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02/08/2026 11:50 am
TL;DR: Enterprises generate around 400 million terabytes of data per day, yet 67% of companies say they do not fully trust their data, according to the source article. Treating data as a product can improve discoverability, ownership, quality and reuse, but the governance model only works when access, lineage and reliability are enforced in the pipeline.
NHIMG editorial — based on content published by DataBahn: data as a product, governance and implementation practices
By the numbers:
- Modern organisations generate vast quantities of data, on the order of about 400 million terabytes per day, which is roughly 147 zettabytes per year.
- IoT devices alone are expected to exceed 21 billion by 2025.
- 76% of companies say data-driven decisions are a top priority, but 67% admit they do not fully trust their data.
Questions worth separating out
Q: How should organisations govern access to data products?
A: Start by treating each data product as a governed service with an owner, an access path, and explicit usage conditions.
Q: Why do lineage and metadata matter when data is reused by AI models?
A: AI models can amplify errors if the dataset feeding them is stale, incomplete or poorly understood.
Q: What breaks when data products do not have clear ownership?
A: When data products do not have clear ownership, requests stall, quality issues linger and no one is accountable for definitions or lifecycle changes.
Practitioner guidance
- Define ownership for every data product Assign a named owner for accuracy, availability and policy decisions before a dataset is shared beyond its source domain.
- Automate lineage and metadata capture Capture schema, transformation history, business definitions and usage context directly from the pipeline so catalog records stay current.
- Embed access control in the product interface Treat subscription, write permissions and masking rules as part of the product contract rather than a separate security ticket.
What's in the full article
DataBahn's full article covers the operational detail this post intentionally leaves for the source:
- Step-by-step implementation guidance for Smart Edge and Data Fabric in security and analytics pipelines
- Detailed examples of stream filtering, enrichment and routing decisions before SIEM ingestion
- Practical architecture patterns for Bronze, Silver and Gold layers in a productised data pipeline
- Source-specific discussion of quality guarantees, metadata capture and cost reduction mechanics
👉 Read DataBahn's analysis of data as a product and the governance model behind it →
Data as a product: is the governance gap really the blocker?
Explore further
02/08/2026 12:59 pm
Data trust gap: The real barrier to productised data is not storage scale but confidence in what each dataset means and who is allowed to use it. When 67% of organisations say they do not fully trust their data, the issue is governance maturity, not just tooling. Product thinking only works when ownership, lineage and access are built into the delivery model, otherwise the organisation simply repackages uncertainty as an asset.
A question worth separating out:
Q: How do organisations know whether a data product is actually trusted?
A: Trust shows up in behaviour, not declarations. If users repeatedly export the same data into spreadsheets, question the numbers in meetings or avoid using the catalogued version, the product has not earned confidence. Operational signals such as quality test pass rates, usage growth and fewer duplicate copies are better indicators than survey answers.
👉 Read our full editorial: Data as a product: why trust and governance still lag
