Data Reliability & Governance
Data you can trust blindly. We implement automated monitoring, quality checks, and clear access controls so your data infrastructure scales.
- dbt
- Great Expectations
- Monte Carlo
- Soda
- BigQuery
- Slack Alerts
What we build
At some point, no one trusts the numbers any more. Marketing says 500 leads, CRM shows 320, finance counts 280 paying customers. They're all right, and all wrong. The problem isn't which system is correct. The problem is that nobody defined what "lead" means. And nobody is responsible for ensuring the definition is implemented consistently everywhere.
Data Reliability & Governance is the structural answer: clear definitions, automated data-quality tests, documented processes, and an access model that prevents people from accidentally breaking production dashboards.
Who it's for
Companies with number conflicts
Internal meetings regularly start with debating which numbers are "the right ones". That's not a data problem, it's a governance problem. And it costs more time than the solution.
BI teams under upstream load
Struggling with poor data quality from marketing systems. If upstream data isn't reliable, every analysis is only as good as its weakest source.
Scaling companies
The previous "everyone does data their own way" no longer works. Past a certain size, data needs its own governance structure.
Pre-data-lake teams
About to build a data lake and want to make sure the foundation is right before they scale.
The three pillars of the service
Definitions & business glossary
If "lead" means three different things across marketing, CRM, and finance, no dashboard will fix it. We bring stakeholders into one room, define the central metrics once, and store them in one place that every downstream system can reference.
What we actually do:
- Stakeholder mapping: who decides based on which metric
- Business glossary with binding definitions for Lead, MQL, SQL, conversion, revenue
- Mapping of definitions to the concrete fields in GA4, CRM, and ERP
- Change-management process for definition updates
Tools: Notion / Confluence, dbt docs, DataHub
Automated data-quality monitoring
Definitions without tests drift again. We build automated checks for the critical pipelines, completeness, freshness, plausibility, so anomalies arrive as a Slack alert before they surface in a dashboard.
What we actually do:
- Define the critical tests per pipeline (marketing, revenue, product)
- Implementation in dbt tests or Great Expectations
- Slack or email alerting with diagnostic context (what, since when, severity)
- Data-quality dashboard: which sources are green, which need intervention
Tools: dbt tests, Great Expectations, BigQuery, Slack Alerts
Schema control & access model
Scaling data setups rarely fail because of tools, they fail because someone accidentally changes a schema or overwrites a production view. A clear RBAC model and schema change management close that gap.
What we actually do:
- Audit of existing access rights: who sees what, who can change what
- RBAC design for data lake and BI tools (BigQuery, Snowflake, Power BI)
- Schema change management with review gates for breaking changes
- Data catalogue with owner assignment per table
Tools: BigQuery IAM, dbt, DataHub, OpenMetadata
Deliverables
Joint engagement with clear ownership split, datascale delivers definitions, schema architecture, and governance processes, Saloid the technical implementation.
Data quality monitoring
- Automated tests for critical data pipelines (completeness, freshness, plausibility)
- Alerting on data-quality issues via Slack or email
- Data-quality dashboard: which sources deliver reliably, which have problems
Schema control & documentation
- Data catalogue: all relevant data sources, tables, fields, described and versioned
- Business glossary: what does "conversion" mean? What does "lead" mean? Defined once, valid everywhere
- Schema change management: process for changes that can break downstream reports
Roles & access model
- Audit of existing access rights: who sees what, who can change what
- RBAC design (Role-Based Access Control) for data lake and BI tools
- Implementation in BigQuery, Snowflake, or Power BI
Governance processes
- Data owner definition: who is responsible for which data
- Change management process for metric changes
- Onboarding process for new data sources
Tools & stack
Definitions & business glossary
- Notion or Confluence for glossary upkeep
- dbt docs for model-level technical definitions
- DataHub or OpenMetadata as the data catalogue
- Stakeholder-workshop format for definition alignment
Monitoring & alerting
- dbt tests (completeness, freshness, plausibility)
- Great Expectations for richer suites
- BigQuery EU region for test execution
- Slack alerts with severity and diagnostic context
Schema & access model
- BigQuery IAM or Snowflake RBAC
- Schema-change management with review gates for breaking changes
- Owner assignment per table in the data catalogue
- Audit logs for production pipelines
Engagement depths
Three depths. Clear scopes.
No retainer trap.
Audit Sprint
We audit what's wrong. Report + prioritised action plan.
plus statutory VAT · fixed price for a clearly bounded scope
Included in the fixed price
- 1 domain
- 1 analytics property
- 1 tag manager / tracking setup
- 1 CMP
- up to 5 core conversions
- 10 working days
- PDF report + 90-min walkthrough
What you get
- Full analysis of your existing setup
- Prioritised report with concrete action items
- Walkthrough call with the team (90 min)
- No follow-up contract, no retainer obligation
When it fits
When the setup works but the numbers are being argued about internally. Or you're unsure what from a UA→GA4 migration still holds.
For e-commerce, multiple domains or App + Web: Audit Sprint Plus, €3,900 net fixed price. Bonus: 50 % of the Audit Sprint credits toward a Build Sprint commissioned within 30 days.
Request an Audit SprintBuild Sprint
Fresh build or restructure of a tracking setup.
plus statutory VAT · final fixed price after scope definition
Typical scope
- 1 domain (multi-domain on request)
- 1 analytics property (GA4 or Piwik PRO)
- server-side container (Stape or own cloud)
- 1 CMP with Consent Mode V2
- up to 15 events / conversions
- 4–8 weeks delivery
- Blueprint, QA sign-off, handover docs
What you get
- Measurement Blueprint for your dev team
- GTM + server-side setup incl. CMP integration
- Full QA against the blueprint with sign-off
- Handover docs + 30-day post-launch support
When it fits
When analytics is structurally broken and fixing it in-flight costs more than a clean rebuild.
Managed Evolution
Ongoing partnership. Analytics as a product, not a one-off project.
plus statutory VAT · monthly cancellation after the minimum term
Included in the monthly price
- up to 3 domains under active care
- GA4 + server-side stack maintenance
- monthly roadmap + sprint planning
- QA on every release deploy
- Slack channel, < 4 h response (Mon–Fri)
- monthly report + executive summary
- 3-month minimum, then monthly
What you get
- Monthly development + feature rollouts
- Ongoing QA on every deploy
- Executive reports + dashboard evolution
- Slack support with guaranteed response times
When it fits
When analytics has to grow with you (new campaigns, new products, new data sources) and you don't want to build that team internally.
All prices net, plus statutory VAT. For companies in Germany, Austria and Switzerland.
Full-Cycle Delivery, who does what
Datascale owns
- Audit of analytics data quality (GA4, Plausible, app tracking)
- Business glossary: KPI definitions shared by marketing and finance
- Data-quality dashboard for marketing-relevant metrics
Saloid delivers
- Automated data-quality tests in dbt (completeness, freshness, plausibility)
- Schema-change management and versioning
- RBAC implementation in BigQuery / Snowflake
- Data catalogue and technical documentation
- dbt tests
- BigQuery IAM
- DataHub / OpenMetadata
- Slack Alerting
B2B SaaS. Unified definition of "MQL" and "SQL" across marketing, CRM, and finance. Reduced "which numbers are right?" debates from weekly to zero.
Saloid: Data Engineering & Analytics Implementation-
What's the difference between data reliability and classic data quality?
Data quality measures individual values (completeness, format, range). Data reliability guarantees that a definition means the same thing across systems, and that automated tests keep it that way when someone changes a schema or connects a new source. Data quality is a snapshot; data reliability is a process.
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Do we already need a data lake or lakehouse?
No. We start with the sources that already exist. GA4, Plausible, CRM exports, ad APIs. If the audit shows a central layer is missing, that becomes a recommendation, not an upfront investment. Most reliability problems can be addressed without a lakehouse, as long as definitions and tests are properly documented once.
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Who takes on the ongoing data steward role after the setup?
Someone internal, from the data or analytics team. We build the framework, document the processes, and onboard the first steward, but governance only works if it's owned internally. In our experience, externally-staffed steward roles don't last 12 months.
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How long until the first quality alerts go live?
After a 2-week Audit Sprint the 3–5 most critical pipelines have alerts in place. The full build with data catalogue, RBAC model, and governance processes typically runs as a 4–8-week Build Sprint, together with Saloid for the technical implementation.
Next step
Data your team trusts blindly.
Strategy call about monitoring, quality checks, and access architecture. Full-cycle implementation together with Saloid.