Data and knowledge management
The Data and knowledge management pillar covers evidence capture, repositories, taxonomies, metadata, data retention, access control and knowledge reuse.
DATA-STO-ACC 1.0.0
Data and knowledge operating model
How research evidence, participant data, repository ownership and knowledge reuse are governed as an operating system.
DATA-STO-ACC 1.1.1
Research knowledge is an operational asset
Research evidence should be managed as a shared operating asset with clear ownership, stewardship, access rules, retention expectations and routes back into decisions.
DATA-STO-ACC 1.1.2
Assign an owner for each research knowledge system
Every research repository, evidence library, archive or knowledge base must have a named owner accountable for access, metadata quality, retention, review cadence and issue escalation.
DATA-STO-ACC 1.1.3
Separate raw data, analysis material and reusable knowledge
Design the knowledge system so raw participant material, working analysis and decision-ready insights have different storage, access, retention and reuse rules.
DATA-STO-ACC 1.1.4
Do not treat evidence as team property
People working with research evidence must respect participant conditions, organisational stewardship and future decision needs rather than storing knowledge only in personal files or local team spaces.
DATA-STO-ACC 2.0.0
Evidence capture and repository entry
How research material is captured, described and entered into approved knowledge systems.
DATA-STO-ACC 2.1.1
Create a repository entry for completed research
Completed research must have a repository entry that records the research question, method, audience, sample, date, owner, consent constraints, artefacts, findings and decision link.
DATA-STO-ACC 2.1.2
Evidence must keep its context
Research findings and recommendations should retain enough method, sample, limitation and consent context for later users to judge whether the evidence can support a new decision.
DATA-STO-ACC 2.1.3
Capture the minimum durable record
Where raw material cannot be retained, create a durable summary that preserves the decision need, key evidence, limitations, consent boundaries and route to the study owner.
DATA-STO-ACC 3.0.0
Metadata, taxonomy and findability
How evidence is named, tagged, described and made findable without losing meaning.
DATA-STO-ACC 3.1.1
Use the approved research taxonomy
Repository entries must use the approved taxonomy for service area, user group, method, risk, artefact type and decision theme unless an exception is recorded.
DATA-STO-ACC 3.1.2
Tag for decisions, not only projects
Add decision themes, policy areas, service stages and known assumptions so evidence can be discovered when future work cuts across previous project boundaries.
DATA-STO-ACC 3.1.3
Do not hide uncertainty in labels
People describing evidence must not use overconfident titles, tags or summaries that make weak, old, narrow or contested findings look stronger than they are.
DATA-STO-ACC 4.0.0
Access, privacy and permission
How access to research material is granted, reviewed and constrained by consent, privacy and sensitivity.
DATA-STO-ACC 4.1.1
Review consent and sensitivity before access is widened
Before research material is reused outside the original study team, the repository owner must check consent, confidentiality, sensitivity, retention limits and whether a lower-risk summary can meet the need.
DATA-STO-ACC 4.1.2
Access should be proportionate to the evidence need
People should receive the least sensitive form of evidence that can answer their legitimate decision need, with raw participant material treated as exceptional access.
DATA-STO-ACC 4.1.3
Use summaries for most cross-team reuse
Use de-identified summaries, insight records or evidence packs for most reuse requests, reserving transcripts, recordings and participant-level records for approved specialist review.
DATA-STO-ACC 5.0.0
Retention, disposal and auditability
How evidence is retained, reviewed, archived or disposed of in line with consent and operating value.
DATA-STO-ACC 5.1.1
Apply retention rules to research material
Research material must have a retention rule that states what is kept, where it is kept, when it is reviewed, when it is deleted or archived and who approves exceptions.
DATA-STO-ACC 5.1.2
Separate disposal of raw data from retention of learning
When participant-level data reaches its disposal point, retain only the approved level of de-identified learning, decision trace and operating metadata needed for future reuse.
DATA-STO-ACC 5.1.3
Do not keep participant material because deletion is inconvenient
People managing research material must not retain recordings, transcripts, contact details or sensitive records beyond approved retention limits for convenience or personal backup.
DATA-STO-ACC 6.0.0
Reuse, synthesis and decision trace
How existing research knowledge is reused, synthesised and connected to organisational decisions.
DATA-STO-ACC 6.1.1
Check existing evidence before commissioning new research
Before commissioning or approving new research, teams must check whether existing evidence can answer the decision need or narrow the research scope.
DATA-STO-ACC 6.1.2
Reuse should preserve evidence limits
Existing evidence should be reused with its original method, sample, date, context, confidence and consent limits visible to the new decision maker.
DATA-STO-ACC 6.1.3
Create synthesis when patterns recur
When multiple studies show a recurring pattern, create a synthesis record that states the pattern, source studies, confidence level, contradictions and current decision relevance.
DATA-STO-ACC 7.0.0
Repository health and knowledge learning
How the knowledge system is reviewed, improved and measured for operating value.
DATA-STO-ACC 7.1.1
Review repository health on a fixed cycle
Repository owners must review repository health on a fixed cycle, including stale entries, missing metadata, unresolved access issues, retention exceptions and evidence reuse.
DATA-STO-ACC 7.1.2
Measure knowledge by retrieval and decision use
Track whether evidence is findable, reused, cited in decisions, used to reduce duplicate research and trusted by researchers and decision makers.
DATA-STO-ACC 7.1.3
Good knowledge systems make evidence reusable without making it unsafe
A mature data and knowledge system should increase the value of research evidence while reducing privacy risk, duplication, context loss and unsupported claims.
Glossary
- Research repository
- An approved system or structured collection used to store, describe, retrieve and reuse research evidence.
- Evidence trace
- The link between a research claim and the source material, method context, consent conditions and limitations that support it.
- Retention rule
- A documented rule that states what research material is retained, reviewed, archived or deleted and who approves exceptions.
- Knowledge reuse
- The controlled use of existing research evidence to inform a new decision, reduce duplicate work or frame future research.
Change history
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2026-07-02
Expanded DATA-STO-ACC into an end-to-end data and knowledge lifecycle covering operating model, evidence capture, metadata, access, retention, reuse and repository health.