Data as an Experience

Data initiatives have failed for decades not because of poor technology, but because the experience for business users has been confusing, slow, and disconnected from the way people naturally work. This is the shift that transforms data from something to consume into something you can experience, explore, and trust.

What does using data actually feel like for the business?

When Cameron published Part 4 of The Death and Rebirth of Data, titled Data as an Experience, one line stopped me completely.

It is a deceptively simple question, but it cuts straight to the heart of why so many analytics initiatives struggle. I have carried that question through every project, every customer conversation, and every transformation program I have worked on. Business teams want to use data. They want clarity, confidence, and insight. What they often receive instead is confusion, overwhelm, or tools that simply do not work for them.

Cameron approaches this topic as a thought leader, drawing on decades of industry evolution and architectural shifts. My perspective is grounded in the day-to-day experience of business users. I sit with teams as they try to navigate dashboards, search tools, definitions, and layers that were never designed with them in mind. I watch their enthusiasm dissolve into frustration when the experience falls short.

This blog is about that experience, why it matters, and how AI finally gives businesses the ability to own it.

For decades the data experience was designed around business users, not with them. This is why analytics initiatives so often fall short.

When I speak with business teams, their reactions are emotional rather than technical. There is the quiet confusion of not knowing where to start, what to trust, or how the pieces fit together. There is the frustration of tools designed for analysts that feel hostile to the people they are meant to serve, and the constant low-grade worry of doing it wrong, that one careless filter will produce the wrong answer. There are the long wait times for reports that arrive after the moment to act has already passed, the conflicting definitions where the same metric means different things in different rooms so no one trusts any of them, and finally the quick return to Excel, because Excel, for all its flaws, simply feels safer.

These reactions do not reflect a lack of skill. They reflect a lack of experience design.

As Cameron writes, meaning does not matter until a human can actually experience it.

This is the first time in decades that technology gives us a fundamentally new interaction model.

Generative AI allows the data experience to become:

People do not hesitate to ask questions in a conversation. AI makes that natural behavior possible in data for the first time.

This shift matters because it finally gives business users control. Not through training. Not through dashboards. Not through technical layers. Through experience.

With AI, business teams can:

• Ask questions in plain language
• Understand definitions instantly
• Explore safely without fear
• Discover insights without hunting
• Build trust through transparency
• Make decisions at the speed of conversation

This is the foundation of a true data experience.

Latttice, the Data Product Workbench, was designed to solve exactly this gap. Not to recreate the past, but to redesign the experience entirely.

Latttice gives business users:

When organizations adopt Latttice, the shift is immediate. Teams no longer wait weeks for answers. Decisions get made when they are needed, because Latttice is an AI-powered, zero-code technology that lets business teams build their own data products without delay. Trust becomes inevitable, because the data is now in the hands of the business without uncertainty or hand-offs. The business develops a new attitude to what is possible. Curiosity flourishes because the experience feels natural. A question can be asked at the moment it matters and answered in time to make a difference. And finally, AI becomes a real possibility, because the data it depends on is available, governed, and trusted.

Semantic layers were created to standardize meaning. But meaning alone is not enough.

Experience is what makes meaning useful.

Experience is what makes AI valuable. Experience is what turns data into action rather than a dashboard.

There are five signals that tell you the problem is not your stack but the experience around it. If your dashboards sit unused, the issue is rarely the chart, it is the experience that surrounds it. The challenge with Data Mesh was never the vision; it was that the architecture often required levels of engineering complexity that made business-led adoption almost impossible in practice. If teams describe your tools as overwhelming, they are telling you, plainly, that the experience is wrong. If Excel keeps winning, it is winning on experience and not on capability. And if your investment in analytics has not delivered the outcomes you expected, experience is almost always the gap between the promise and the reality.

It is not a failure of intelligence or capability. It is a failure of experience. And experience can always be redesigned.

If you want to experience data differently, intuitively, confidently, and with real clarity, join a data conversation.

• Gartner. "Why Data and Analytics Programs Fail." 2023.
• BARC Research. "The State of Data and Analytics Adoption." 2023.
• Forrester. "The Rise of Data Product Thinking." 2022.
• ThoughtWorks. Zhamak Dehghani. "Data Mesh Principles and Logical Architecture." 2019.
• Deloitte Insights. "Data Mesh and the Future of Decentralized Data." 2022.
• AWS Data Analytics Blog. "Why Data Projects Fail." 2023.
• McKinsey. "The Data Driven Enterprise of 2025." 2021.