Most organisations don’t struggle with data because they lack platforms, pipelines, or tooling.
They struggle because no one is truly accountable for the value their data is supposed to create.
In this episode, Roland Brown tackles one of the most misunderstood and quietly destructive aspects of modern data product thinking: ownership. Building directly on Episodes 60 through 64, he explores why so many data initiatives stall after delivery, why trust erodes over time, and why value disappears even when the data is technically sound.
Roland explains that many organisations claim to have “owners” for their data products but what they really have are custodians of pipelines, tables, or dashboards. When ownership is defined around assets instead of outcomes, accountability becomes fragmented, decisions slow down, and responsibility evaporates the moment something goes wrong.
The episode breaks down the most common ownership models seen in practice:
• platform-owned data
• centrally governed data
• engineering-owned outputs
• domain-owned products
and explains why only one of these reliably scales value.
A critical distinction is introduced between being responsible for data and being accountable for decisions enabled by data. Roland shows how true data product ownership means standing behind definitions, quality trade-offs, prioritisation, and change not just delivery milestones.
Drawing on earlier episodes, he connects ownership directly to the anatomy of a data product introduced in Episode 62:
• Intent becomes enforceable only when someone owns the decision it supports
• Semantics stabilise when one owner resolves ambiguity
• Quality becomes contextual when owners understand how data is actually used
• Interfaces improve when owners feel the pain of poor consumption
• Lifecycle management becomes possible when someone can say “this no longer delivers value”
The episode also confronts a hard truth: shared ownership often means no ownership. When accountability is spread across committees, centres of excellence, or “the data team,” products survive technically but fail operationally. Consumers compensate with workarounds, shadow logic, and parallel definitions slowly draining trust from the system.
A practical ownership test is introduced:
When this data product causes harm not just inconvenience who gets the call?
If the answer is unclear, the product doesn’t truly have an owner.
Roland grounds the discussion with familiar examples from revenue, customer, and risk domains, showing how ownership models directly influence whether data products are trusted inputs to decisions or merely optional references.
The episode closes with a reframing that sits at the core of product-oriented data organisations:
ownership is not about control — it’s about accountability for outcomes.
When someone is clearly accountable for the value a data product is meant to deliver, trust becomes durable, decisions accelerate, and platforms finally start paying for themselves.
Discover insights on:
• Why asset ownership is not the same as product ownership
• The most common ownership models and where they fail
• How accountability shifts when decisions come first
• Why shared ownership often destroys trust
• How ownership connects directly to data product anatomy
• A practical test to identify whether your products are truly owned
“Pipelines have operators.
Platforms have administrators.
Data products need owners who are accountable for value."
🎧 Listen to The Data Journey wherever you get your podcasts, or visit thedatajourney.com
