The Real Reason Your Data Still Fails
- Spark
- 2 days ago
- 3 min read
Let’s get real here. Like most organisations, you probably still don't fully trust your data, do you? And, you’ve invested in new platforms, migrated to the cloud, built dashboards, and hired smart people. Yet the same frustrations remain, because data transformation isn’t just about technology, it’s about changing ownership and behaviour.
Reports that never quite match.
Definitions that keep changing.
Teams arguing over which spreadsheet is “right.”
Sound familiar?
Here’s the truth: your data problem isn’t technical, it’s cultural.
The Problem Isn’t the Tools. It’s the Ownership.
We love to blame the tech stack.“It’s the legacy system.”“It’s integration issues.”“It’s IT’s fault.”
But here's the real issue: no one truly owns the data.
Finance checks numbers when it’s time to close the books.
Ops fixes errors when they cause delays.
IT patches pipelines when something breaks.
No one wakes up thinking, “My job is to make sure our data is trusted, consistent, and valuable.”
Until someone does, your data will keep failing you.
If everyone’s responsible, no one is.
Treat Data Like a Product, Not a Project
Imagine if your customer-facing products were built the way your data is managed —by different teams changing specs, with no clear owner, no roadmap, and no feedback loops. You’d never ship.
High-performing organisations treat data as a product. That means it has:
A clear owner with accountability.
Defined users who rely on it.
Quality metrics that measure reliability and completeness.
A lifecycle — improved, maintained, and versioned over time.
This mindset changes everything. Ownership creates clarity. Clarity creates trust/ Trust creates speed.
Fix ownership, and quality follows.
Data Quality Is a Business Problem, Not an IT Problem
For too long, data has lived inside IT. But business teams create it, use it, and depend on it, so they must help shape it.
When finance treats data as a financial asset, when operations sees it as part of process design, when marketing treats it as brand equity — that’s when the culture shifts from compliance to performance. As McKinsey’s research on building data-driven cultures shows, sustained improvement only happens when ownership and accountability sit with the business.
Quality data isn’t a technology upgrade. It’s an organisational behaviour change.
Stop Measuring Activity. Measure Confidence.
Many organisations measure progress by delivery metrics: the number of reports automated, pipelines built, or dashboards launched. But that’s not success.
Success is when teams trust the data enough to make faster, better decisions without reconciliation meetings or side spreadsheets.
Ask:
Do people believe the data?
Are decisions faster?
Has confidence improved?
Measure trust, not traffic.
Spark’s View
Your data quality won’t improve until your ownership model does.
Technology amplifies behaviour, it doesn’t fix it. The organisations leading on data maturity didn’t buy better tools; they just built better habits.
They made data everyone’s responsibility, but someone’s job. They built governance that enabled, not restricted. And they treated data as a product that deserved
investment, care, and accountability.
Because when ownership is clear, data finally starts to work for the business — not the other way around.
The organisations leading on data maturity didn’t buy better tools; they built better habits. And they understood that AI success would depend on the same principle — strong data foundations and accountability. (Read: Why Most AI Pilots Fail — And What To Do Instead)
Next in the Series
👉 AI Starts With Data Integrity, Not Algorithms. Why every successful AI initiative depends on the same cultural shift toward ownership and accountability.
