What impact data is, why it fails from drift rather than absence, what impact data software must do beyond storing it, and the data dictionary that keeps it readable.
Impact data is evidence used to understand changes in people, communities, organizations, systems, or the environment and the contribution of a program, policy, grant, investment, or business activity. It includes structured measures, demographics, services, transactions, open-ended responses, interviews, observations, documents, benchmarks, and contextual evidence. Impact data is useful only when definitions, identities, dates, permissions, calculations, and sources remain clear.
Four funders can mean four spreadsheets rebuilt every reporting season. A data dictionary starts with a more useful question: what does each measure actually mean? Define the population, unit, time period, inclusion and exclusion rules, and calculation once so each report uses the same approved meaning.
Watch: One Data Dictionary, Every Impact Framework: IRIS+, SDG & ESRS Without the Rebuild.
The video explains how shared definitions support framework mappings and sector-specific vocabulary. Label each mapping Exact, Related, or Organization-Specific so a useful connection is not mistaken for an equivalent measure. Funder templates and branded reports can draw on the same governed data; each framework's evidence requirements still need to be checked.
Read the full method: build a portfolio data dictionary and map measures to standards →
Key takeaways
Organizations rarely lack data. They have surveys, spreadsheets, case systems, grant reports, interviews, notes, and PDFs. Problems arise when the same measure has different definitions, records cannot be matched, reporting periods drift, corrections are undocumented, or qualitative evidence is detached from the person or program it describes.
Cleaning at the end cannot recover evidence that was never linked, timestamped, or collected from the right population. The data architecture must preserve meaning and source from the moment evidence enters the workflow.
Sopact connects authorized quantitative, qualitative, document, and longitudinal evidence to persistent people, organizations, programs, cases, grants, or investments. A governed data dictionary defines fields, measures, calculations, segments, and source rules.
Teams can ask questions across the connected record, inspect missingness and contradictions, open the sources behind a finding, and produce audience-specific reports without changing the underlying meaning — because the dictionary fixes what a field is, while the context kept beside it decides how the answer should be read.
Watch (6:07): two participants both answer 3 on a confidence scale — one a baseline, one a drop from 5 — and why a dictionary that defines the field perfectly still cannot tell them apart.
Use one reported claim and trace it through the full data workflow. Include a quantitative measure, an open-ended explanation, a document, a second period, a correction, a missing record, and the audience view.
Program, MEL, grant, and portfolio teams should be able to update definitions, review missingness, correct records, and answer routine questions without rebuilding exports.
How the options differ
Measures, comments, documents, services, grants, and follow-up need stable people, organization, program, or investment identities.
How the options differ
The platform should handle real row counts, long text, files, repeated updates, and exceptions at the required cadence.
How the options differ
Impact data must support change across baseline, delivery, exit, follow-up, reporting periods, and corrected history.
How the options differ
Open-ended responses, interviews, observations, and notes explain why a measure moved and reveal unexpected effects.
How the options differ
Applications, partner reports, evaluations, plans, policies, and case documents contain material impact evidence.
How the options differ
An assistant should answer only within approved definitions and permissions and disclose included records, calculations, exclusions, and evidence.
How the options differ
Reliable impact data preserves definitions, transformations, corrections, calculations, qualitative boundaries, model configuration, review, and sources.
How the options differ
A useful evidence record combines the types required by the decision rather than treating one source as complete.
| Data type | Examples | What it contributes |
|---|---|---|
| Identity and context | Participant, household, partner, site, program, grant, investment, demographics, location, dates | Defines who or what the evidence describes and supports segmentation and longitudinal follow-up. |
| Activity and service data | Enrollment, attendance, dosage, referrals, training, mentoring, funding, engagement | Shows what was delivered, to whom, when, and with what intensity. |
| Outcome and impact measures | Skills, confidence, employment, health, wellbeing, income, stability, environmental or organizational change | Shows the direction, magnitude, distribution, and durability of change. |
| Qualitative evidence | Open-ended responses, interviews, case notes, observations, stories, stakeholder feedback | Explains experience, mechanism, barriers, unexpected effects, and differences between groups. |
| Documents and external evidence | Applications, reports, evaluations, policies, research, benchmarks, plans, verification records | Supports context, assumptions, standards alignment, verification, and source traceability. |
Yes. Keep survey, case, grant, CRM, learning, finance, portfolio, warehouse, BI, and research tools that serve their operational purpose. Connect only authorized evidence needed for a clear decision or report.
Start with the Academy lessons on building a data dictionary, connecting quantitative and qualitative data, and governed data reliability.
Impact data is evidence used to understand changes in people, communities, organizations, systems, or the environment and the contribution of an intervention or activity.
It is software that helps define, collect, connect, analyze, govern, and report quantitative, qualitative, document, and longitudinal evidence.
Activity data describes what was delivered, such as services, funding, sessions, or referrals. Impact data also examines outcomes, experience, context, contribution, and durability.
It is a governed set of definitions for identifiers, fields, measures, response options, segments, calculations, owners, cadence, sources, permissions, and standards mappings.
Authorized evidence, stable identities, approved definitions, consistent structure, documented quality, explicit boundaries, source citations, reproducible calculations, and human review.
Yes. Interviews, open-ended responses, observations, notes, and documents are often essential for explaining mechanisms, barriers, subgroup differences, and unintended effects.
Preserve identity, definitions, dates, units, versions, corrections, permissions, sources, and longitudinal relationships; review quality while evidence can still be corrected.
Use AI to read language, extract fields, identify patterns, flag gaps, and support questions. Keep governance, deterministic calculations, citations, permissions, and human judgment.
