"As PMs, we're great at wrangling dependencies when they look like APIs, SDKs, or design systems. But we often treat data like infrastructure, until it delays a launch, derails an experiment, or erodes trust in a KPI. I want to encourage you to shift towards treating data as inventory, not infrastructure. That means managing data like a real product that your team depends on, tracking things like its quality, freshness, lead time, cost, and ownership to keep it organized, reliable, and accountable."
"A little while back, the product team that I managed was building a retention dashboard to guide pricing and lifecycle triggers. The linchpin was a customer identity dataset, which seemed straightforward: daily freshness, stable schema, easy joins. In reality, the identity resolution job lagged 24-60 hours under peak load. Issues started cropping up over time. A column rename slipped into production without a heads-up. The dashboard missed its window. Worse, a feature that reused those same identifiers slipped too."
Treat data as inventory rather than invisible infrastructure. Manage datasets as products with defined owners and measurable attributes: quality, freshness, lead time, cost, and ownership. Track and communicate those SLAs to prevent surprises, missed launch windows, and eroded KPI trust. Unmanaged data dependencies can cause latent failures: slow identity resolution, schema changes, and delayed pipelines can break downstream features despite engineering claiming completion. Embedding data-product metrics into PM rituals enables clearer commitments, faster incident detection, and more predictable feature delivery. Operationalizing data inventory reduces rework, experiment failures, and hidden technical debt across product teams.
Read at LogRocket Blog
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