From Data Access to Decisions: Why Data Visualization Can't Solve Your Data Problems
Empowering business teams to create governed, reusable data products without code. When BI tools connect via API key, dashboards finally illuminate trusted insights.
The Problem with "Fix It in the Dashboard"
Too often, businesses are told to "fix it in the dashboard." But solving data access inside visualization tools leads to fragile, bespoke work that breaks trust. Latttice changes the story by solving access before visualization, empowering business teams to create governed, reusable data products, share them in the Pin Board marketplace, and query them conversationally through Latttice GPT. When BI tools connect via API key, dashboards finally do what they're meant to: illuminate trusted insights. Zero-code needed.
Years of Seeing the Same Story
I've spent years working in consulting, often caught between business teams and data engineers. The same cycle played out again and again. Projects started with big promises:
But once the contracts were signed, reality looked very different. Work was handed to teams with little experience or context. Costs blew out. Deadlines slipped. And the one thing that mattered most, access to usable data, remained unsolved.
- "This will be affordable and delivered by quality teams."
- "This time it will be a success."
By the time dashboards were delivered, they rarely looked like what the business expected. Instead, visualization tools had been forced to carry the burden of broken access and weak governance. The result? Bespoke dashboards that couldn't be reused, fragile outputs that broke as soon as data changed, and business teams left feeling let down after investing time, energy, and money.
Sadly, this story isn't unique. Gartner's 2024 Data & Analytics Priorities Survey highlights that "simplifying data access" remains a top challenge for leaders, even after decades of investment in analytics platforms. And Gartner's peer research shows that business satisfaction drops sharply when governance frameworks don't align with business needs.
The Cycle That Always Repeats
Here's how I saw projects unfold time after time:
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The project begins with energy and excitement.
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Engineers spend months stitching datasets together or eventually telling the Business teams they can't have the datasets they need because the data is "too hard" to access or join together.
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Resulting in deadlines slipping because access was never solved at the start.
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Costs grow far beyond the original budget.
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By the time dashboards are delivered, expectations have shifted, trust has eroded, and adoption is low.
This is because the BI tools in the end usually cop the full brunt of the blame, as to why the numbers are no longer matching what the business expects, when in fact the true failure happened at the very beginning with the deliverable of "access to the data" never being truly met.
Gartner
Reports that under-adoption of BI tools is common when expectations aren't met.
McKinsey
Has long observed that 70% of large-scale transformation projects fail to meet their goals.
Forrester
Reports fewer than 30% of employees in most enterprises actively use BI tools, citing low trust and accessibility (Forrester, BI Tool Adoption Stagnation).
BARC Germany adds that treating data as governed, reusable products is the only way to break free from fragile, one-off dashboards (BARC, Data Products as a Foundation for Analytics Maturity).
When Data Contracts Created More Barriers
One of the industry's big answers to the trust gap has been data contracts. They were meant to align business and data teams by defining rules, responsibilities, and expectations upfront.
But in practice, they rarely worked that way. They were written by engineers, for engineers, in languages like YAML or cURL that the business couldn't interpret. I've been in those meetings where contracts were presented as the solution, while business leaders quietly wondered what they were even signing off on. Later, when the project went off track, the response was too often: "But you approved this."
Even recent studies of data mesh adoption highlight this same problem: governance mechanisms and contracts created without business inclusion break down quickly (Bode et al., Towards Avoiding the Data Mess: Industry Insights from Data Mesh Implementations, arXiv, 2023).
The truth is, contracts are rigid at the start but almost always adjusted as projects run into delays, shifting scope, and misaligned outcomes. For business teams, they quickly feel like wasted effort, another artifact to justify cost, rather than something that delivers value.
A New Approach: Trust Baked Into Design
And it raises a real question: if business domains are empowered to build their own data products, what is the need for engineers to design contracts at all? Perhaps they become a thing of the past. Instead, with platforms like Latttice, trust is baked into the design. Computational governance, access rules, and sensitivity controls are embedded directly in every data product.
Technical Metadata
Engineers rely on precise technical tags and specifications
Business Terminology
Domain owners understand clear business language
Shared Foundation
Both perspectives validated, both can trust the same data
Even more importantly, Latttice gives every data product both the technical metadata tags engineers rely on, and the business terminology domain owners understand. Both parties are acknowledged. Both perspectives are validated. And both can finally trust that they are working from the same foundation.
Why Visualization Isn't the Fix
Dashboards are incredibly valuable for presenting insights, but they cannot solve upstream access issues. When problems are pushed downstream, forcing visualization tools to cover for gaps leads to:
One-off outputs tied to narrow use cases.
Fragile dashboards that break when data changes.
Inconsistent answers that erode confidence between teams.
Blame of the BI tool and eventually abandonment.
Analysts echo this reality:
"By 2025, 80% of organizations seeking to scale digital business will fail because they do not take a modern approach to data and analytics governance."— Gartner
"Why is data-driven decision making so hard? Because access, trust, and usability remain the biggest roadblocks."— Harvard Business Review
Cutting Through the Noise
Recently, the data space has grown noisy with talk of "data products." Loud, ego-driven voices often say: "Who said it was hard to build a data product?" Then they immediately retreat into technical jargon, APIs, cURL commands, GitHub workflows, that actively excludes business teams.
What begins as a dismissal of difficulty becomes proof of it. The effect is fear. Business teams conclude data products are out of reach, reserved only for engineers. Perhaps that's the point: simplifying the process would make some of these voices redundant.
As Gartner's peer insights community notes: "Talking in jargon or technical terms can put decision makers off and make justification challenging when there is a disparity over language." (Gartner Peer Community, cited in Opendatasoft, 2023).
With Latttice, that fear is redundant. Business teams are relieved of the burden of technology. They can create data products in minutes, without coding, without jargon, without interpreters. Data engineers, freed from building business-facing products they lack context for, can focus on the first mile: pipelines, frameworks, infrastructure. Both sides are respected.
Why Latttice Is Different
Latttice was created to break this cycle. It grew out of years of conversations with business leaders, the true owners of the data, who know what it means, and how it should be used. And with the data engineers I've admired throughout my career, the ones who worked closely with the business to understand needs and outcomes.
These engineers were collaborative. They focused on the first mile: pipelines, governance frameworks, and infrastructure. But they also understood that the business needed a platform for the final mile, one that would empower them to build their own trusted data products in plain language, without feeling excluded by technical jargon.
That's why every Latttice data product includes both the technical metadata engineers need and the business descriptors domain owners understand. Both perspectives are visible. Both are validated. And both sides can trust that they're working from the same foundation. It's a bridge between business and data teams, creating a shared language that fosters trust across the organization.
Unlike many engineer-led platforms, Latttice is designed for business ownership of data products. Domain owners know their data better than anyone. Yet too often, the conversation about data products is dominated by technical jargon, APIs, cURLs, GitHub workflows, language that excludes the very people the data is meant to serve. That's not collaboration. It's not inclusion.
Latttice flips that narrative.
Every data product in Latttice comes with computational governance built in, RBAC, ABAC, FGA, and sensitivity tagging, so products are trustworthy by design. They're ready to connect into BI tools without forcing those tools to patch over gaps. No need for dashboards to shoulder the burden of governance.
As someone who isn't deeply technical, I became Data Tiles' first test case. If I could build a data product within minutes, not only creating it but also setting who could access it, what needed to be masked, and what didn't, then anyone could. That's when I knew Latttice was different. It was designed for business teams, to give them the confidence and capability to create, use, and share governed data products.
How Latttice Empowers Business Teams
With Latttice, business teams can:
Step 1: Access Data Instantly
No tickets. No delays. Business users can pull in data from CRMs, finance systems, or other tools instantly using plain language. This reflects Gartner's call to modernize data management and empower self-service (Gartner, Modernize Data Management to Drive Value).
Step 2: Build Trusted Data Products
Latttice enables governed, reusable data products. Sources can be fused, rules applied, and results shared. This reflects data mesh and data fabric principles: treat data as a product with ownership and usability (Gartner, Data Mesh and Data Fabric Trends).
Step 3: Converse with Your Data in Latttice GPT
Business teams want answers, not code. With Latttice GPT in the ChatGPT Marketplace, they can simply ask:
- "What's driving churn this quarter?"
- "Show me sales by region compared to last year."
Instant Answers in Plain Language
Responses come back instantly, in plain language, with visuals attached, aligning with Gartner's trend toward decision-centric enterprises (Gartner, Top Data & Analytics Trends 2022–2024).
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Step 4: Extend Into BI Tools
Executives still need dashboards. Latttice enables BI tools to connect directly via API key. Dashboards are powered by trusted, governed data, and adoption sticks because the foundation is strong.
Why I'm Committed
I've seen too many projects fail, not because people didn't work hard, but because the business was excluded from ownership of the data. Let down by fragile dashboards, contracts no one understood, projects where trust eroded before delivery, but the biggest let down was usually lack of access to the data needed. I've seen business teams bewildered by jargon, expected to sign off on agreements they couldn't interpret, and left carrying the costs when outcomes fell short.
That's why Latttice matters so much to me. It's not another layer of complexity; it's a bridge between the business and data teams, built for collaboration and trust.
Breaking the Cycle
With Latttice, we finally break the cycle:
Access is immediate.
Data products are governed, accessible and reusable.
Insights come through natural conversation.
Dashboards are powered by trust, not broken promises.
Try It Yourself
Try it yourself: Explore Latttice GPT on the ChatGPT Marketplace or connect your BI tools directly to Latttice via an API key to see how quickly you can go from data access to trusted decisions. Zero-code needed.
Because business teams deserve more than dashboards that disappoint. They deserve inclusion, collaboration, and tools built with their success in mind.
Join a data conversation,
Jessie Moelzer
References
- Gartner, Data & Analytics Priorities and Challenges – 2024
- Gartner Peer Community, Data Governance Frameworks and Challenges
- Opendatasoft, Self-Service BI Adoption & Disappointment (Summarizing Gartner)
- McKinsey, The Inconvenient Truth About Change Management, 2015
- Gartner, predicts 2025: Data and Analytics Governance Is Critical for Scaling Digital Business, 2023
- Forrester, Augment Decision-Making with BI And Analytics, 2022
- BARC, Data Products: A Paradigm Shift in Data Management, 2024
- Harvard Business Review, Why Is Data-Driven Decision Making So Hard? 2021
- Gartner, Modernize Data Management to Drive Value, 2023
- Gartner, Data Mesh & Data Fabric: Emerging Data Architectures, 2023
- Gartner, Top Data & Analytics Trends – Decision-Centric Enterprise, 2023
- arXiv.org, Towards Avoiding the Data Mess: Insights from Data Mesh Implementations, 2023