On 3rd March 2026, we hosted a private data leadership roundtable over dinner in Manchester, bringing together CTOs, CDOs, CIOs, and senior digital leaders to discuss a problem that feels increasingly universal:
You’ve invested in a powerful data platform… so why is everyone still exporting to Excel?
The session was chaired by Sue Gouldson, Head of Data & Integration at SP Electricity North West. Sue shared her experience building a modern data function from the ground up and opened a candid discussion about the last mile problem in data transformation – the gap between technical capability and real organisational adoption.
Over three courses and some very open conversation, a number of clear themes emerged.
The Governance Problem: Who Actually Owns the Data?
One of the biggest issues raised during the discussion was data ownership.
Many organisations still operate with unclear or non-existent ownership structures for data. Without defined accountability, questions like the following become surprisingly difficult to answer:
- Who owns the metric?
- Who defines the calculation?
- Who resolves discrepancies?
A simple example raised during the discussion was the definition of a “new customer.”
Without agreed definitions, different teams can report entirely different numbers – even when working from the same underlying data.
The solution Sue described was the development of Centres of Excellence across key data domains. These groups help create shared standards, definitions, and documentation while still enabling teams to use the data in ways that suit their work.
The Importance of Shared Definitions
During the discussion, one attendee shared a useful analogy through a simple team exercise: “How do you make toast?”
When people are asked to write down the steps involved, the answers vary dramatically. Some start with buying bread. Others start with putting bread in the toaster. Some include buttering, others don’t.
The exercise highlights a common problem in data environments:
If people don’t agree on the process, they won’t agree on the data that measures it.
Before dashboards or analytics can work effectively, organisations need shared definitions and a common language around data.
Without that alignment, even the best platforms struggle to deliver trusted insights.
The Four Internal Personas Blocking Data Adoption
During the session, Sue also described four common reactions data teams encounter internally.
1. “Just give us the data”
These teams want raw access and prefer to bypass governance, dashboards, and agreed processes.
2. “Copy and paste lovers”
Highly manual workflows persist – exporting spreadsheets, copying data between files, and rebuilding analysis repeatedly.
3. “We don’t need help”
Some teams resist centralised data support entirely, believing their local approach works fine.
4. “We’ll take this away”
Siloed teams who prefer to solve problems independently rather than collaborate with central data teams.
None of these behaviours are malicious, but they slow down adoption and reinforce fragmentation.
Data Has Always Been There – But Why Does It Matter?
Another theme explored was data enablement.
The reality is that data itself is not new. What’s changed is the scale, accessibility, and expectation around it.
To build genuine adoption, organisations need to answer three simple but powerful questions:
- What do we actually need data for?
- How will it help the business make better decisions?
- Why should people care about it?
Different industries – from retail and logistics to insurance and construction – are wrestling with the same challenge.
The opportunity is enormous. Our lives and customers are increasingly digitally enabled, generating vast amounts of data. But unless organisations help people understand how to use it, that potential remains untapped.
The Real Challenge: Culture, Not Technology
Across the evening, one theme repeatedly surfaced:
Technology is rarely the limiting factor.
Most organisations already have:
- Modern data platforms
- Advanced analytics tools
- Visualisation capabilities
What they often lack is:
- Confidence in the numbers
- Training and enablement
- People who can bridge technical and business worlds
This skills gap – especially in data storytelling and communication – remains one of the hardest challenges to solve.
The Weight of Legacy
Another reality discussed openly was legacy reporting debt.
Many organisations have hundreds – sometimes thousands – of historical reports that nobody fully understands but everyone is reluctant to retire.
Sue’s team faced 600 legacy reports, forcing difficult conversations about:
- Which reports actually create value
- Which exist purely out of habit
- Which should be consolidated or retired
Modernisation is not just about building new dashboards. It’s also about letting go of outdated ones.
Looking Ahead: AI and the Need for Agility
The discussion naturally turned toward the future and the role of AI.
While AI offers huge potential, the group also raised an important question: do we always need it?
Many of the challenges organisations face today – manual reporting, spreadsheet workflows, and duplicated analysis – could often be solved through better automation and improved data processes alone.
Before introducing generative AI, several attendees suggested organisations should first ask whether the problem truly requires AI, or whether automation could deliver the same value more simply.
The group agreed that AI will only be as effective as the data foundations behind it.
Before organisations can unlock AI at scale, they still need:
- Clear definitions
- Reliable governance
- High-quality data
- Teams confident enough to use it
In many ways, the future of AI depends on solving the same cultural and organisational challenges discussed throughout the evening.
Final Thought: Helping Organisations Fall in Love with Data
Perhaps the most powerful idea to emerge during the conversation was about the ultimate goal of data transformation: helping people genuinely fall in love with data.
Not through mandates or dashboards alone, but through helping people see how data improves the decisions they make every day.
Because in the end, becoming a data-driven organisation isn’t about platforms or tools.
It’s about people believing the numbers are worth using.
