Overview
As a global consumer products manufacturer prepared to launch a trade-promotion reporting platform, its business users lacked visibility into the data pipeline that powered it. When data appeared wrong or incomplete, they could not see what had changed, identify a pipeline failure, or determine whether action was needed. Every question became an IT support ticket.
Analytic Vizion helped the client establish proactive data observability: a business-facing capability that made pipeline health, validation states, and known data conditions visible before issues became escalations. The result was a more transparent, supportable reporting product ready for production use.
The Challenge
As the manufacturer prepared to launch a business-critical trade-promotion reporting platform, business users had no visibility into the data pipeline powering the insights they needed to trust.
This created several barriers:
- No view into pipeline health: Users could not see whether pipelines had run successfully, where an issue occurred, or which data conditions might affect reporting outputs.
- Silent production failures: Azure Data Factory jobs could succeed in debug mode but fail when triggered in production, without a clear signal identifying the root cause.
- Ticket-dependent troubleshooting: When data looked incomplete or unexpected, every question required a support ticket and a wait for IT investigation.
- No business-facing observability layer: The Databricks, Azure Data Factory, dbt, and Snowflake environment lacked an auditable way for users to monitor and investigate data-health conditions independently.
Without addressing these gaps, the client risked taking a high-visibility reporting product live with hidden failure conditions, an unsustainable support model, and limited stakeholder confidence in the data. That could undermine adoption, make service-level expectations harder to support, and force engineering teams to remain the first line of response for routine data questions.
Our Approach
Analytic Vizion combined user-centered design, disciplined delivery, and the client’s existing data stack to establish a proactive observability capability ahead of go-live.
People
Analytic Vizion partnered with business users to understand the real questions behind data issues: what happened, what data was affected, and whether the issue required technical escalation. Those scenarios shaped an observability experience that business stakeholders could use independently, while preserving engineering capacity for deeper technical investigation.
Process
The team first investigated and resolved the underlying production instability, including the Azure Data Factory trigger-versus-debug failure. From there, Analytic Vizion built and refined the observability capability iteratively, using ongoing end-user feedback throughout UAT to make pipeline conditions and known issues more transparent before full production rollout.
Technology
Analytic Vizion used Databricks for compute and monitoring logic, Azure Data Factory for pipeline orchestration, dbt for the models and configuration logic that surfaced validation states, and Snowflake as the validated production source of truth. The resulting dashboard exposed pipeline health and data conditions in an auditable format, with Power BI identified as the next-phase expansion platform.
Tools & Technology Stack
- Databricks
- Azure
- dbt
- Power BI
- Snowflake
The Results
The client established proactive data observability before go-live. The solution made pipeline health and validation states visible to business users. It eliminated a class of silent production failures and restored reliable pipeline runs.
Business users could audit data conditions and investigate issues without opening an IT ticket for every question. This reduced reliance on engineering teams for initial troubleshooting and created a clearer escalation path for complex issues.
The observability dashboard improved visibility during UAT by documenting known data issues before production rollout. The client later funded a Power BI expansion, extending data observability beyond the initial reporting platform.
Key Takeaways
Data observability is essential to trusted self-service
A dashboard can make data available, but users cannot confidently act on it when they lack visibility into the pipelines, validations, and conditions behind the output.
Silent failures are a business-risk issue, not only an engineering issue.
When pipeline conditions are hidden, business users lose trust in the output and technical teams inherit every question. Making failures detectable and understandable protects adoption as a data product scales.
Effective observability is designed around action.
The most useful signals are not merely technical alerts. They help users understand what happened, assess whether the condition affects them, and know when an escalation is warranted.
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