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How do you connect production data and quality control in one dashboard?
By putting production and quality data on one model and adding alerts, so a deviation reaches someone while it can still be acted on. A seed operations department needed reporting that was consistent, fast and proactive rather than after the fact. Fabric, dataflows and deployment pipelines delivered the integration; Data Activator delivered the alerts.
Seed production and agriculture Microsoft Fabric, Dataflows, Databricks, dbt, Power BI
The challenge
A seed operations department needed reliable, scalable business intelligence to support strategic and tactical decisions. The existing reporting lacked consistency, speed and proactive monitoring, which limited the ability to act on what the data showed.
Our approach
Complex business needs were translated into scalable data models and dashboards. Microsoft Fabric, dataflows and deployment pipelines provided the integration and automated delivery. Data Activator alerts added real-time monitoring, so a deviation reaches someone rather than waiting to be found.
For long-term consistency we helped define uniform data definitions and a governance framework, which made collaboration between teams possible. Tabular Editor was used to optimise the performance of the tabular models, and Databricks with dbt automated the large-scale transformations underneath.
Results
Reporting became faster, more consistent and more usable across the department. Real-time alerts made timely intervention possible, and the optimised models made analysis quicker and deeper. Aligning with governance practice produced reporting structures that could be reused rather than rebuilt.
Expertise involved
Close collaboration between IT, data professionals and policy advisers. Data governance, framework design and readiness for AI, tied together so that fundamental data principles and newer technology pointed the same way.
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Why were the existing reports not enough?
They lacked consistency, speed and any form of proactive monitoring. That meant insight arrived after the moment it could have been used, which is the same as not having it.
What does an alert add over a dashboard?
A dashboard requires someone to look. An alert reaches the person who can act, at the moment the deviation happens. For production processes that difference is most of the value.
How do you keep this consistent across teams?
By agreeing uniform data definitions and a governance framework before scaling. Without that, every team builds its own version of the same figure and the reporting stops being comparable.