect different detail. Map only compatible observations to the shared definition; preserve original wording and report incompatible measures separately. The related lesson’s metric definition worksheet records those meanings. This chapter connects them to reporting requirements and evidence sources.
The technical implementation—stable IDs, allowed values, validation, missing-data rules, access classification, and version history—still matters. It belongs in the supporting guide, How to Build a Data Dictionary for Program and Impact Data, after the team understands the practical job.
Are data dictionaries, schemas, standards, methods, and AI skills the same?
No. They are connected layers with different jobs. Treating them as synonyms makes the system harder to govern and easier to misapply.
What do SDG, IRIS+, GRI, the Five Dimensions, and SROI actually do?
They are not peer metric lists and they are not interchangeable. Each contributes a different kind of reporting or measurement guidance. Use only what applies to the decision and document the source and version.
| Source | Plain-language job | What not to assume |
|---|---|---|
| SDG indicators | Official global indicators and metadata used to follow progress on the 2030 Agenda | Selecting an SDG icon does not create a program measure or prove contribution |
| IRIS+ | Impact-investing guidance, Core Metrics Sets, taxonomy, and qualitative and quantitative metric catalog | A similar metric name is not an exact mapping |
| GRI | A modular sustainability-reporting system with Universal, Sector, and Topic Standards | GRI is not merely a list of impact-program metrics |
| Five Dimensions of Impact | Organizing norms for considering What, Who, How Much, Contribution, and Risk | A framework does not supply every field, instrument, or reporting rule |
| SROI | A social-value methodology involving stakeholders, outcomes, materiality, valuation, transparency, verification, and avoiding overclaiming | SROI is not simply assigning money to an output and calculating a ratio |
| Custom measures | Organization-, service-, community-, or theme-specific evidence external sources do not capture | Custom does not mean ungoverned or less credible |
The official UN SDG metadata repository, IRIS+ overview, GRI Standards, Impact Frontiers norms, and Social Value International guidance describe these purposes in detail. Standards and methods evolve; record the version and revalidate before relying on a mapping or formal claim.
Why is evidence bigger than a spreadsheet?
Many reporting claims require numbers and context together. Useful evidence can be structured, qualitative, documentary, and longitudinal. The shared structure must preserve the source, date, person or entity, program stage, access rule, and citation—not force everything into a numeric column.
Worked example: disability-inclusive workforce reporting
Fictional practice scenario, not customer results. Confirm definitions with the intended reporting audience before applying this example.
Translate each reporting requirement into an approved measure or question, then identify the source and gap. Do not begin by adding every available disability, employment, or SDG indicator to a survey.
Illustrative scenario: A workforce program must report how participants with disabilities enroll, complete training, enter employment, and retain employment at 90 days. The funder also wants evidence about barriers and the program’s response, plus references to selected SDG and IRIS+ measures.
| Reporting requirement | Approved measure or question | Evidence source | Mapping status | Gap or next action |
|---|---|---|---|---|
| Participants with disabilities enrolled | Confirmed enrollment, disaggregated using the approved disability measure | Consent + intake + enrollment record | Candidate | Approve instrument, language, consent, threshold, and access |
| Training completed | Completion under the agreed completion event and denominator | Attendance + assessment + completion record | Organization-approved | Confirm the reported core measure uses a compatible rule; retain distinct local measures separately |
| Entered employment | Employment started within the approved window | Participant record + employer verification email | IRIS+ candidate | Compare population, window, numerator, denominator, and version |
| Retained at 90 days | Unresolved: continuous employment for 90 days, or employment status on day 90? | Point-in-time follow-up establishes status; continuity requires an employment history covering the agreed interval | Needs clarification | Agree the starting event, permitted gaps and evidence before collecting; keep unknown status separate |
| Barriers participants experienced | “What, if anything, made participation or staying in work difficult?” | Open text + interview + case note | Custom qualitative | Preserve exact words; human-review themes |
| How the program responded | Barrier → action → owner → date → result | Case action, meeting note, referral, partner email | Custom narrative | Link each response to evidence and timestamp |
If the reporting purpose requires comparable disability disaggregation, use a suitable approved instrument rather than asking staff or AI to infer disability from narrative evidence. The Washington Group Short Set on Functioning was developed for censuses and surveys to produce comparable functioning data; WHO’s WHODAS 2.0 is a standardized assessment of functioning and disability. Neither is a universal substitute for deciding the reporting purpose, population, consent, administration mode, language, safeguarding, and local applicability.
Build a requirement-to-evidence matching table
Give each requirement a stable identifier. Keep the exact request and its page, clause or email date beside the approved interpretation. The table below uses a fictional training example; it does not add results or disability breakdowns that the available records cannot support.
| Requirement | Collect or request | Check on arrival | Ready for reporting when |
|---|---|---|---|
| Employment status 90 days after exit | Contact ID, exit date, reference date, status and source; ask about the specified date rather than today | Correct follow-up window; duplicate contact; unknown versus not employed; conflicting confirmations | Owner resolves conflicts and the report shows known-status coverage alongside the result |
| Reasons participation was difficult | A neutral optional question or permissioned interview; keep the respondent’s words and date | Link to the right record; flag missing context; review proposed themes | Each theme can be traced to a source and the report explains how participants were selected |
| Quarterly partner expenditure | Partner ID, reporting period, currency, agreed expenditure categories and supporting financial document | Period and currency match; totals reconcile; required file is present; flag discrepancies for the finance reviewer | The reviewer accepts the reconciliation or records an unresolved difference. A file upload alone is not an audit |
| Actions after a social audit | Audit document, finding reference, action owner, due date and follow-up evidence | Link each action to its finding; identify overdue or unsupported closure claims | Evidence supports the stated action status and authorized staff review the conclusion |
For the training exercise, 36 of 60 people with known status were employed, from 80 starters. Report 60% among known responses with 75% status coverage; 45% of all starters were confirmed employed. These records do not establish uninterrupted employment or permit an invented disability breakdown. Write those gaps into the plan before designing another question.
Test the handoff before sending a form
- Trace one request. Can a colleague find its original clause and the agreed interpretation?
- Run one complete submission. Can the data and supporting file produce the intended report row without retyping?
- Run one incomplete submission. Does a missing response stay unknown instead of becoming zero or failure?
- Run one conflicting submission. Does the discrepancy reach an owner with both sources intact?
- Check access. Can the intended reviewer see the necessary evidence without exposing unrelated personal information?
If a requirement fails these tests, change the collection design or clarify the requirement. Do not conceal the gap in a polished report.
How should AI help with standards and long documents?
AI can reduce the first-pass reading burden by extracting candidate requirements, comparing definitions, inventorying evidence, and flagging gaps. It cannot decide which standard applies, establish compliance, validate causality, or approve a sensitive measure.
- Extract requirements with page or source citations
- Compare candidate definitions and mappings
- Find relevant evidence across PDFs, emails, interviews, and tables
- Flag missing, contradictory, or outdated information
- Draft audience-specific outputs from approved evidence
- Which source and version are authoritative
- Whether the framework applies
- Whether a mapping is valid
- What evidence supports the claim
- How privacy, consent, and access are governed
- What the organization will report and sign
Prompt: build a draft Reporting-to-Evidence Map
Where does Sopact Sense help?
A team can build this map manually in a spreadsheet and shared folder. Friction grows when each funder has different documents, evidence is spread across staff and partner systems, the same requirement recurs under different wording, and a report must be regenerated after new evidence arrives.
A configured Sopact Sense workflow can collect partner submissions and uploaded documents against the relevant contact, organization and reporting period. Agreed imports can bring in additional sources. Set the definitions and review rules so AI can prepare candidate requirements and identify possible gaps as evidence arrives. Test citations, permissions and exception handling with sample records before using the workflow for a live report. People validate the standards mapping, approve definitions, review sensitive interpretations, and retain final authority for the report.
For high-stakes reporting, test extraction and qualitative coding across languages, document overrides, restrict access to sensitive participant data, establish retention rules, and keep the source text and citation available for review. AI should make the work more traceable—not make unsupported claims sound more polished.
Watch: connect definitions to reporting context
Watch the 1-minute 25-second introduction to using shared definitions across reporting frameworks. A proposed mapping still needs a source and review.
Watch the 6-minute 7-second explanation of the context that travels with a reporting field. If you watched it earlier, use this lesson to apply the idea to your own requirement.
Frequently asked questions
How do you turn reporting requirements into collectable evidence?
Identify the report and authoritative sources, extract each measure, breakdown, narrative, method, and document requirement, connect each to an approved organization definition, identify the evidence source and collection moment, and record gaps and human decisions. Test whether the resulting evidence can support the required output with citations and visible limitations.
What is a data dictionary for a nontechnical team?
It is the organization’s shared evidence language written down and governed. It says what every approved measure or evidence item means, how it is calculated or interpreted, where it comes from, who owns it, and where it can be used. Technical fields implement that agreement; they are not the starting explanation.
Do we need to choose one framework for the whole organization?
Not necessarily. Different reports may legitimately use different standards, frameworks, and methods. Govern the organization’s definitions once, then document how each external requirement relates to them. Do not force unrelated requirements into one framework or claim equivalence when populations, boundaries, periods, or calculations differ.
Can AI select the right SDG, IRIS+, or GRI metric?
AI can propose candidates and show definition differences, but it should not make the final selection. A responsible owner must confirm the reporting purpose, applicable source and version, population, unit, period, boundary, calculation, and disclosure requirements against the official material.
Are custom metrics less credible than standard metrics?
No. A custom metric may be essential when an external standard does not capture the service, community, or decision. Credibility comes from a clear definition, appropriate collection, stakeholder relevance, documented calculation, traceable evidence, consistent use, and honest limits—not from attaching a familiar label.
Can an email, interview, or PDF count as evidence?
Yes, when its role and limitations are clear. Preserve the source, author or speaker, date, context, access rule, and citation. A document can support a claim, reveal a requirement, or provide narrative context, but it is not automatically accurate or sufficient merely because it exists.
Should qualitative responses be converted into metrics?
Not automatically. Preserve the participant’s exact words. An approved coding method may group responses into themes, counts, or ordered categories, but the code and source text should remain linked and interpretations should be human-reviewed. Never infer a quantitative value or sensitive attribute that the respondent did not provide.
Is SROI just another reporting standard?
No. SROI is a methodology for analyzing social value. It requires stakeholder involvement, evidence of outcomes, judgments about materiality and value, attention to contribution and overclaiming, transparency, and verification. The evidence map can preserve inputs for a later SROI analysis, but it should not manufacture a monetary value before the method is properly applied.
What happens after the Reporting-to-Evidence Map is approved?
Turn each evidence requirement into a collection moment inside the real workflow: enrollment, attendance, service delivery, follow-up, interview, case note, partner report, or document review. Define who collects it, what validation occurs, how identity and time are linked, and when the evidence becomes available for analysis.
Sources and versions
- United Nations Statistics Division — SDG Indicators Metadata Repository
- GIIN — IRIS+ Catalog Downloads
- Global Reporting Initiative — GRI Standards
- Impact Frontiers — Five Dimensions of Impact
- Social Value International — Standards and Guidance
- Washington Group — Short Set on Functioning
- World Health Organization — WHODAS 2.0 Manual
Author: Sopact (Unmesh Sheth). Revised by Sopact Academy, September 12, 2026. Standards and methodologies change; verify the authoritative version and applicability before formal reporting, assurance, regulatory filing, or SROI analysis.
Related practice: You now know what every report requires, which approved measure answers it, where the evidence lives, and what is missing. Put that plan inside enrollment, service delivery, follow-up, interviews, and partner workflows in How Do You Collect Clean Evidence Inside the Workflow? →
Use How to Write an Impact Report to plan the final document, and explore report examples to check how evidence will be presented.