Before adding another dashboard, agree on what the numbers mean
Databricks’ October 29 Procore case study article describes how Procore evolved its construction analytics architecture to deliver curated data more frequently. One detail stood out to me: the same modeled data supports both application reporting and customer extracts.
That connects directly with my work across facilities applications, API integrations, SQL, Python automation, and Cognos reporting. Consider a maintenance backlog report. Two teams can query the same system and still produce different totals because they handle canceled work orders, status changes, or reporting dates differently. A faster refresh won’t resolve those differences. When designing an operational data product, I would start with four questions: • What does each measure mean? • Which records belong in the calculation? • Who owns the definition and validates changes? • Can users see when the data was last updated? Then build the extraction, transformation, and reporting around those answers. For facilities and asset management, shared definitions make it easier to interpret workload, costs, and performance consistently. My takeaway: treat metric definitions and data ownership as part of the product. They deserve the same attention as the interface people see.
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