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Data as an Experience: Why the Future of Analytics Lies in Feeling, Not Just Function
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.
The Question That Changes Everything
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.
"What does using data actually feel like for the business?"
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.
Business Teams Have Never Truly Owned Their Data Experience
For decades the data experience was designed around business users, not with them. Industry research has shown this repeatedly.
Gartner
Over 80 percent of data and analytics initiatives fail to deliver outcomes because business users do not adopt the tools.
Forrester
Warns against "data product theater" where products are built without business co-creation and go unused as a result.
BARC
Finds that inconsistent definitions and unclear meaning are among the top reasons analytics efforts break down.
Deloitte and ThoughtWorks
Report that many Data Mesh pilots collapse because organizations implement it as an engineering program instead of a business transformation.
AWS and McKinsey
Both emphasize that most data investments fail at the human layer, not the technical layer.
The conclusion is consistent.
Organizations invested heavily in data products and platforms, but never designed the experience that makes them usable.
Where Data Efforts Fell Short for the Business
When I speak with business teams, their reactions are emotional rather than technical.
  • Confusion
  • Frustration
  • Concern about "doing it wrong"
  • Long wait times for reports
  • Unclear or conflicting definitions
  • A quick return to Excel because it simply feels safer
These reactions do not reflect a lack of skill. They reflect a lack of experience design.
Dashboards often feel like assignments. Analytics tools feel like obstacle courses. Data lakes feel like places where information disappears. Data Mesh often feels theoretical rather than approachable.
As Cameron writes, meaning does not matter until a human can actually experience it.
Why Experience Matters Now, and How AI Changes Everything
This is the first time in decades that technology gives us a fundamentally new interaction model.
Generative AI allows the data experience to become:
Conversational
Contextual
Guided
Explanatory
Human
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.
How Latttice Transforms Data From a Product Into an Experience
Latttice was designed to solve exactly this gap. Not to recreate the past, but to redesign the experience entirely.
Latttice gives business users:
  • Direct access to governed data products with no unnecessary movement
  • A conversational interface that feels natural and intuitive
  • Transparent definitions and shared meaning
  • AI guidance that supports curiosity
  • A sense of ownership over their data experience
When organizations adopt Latttice, the shift is immediate.
Decisions Accelerate
Teams no longer wait weeks for answers.
Trust Improves
Definitions are visible and shared. Discussions become action oriented.
Curiosity Grows
When the experience feels natural, questions double rather than disappear.
AI Becomes Possible
You cannot achieve good AI outcomes with a broken experience. Latttice removes that barrier.
Business Ownership Returns
This is the center of Cameron's argument and the heart of our mission.
For the first time, business users do not just consume data. They experience it.
Experience as the New Semantic Layer
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.
Experience is the new semantic layer.
An Invitation to Rethink What Is Possible
If your dashboards are not adopted
If your Data Mesh pilot stalled
If your tools feel overwhelming
If your teams still rely on Excel
If your investment in analytics has not delivered outcomes

It is not a failure of intelligence or capability. It is a failure of experience. And experience can always be redesigned.
The future of data is human. The future of analytics is conversational. For the first time every business can have a data experience that works.
If you want to experience data differently, intuitively, confidently, and with real clarity,
Join a data conversation,
Lili Marsh.
References
  • 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.