We structure and document your tracking, analytics and reporting systems so data is clearly defined, consistently implemented and usable across teams, platforms and programmes.
We structure and document your tracking, analytics and reporting systems so data is clearly defined, consistently implemented and usable across teams, platforms and programmes.
Most businesses collect data. Very few can trust it. Events get named inconsistently, tracking is partially implemented, dashboards lack clear definitions, different teams interpret the same metric differently, changes are made without documentation, and knowledge sits with individuals rather than inside the system. When the person who set it up leaves, the data effectively leaves with them.
Data Documentation solves this by treating documentation as a core system layer, not a support task. Events, KPIs, data flows and integrations are structured, versioned and readable by anyone on the team. So reporting is credible, decisions are faster, onboarding is painless, and system changes don't break everything downstream.
Events named three different ways. Dashboards built by someone who left. KPIs defined differently in Finance, Marketing and Product. Changes made in GTM that no one tracked. Reports that reconcile when two people calculate them manually but never when the dashboard refreshes.
Structured event libraries, versioned data layer schemas, aligned KPI definitions, annotated dashboards and mapped system integrations. One source of truth every team reads from. So reporting is trusted, audits are straightforward, and the system survives people changing roles.
Trustworthy data requires documentation that covers
Data sources
Event names and definitions, trigger conditions, parameters and properties, user actions and journeys, documented as structured event maps, naming conventions and event libraries. Every event fired on the site has a defined purpose, a documented trigger and a known parameter schema rather than existing as a mystery name in GA4.
Data layer schema, event sequencing, Tag Manager integration and platform-specific requirements, documented as data layer specs, event flow diagrams and implementation references. Developers implementing tracking work from a defined spec rather than interpreting a ticket, so what ships matches what was designed.
Core business KPIs, metric calculations, attribution logic and reporting rules, structured as KPI definition documents, calculation logic references and reporting frameworks. Finance, Marketing and Product read from the same definition of "conversion rate" rather than three plausible-but-different versions that never quite reconcile.
Dashboard structure and purpose, data sources and dependencies, update frequency and interpretation guidelines, delivered as annotated dashboards, user guides and metric explanations. Dashboards become usable tools rather than visuals where nobody remembers what the filter is doing or when the data last refreshed.
How data moves across website and eCommerce platforms, CRM and email systems, analytics tools and paid media, documented as system architecture diagrams, data flow maps and integration references. When something breaks or a platform is replaced, the impact is mapped rather than discovered by cascading failures.
Documentation structured for internal governance, external audits, grant and funding reporting and regulatory requirements, produced as audit-ready documentation, reporting structures and compliance references. When auditors or grant bodies ask for evidence, the answer is a document rather than a scramble.
Phase 01
We review existing tracking setup, analytics platforms, dashboards and integrations, identifying inconsistencies, gaps and risks. What's named inconsistently, what's missing entirely, what's firing but undocumented, and which reports quietly depend on one person remembering how it was set up.
Phase 02
We define event naming conventions, data models, KPI frameworks and reporting logic. The governing rules every piece of documentation will reference. One set of conventions rather than three competing ones depending on who's implementing at the time.
Phase 03
We create structured documentation sets, diagrams and mappings, and reference materials for teams. Everything is versioned, stored in a place the team already uses (Notion, Confluence, Google Drive) and written to be read rather than filed and forgotten.
Phase 04
We verify tracking accuracy, consistency across platforms and alignment with reporting. Testing that documented events actually fire as specified and that KPIs reconcile across dashboards. Documentation that doesn't match reality is worse than no documentation; validation closes that gap.
Phase 05
We maintain documentation as systems evolve. Updates for new features and campaigns, alignment across teams and platforms, change logs when integrations move. Documentation becomes a living system rather than a snapshot that ages into irrelevance six months after handover.
Data Documentation is the governance layer of Qwrki OS. The system that makes every other data-dependent capability in the stack trustworthy, transferable and audit-ready.
Data Documentation
Utilities & Resources
Tracking
Clear event structure and consistent tracking. The documented event library governs what gets fired, how it's named, and what parameters come with it. Tracking implementation references the docs rather than diverging every time a new page ships.
Analytics
Aligned dashboards and reporting frameworks. Every dashboard has a documented source, update cadence and interpretation guide. Metric definitions are consistent across GA4, Looker, Shopify analytics and any internal reporting layer.
CRM & Email
Reliable data for segmentation and lifecycle flows. Klaviyo segments and CRM logic read from documented event schemas, so a "high-value customer" means the same thing in email that it does in on-site personalisation and paid audiences.
eCommerce
Accurate product, transaction and behaviour tracking. Shopify event structure, product metadata and order data documented so commerce reporting reconciles with analytics, and platform changes don't silently break downstream flows.
Grant Mgmt
Structured reporting, compliance and audit readiness. Documentation formatted to meet grant funder expectations, internal governance requirements and external audits. Evidence exists as a document, not a scramble the day before the deadline.
Automation
Workflow triggers reference documented events and data schemas. So automations fire on the right conditions, produce traceable logs, and survive being handed between team members without breaking.
Clear, structured understanding of your data systems. Every event, flow, integration and metric documented in one referenceable place.
Consistent event tracking and naming. Conventions applied across platforms, so reports reconcile rather than quietly disagreeing.
Aligned KPI and reporting definitions. Finance, Marketing, Product and leadership read from the same definitions rather than competing interpretations.
Reliable dashboards that teams trust. Annotated, sourced and maintained, so the number shown is the number everyone agrees it should be.
Reduced dependency on individual knowledge. System understanding lives in documentation, not in the heads of two people at risk of leaving.
Faster onboarding for new team members. New hires get productive in days rather than months because the system is readable.
Support for audits, compliance and funding. Documentation structured to meet grant reporting, regulatory and governance requirements.
A system that can evolve without breaking. Platform migrations, tool changes and feature launches handled with known impact rather than cascading surprises.
Rationalising a sprawl of inconsistent event names, duplicated triggers and undocumented GTM tags. Replaced with a governed event library that every team reads from and every new feature extends rather than works around.
Ending the "three different versions of conversion rate" problem. Producing canonical KPI definitions, calculation references and reporting frameworks so Finance, Marketing, Product and leadership reconcile rather than debate.
Documenting current-state tracking and reporting before a migration (GA Universal to GA4, Shopify replatforms, CRM moves), so the upgrade preserves what works and explicitly handles what's changing rather than discovering gaps post-launch.
Producing audit-ready documentation for funders, governance bodies and regulatory requirements. Reporting structures that meet compliance expectations without requiring a special-project sprint every time something needs evidencing.
Onboarding new hires, new agencies or new internal teams into existing data systems. Structured reference materials that make the system learnable in days rather than months, and transferable across people without loss of fidelity.
De-risking the "only one person understands our tracking" scenario. Moving institutional knowledge out of individual heads and into governed documentation, so key-person dependencies stop being a quiet operational risk.
Get a data audit and documentation proposal for your business.
Talk to a humanPerformance evidence
First-party RUMReal browser field data from public visits. No provider or synthetic values.
LCP is loading, INP is interaction response and CLS is layout stability. Values are a daily privacy-minimised sample, retained for about 35 days. No IP address, browser identifier, referrer, user or session ID is stored.
Most businesses collect data. Very few can trust it. Events get named inconsistently, tracking is partially implemented, dashboards lack clear definitions, different teams interpret the same metric differently, changes are made without documentation, and knowledge sits with individuals rather than inside the system. When the person who set it up leaves, the data effectively leaves with them.
Data Documentation solves this by treating documentation as a core system layer, not a support task. Events, KPIs, data flows and integrations are structured, versioned and readable by anyone on the team. So reporting is credible, decisions are faster, onboarding is painless, and system changes don't break everything downstream.
Events named three different ways. Dashboards built by someone who left. KPIs defined differently in Finance, Marketing and Product. Changes made in GTM that no one tracked. Reports that reconcile when two people calculate them manually but never when the dashboard refreshes.
Structured event libraries, versioned data layer schemas, aligned KPI definitions, annotated dashboards and mapped system integrations. One source of truth every team reads from. So reporting is trusted, audits are straightforward, and the system survives people changing roles.
Trustworthy data requires documentation that covers
Data sources
Event names and definitions, trigger conditions, parameters and properties, user actions and journeys, documented as structured event maps, naming conventions and event libraries. Every event fired on the site has a defined purpose, a documented trigger and a known parameter schema rather than existing as a mystery name in GA4.
Data layer schema, event sequencing, Tag Manager integration and platform-specific requirements, documented as data layer specs, event flow diagrams and implementation references. Developers implementing tracking work from a defined spec rather than interpreting a ticket, so what ships matches what was designed.
Core business KPIs, metric calculations, attribution logic and reporting rules, structured as KPI definition documents, calculation logic references and reporting frameworks. Finance, Marketing and Product read from the same definition of "conversion rate" rather than three plausible-but-different versions that never quite reconcile.
Dashboard structure and purpose, data sources and dependencies, update frequency and interpretation guidelines, delivered as annotated dashboards, user guides and metric explanations. Dashboards become usable tools rather than visuals where nobody remembers what the filter is doing or when the data last refreshed.
How data moves across website and eCommerce platforms, CRM and email systems, analytics tools and paid media, documented as system architecture diagrams, data flow maps and integration references. When something breaks or a platform is replaced, the impact is mapped rather than discovered by cascading failures.
Documentation structured for internal governance, external audits, grant and funding reporting and regulatory requirements, produced as audit-ready documentation, reporting structures and compliance references. When auditors or grant bodies ask for evidence, the answer is a document rather than a scramble.
Phase 01
We review existing tracking setup, analytics platforms, dashboards and integrations, identifying inconsistencies, gaps and risks. What's named inconsistently, what's missing entirely, what's firing but undocumented, and which reports quietly depend on one person remembering how it was set up.
Phase 02
We define event naming conventions, data models, KPI frameworks and reporting logic. The governing rules every piece of documentation will reference. One set of conventions rather than three competing ones depending on who's implementing at the time.
Phase 03
We create structured documentation sets, diagrams and mappings, and reference materials for teams. Everything is versioned, stored in a place the team already uses (Notion, Confluence, Google Drive) and written to be read rather than filed and forgotten.
Phase 04
We verify tracking accuracy, consistency across platforms and alignment with reporting. Testing that documented events actually fire as specified and that KPIs reconcile across dashboards. Documentation that doesn't match reality is worse than no documentation; validation closes that gap.
Phase 05
We maintain documentation as systems evolve. Updates for new features and campaigns, alignment across teams and platforms, change logs when integrations move. Documentation becomes a living system rather than a snapshot that ages into irrelevance six months after handover.
Data Documentation is the governance layer of Qwrki OS. The system that makes every other data-dependent capability in the stack trustworthy, transferable and audit-ready.
Data Documentation
Utilities & Resources
Tracking
Clear event structure and consistent tracking. The documented event library governs what gets fired, how it's named, and what parameters come with it. Tracking implementation references the docs rather than diverging every time a new page ships.
Analytics
Aligned dashboards and reporting frameworks. Every dashboard has a documented source, update cadence and interpretation guide. Metric definitions are consistent across GA4, Looker, Shopify analytics and any internal reporting layer.
CRM & Email
Reliable data for segmentation and lifecycle flows. Klaviyo segments and CRM logic read from documented event schemas, so a "high-value customer" means the same thing in email that it does in on-site personalisation and paid audiences.
eCommerce
Accurate product, transaction and behaviour tracking. Shopify event structure, product metadata and order data documented so commerce reporting reconciles with analytics, and platform changes don't silently break downstream flows.
Grant Mgmt
Structured reporting, compliance and audit readiness. Documentation formatted to meet grant funder expectations, internal governance requirements and external audits. Evidence exists as a document, not a scramble the day before the deadline.
Automation
Workflow triggers reference documented events and data schemas. So automations fire on the right conditions, produce traceable logs, and survive being handed between team members without breaking.
Clear, structured understanding of your data systems. Every event, flow, integration and metric documented in one referenceable place.
Consistent event tracking and naming. Conventions applied across platforms, so reports reconcile rather than quietly disagreeing.
Aligned KPI and reporting definitions. Finance, Marketing, Product and leadership read from the same definitions rather than competing interpretations.
Reliable dashboards that teams trust. Annotated, sourced and maintained, so the number shown is the number everyone agrees it should be.
Reduced dependency on individual knowledge. System understanding lives in documentation, not in the heads of two people at risk of leaving.
Faster onboarding for new team members. New hires get productive in days rather than months because the system is readable.
Support for audits, compliance and funding. Documentation structured to meet grant reporting, regulatory and governance requirements.
A system that can evolve without breaking. Platform migrations, tool changes and feature launches handled with known impact rather than cascading surprises.
Rationalising a sprawl of inconsistent event names, duplicated triggers and undocumented GTM tags. Replaced with a governed event library that every team reads from and every new feature extends rather than works around.
Ending the "three different versions of conversion rate" problem. Producing canonical KPI definitions, calculation references and reporting frameworks so Finance, Marketing, Product and leadership reconcile rather than debate.
Documenting current-state tracking and reporting before a migration (GA Universal to GA4, Shopify replatforms, CRM moves), so the upgrade preserves what works and explicitly handles what's changing rather than discovering gaps post-launch.
Producing audit-ready documentation for funders, governance bodies and regulatory requirements. Reporting structures that meet compliance expectations without requiring a special-project sprint every time something needs evidencing.
Onboarding new hires, new agencies or new internal teams into existing data systems. Structured reference materials that make the system learnable in days rather than months, and transferable across people without loss of fidelity.
De-risking the "only one person understands our tracking" scenario. Moving institutional knowledge out of individual heads and into governed documentation, so key-person dependencies stop being a quiet operational risk.
Get a data audit and documentation proposal for your business.
Talk to a humanPerformance evidence
First-party RUMReal browser field data from public visits. No provider or synthetic values.
LCP is loading, INP is interaction response and CLS is layout stability. Values are a daily privacy-minimised sample, retained for about 35 days. No IP address, browser identifier, referrer, user or session ID is stored.
Reference
Versioned · team-ready
Reference outputs
Data sources · Documentation system · Governed reference · Trusted decisions
Personalisation
Segmentation logic built on documented behavioural events. The Personalisation Engine reads from a known event library, so rules reference stable inputs rather than events that might silently be renamed next quarter.
Reference
Versioned · team-ready
Reference outputs
Data sources · Documentation system · Governed reference · Trusted decisions
Personalisation
Segmentation logic built on documented behavioural events. The Personalisation Engine reads from a known event library, so rules reference stable inputs rather than events that might silently be renamed next quarter.
