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Data, Reclaimed
What It Looks Like When You're Close to the Customer
Summary
Let's delve into why business leaders have data, but can't use it fast enough for it to matter. Cameron Price's conclusion to The Death and Rebirth of Data series (Part 5), outlines why data drifted from the business and what it takes to bring it back. What he describes strategically, I see play out in real conversations every day. This isn't about technology, it's about the friction between having information and actually being able to act on it. In this blog, I'll share what data problems look like from the customer side: the trust gaps, the experience failures, and the ownership shifts that turn data from obstacle into momentum.
Fresh Perspective
When Theory Meets Reality
Cameron Price's final piece in The Death and Rebirth of Data series maps out the strategic shift needed to reconnect data with business value. From where I sit, those principles aren't abstract, they're the difference between teams that move fast and teams that stay stuck.
I spend my days talking to business leaders, customer-facing managers, and teams who live in the messy middle between having data and actually using it. The gap isn't theoretical. It's felt every single day in delayed decisions, frustrated conversations, and opportunities that slip through the cracks.
What I've learned is this: when data works, it doesn't feel like data. It feels like clarity. Like momentum. Like finally having the answer you've been circling for months.
When data works, it doesn't feel like data. It feels like clarity.
The Friction No One Talks About
Data problems don't announce themselves as technology failures. They show up as friction. As the three-day turnaround for a simple question. As the meeting that gets rescheduled because the numbers aren't ready yet. As the gut-check decision made without data because waiting for it would mean missing the window entirely.
One customer told me recently: We have all this data, but it takes so long to get an answer that by the time we have it, the question has changed. That's not a data problem. That's a trust problem. That's a speed problem. That's an experience problem.
The gap between having data and being able to use it is where businesses lose time, confidence, and competitive advantage.
It's where good intentions meet real-world constraints. And it's wider than most people realize until they're standing in the middle of it.
Industry research consistently shows this pattern. Dataversity reports that approximately 60% of business intelligence initiatives fail to deliver measurable business value when organisations prioritise tools and implementation over decision outcomes.
The Three-Day Wait
Simple questions take days because data lives in silos and requires multiple people to extract meaning.
The Changed Question
By the time you get the answer, business context has shifted and you're asking something different.
The Gut Decision
Teams make calls without data because waiting would mean missing critical windows of opportunity.
Trust Is Built on Understanding, Not Dashboards
The trust gap isn't about whether the data is accurate.
It's about whether people understand what they're looking at well enough to act on it.
I hear this constantly: "I don't trust these numbers because I don't know where they came from." Not because the source is unreliable, but because the path from raw data to insight is invisible. When you can't trace the logic, you can't trust the conclusion.
Trust comes from ownership of meaning. When the business defines what metrics matter, how they're calculated, and why they're relevant, suddenly those numbers carry weight. They become real. They become actionable.
Dashboards don't build trust.
Understanding does. And understanding requires transparency,
not just visualization.
Data as an Experience, Not a Deliverable
Here's what changed my thinking:
Data isn't a thing you deliver. It's an experience you create.
When someone asks for data,
what they're really asking for is the ability to make a decision.
They want confidence. They want context.
They want to know what happens next.
Handing them a spreadsheet or a dashboard is only useful if the experience of engaging with that data gets them closer to action.
Ease of Discovery
Can they find what they need without hunting through tabs or asking someone else?
Clarity of Context
Do they understand what they're looking at and why it matters right now?
Speed to Action
Can they move from insight to decision without waiting for interpretation?
The best data experiences I've seen don't feel like you're using data. They feel like you're doing your job, and the insight is just there when you need it. Seamless. Contextual. Human.
Why Ownership Changes Everything
Ownership of data doesn't mean everyone becomes a data analyst. It means the business controls the meaning, not just the access.
When IT or analytics teams own data definitions, they're making assumptions about what matters to the business. Those assumptions are often close, sometimes spot-on, but rarely perfect. And the gap between close enough and exactly right is where trust erodes.
When the business owns the definitions, something shifts. Suddenly, people stop questioning the numbers and start using them.
They don't need to verify with someone else because they defined what customer retention or conversion rate means in the first place.
That ownership creates accountability. It creates speed. It creates momentum.
Gartner predicts that by 2027, 80% of data and analytics governance initiatives will fail unless they are directly aligned to tangible business outcomes.
Modern data management literature defines a data product as a domain-owned asset designed to deliver measurable business value, with embedded quality, governance, and semantics.
Business Defines Metrics
Teams decide what matters and how it should be measured based on actual business context.
Data Teams Enable
Technical teams build infrastructure that makes those definitions accessible and reliable.
Trust Compounds
When people own the meaning, they trust the numbers and act faster with confidence.
Insights Scale
The organization moves from one-off reports to sustainable, self-service insight generation.
Simplification from the Customer Side
Simplification isn't about dumbing things down. It's about removing everything that doesn't serve the decision at hand.
One of the biggest accelerators I see in customer success is when teams stop trying to provide every possible data point and start focusing on the three metrics that actually drive action. Not because the other data isn't valuable, but because cognitive load is real and clarity beats comprehensiveness every time.
Customers don't want more data.
They want less noise.
They want the signal to be obvious.
They want to look at something and immediately know whether things are going well or not, and what to do if they're not.
Simplification is an active choice. It requires understanding what matters most and having the discipline to let everything else fade into the background. That's harder than it sounds, but it's where real customer success happens.
Customers don't want more data. They want less noise.
Where AI Helps, and Where It Doesn't
AI is a multiplier, not a foundation. If your data experience is broken, AI will make it faster to get the wrong answer.
I'm watching organizations rush to implement AI-powered analytics, chatbots, and predictive tools on top of data environments that people don't trust or understand. The result is impressive demos and underwhelming adoption.
AI Excels At
  • Pattern recognition across massive datasets
  • Surfacing anomalies you wouldn't catch manually
  • Accelerating analysis when you know what you're looking for
  • Automating repetitive data transformations
AI Struggles With
  • Understanding business context without clear definitions
  • Building trust when the underlying data is questionable
  • Replacing the need for ownership and accountability
  • Making strategic decisions that require judgment
Where AI actually works is when it sits on top of a solid foundation. When data is trusted, owned, and simplified. When people understand what they're looking at. When the experience is already good, and AI makes it faster, smarter, more proactive.
AI as a multiplier of good systems is transformative.
AI as a bandaid for broken ones is just expensive theatre.
Research from the Centre for Business Analytics at Melbourne Business School highlights that more than 80% of analytics and AI projects fail due to poor alignment between data, people, and business context.
Studies on AI adoption consistently show that the majority of AI project failures stem from poor data readiness, unclear ownership, and lack of context rather than model capability.

This is where Latttice quietly works. Not by taking control away from teams, but by giving it back. The business owns meaning and intent. Engineers are able to hand over data without losing trust or control. Leaders gain confidence that AI is no longer experimental, but grounded in trusted, governed data. And governance itself stops being a blocker and starts becoming active, embedded into how data is created, shared, and used. From a customer success perspective, this is what progress actually feels like: clarity without friction, confidence without delay, and AI that finally has something solid to stand on.
What Success Looks Like in Practice
When data is working, you can feel it. Meetings get shorter because people arrive with answers, not questions. Decisions happen faster because the context is already there. Teams stop second-guessing numbers and start debating strategy.
That's the shift. From validating to acting. From reporting what happened to shaping what happens next.
1
Faster Decision Cycles
Teams report significantly reduced time from question to action when data trust is established.
2
Higher Engagement
Self-service adoption increases dramatically when business users own metric definitions.
3
Improved Confidence
Business leaders express greater trust in insights when they understand the underlying logic.
Success doesn't look like perfect dashboards or flawless pipelines. It looks like momentum. It looks like a team that's moving forward with confidence, using data as a tool instead of wrestling with it as an obstacle.
Coming Full Circle
Cameron talks about the rebirth of data as a return to purpose, to business ownership, to human experience. From where I sit, that rebirth is already happening in pockets. In conversations. In teams who've decided they're done with the old way and ready to build something better.
It's not a technology shift. It's a mindset shift. It's choosing experience over deliverables. Trust over dashboards. Simplification over comprehensiveness. Ownership over delegation.
From Technology Focus to Experience Focus
Shift from merely tool-centered thinking to prioritizing the user's interaction and ease of access to insights.
From Dashboards to Trust
Move beyond static reports to foster an environment where data is a foundation for shared understanding and confident decision-making.
From Comprehensiveness to Simplification
Prioritize clarity and focus by presenting only the most critical information, enabling faster and more impactful decisions.
From Delegation to Ownership
Empower individuals to manage and understand their data, fostering accountability and a deeper connection to business outcomes.
The data was always there. What's being reclaimed is the relationship between people and insight. Between question and answer. Between having information and actually being able to use it.
That's the rebirth. And it's worth building toward.
Join a data conversation,

Lili Marsh.
References
Dataversity (2025). Why 60% of BI Initiatives Fail (and How Enterprises Can Avoid It).
Analysis highlighting that the majority of BI initiatives fail to deliver business value when treated as technical implementations rather than strategic decision-enablers.
Gartner, Inc. (28 February 2024). Gartner Predicts 80% of Data & Analytics Governance Initiatives Will Fail by 2027.
Gartner research explaining why governance efforts fail when they are disconnected from business outcomes and value creation.
Centre for Business Analytics, Melbourne Business School (2024). Why Do Analytics and AI Projects Fail?
Research identifying misalignment between data, people, and business context as the leading cause of analytics and AI project failure.
Wikipedia Contributors (2025). Data Product.
Definition and principles outlining data products as reusable, domain-owned assets designed to deliver measurable business value.
Ethyca Research (2025). Why 80% of AI Projects Fail: Data Readiness for the Speed of AI.
Research demonstrating that AI project failures are primarily driven by poor data foundations rather than limitations in AI technology.