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Data Tiles · Lili Marsh

Don't Let Data Be Your AI Blocker

Break the cycle. Own your data. Start your AI journey now with Latttice by Data Tiles.

Editorial photo of a business team reviewing data and AI dashboards in a modern office
The Harsh Reality

Years Before You Can Even Start

Despite all the AI hype, most enterprises are stuck waiting, sometimes for years, before they can even access their own data, let alone innovate.

Only 48%
of digital initiatives deliver on business outcomes, according to Gartner. Many fail not due to lack of ambition, but because foundational issues, like data access, remain unresolved.
85% Fail
of big data and analytics projects fall short. These aren't fringe cases, they're systemic signals that the traditional model is broken.
87% Never Launch
of data science projects never reach production. The promise of AI often fizzles out before it ever reaches business impact.
Sketch infographic of three hand-drawn circles filled with amber watercolor to varying levels, representing the progressive fall-off from digital ambition to AI delivery
Fig 1. Three statistics, one pattern — ambition outpacing access. Sources: Gartner; McKinsey.

At Data Tiles, we see this firsthand. Clients come to us after spending millions on cloud migrations and data platforms, only to realize they're still waiting for tangible outcomes. The frustration is real.

We've written extensively about this in The Unstructured Data Bottleneck, where we show how GenAI and Latttice are turning inaccessible documents, PDFs, and raw data into governed data products in minutes.

"For too long, unstructured data sources like customer feedback forms, supplier PDFs, or embedded tables in slide decks have remained dark to analysis. With GenAI, we've finally cracked the bottleneck, and Latttice makes it repeatable and governed."

The Data Prep Problem

80% of Time Wasted Before Value Begins

80% of time in analytics is wasted on data prep, not value creation.

Over 30% of data professionals say data prep is their biggest time sink, yet it's rarely solved by cloud migrations or traditional platforms.

Stop Waiting

Migrations and IT Backlogs Are the Real Bottleneck

The standard data playbook still recommends:

Sketch infographic of a circular cycle diagram with four hand-drawn icons — a warehouse, a cloud, an hourglass, and a question mark — looping back into each other
Fig 2. The standard playbook — a loop of centralization, migration, waiting, and hope.
  1. 01Centralize Everything. Lifting everything into a central data lake or warehouse.
  2. 02Migrate to Cloud. Embarking on a costly multi-year cloud migration.
  3. 03Wait for IT. Waiting for IT teams to build and prioritize data pipelines.
  4. 04Hope for Results. Hoping that usable, governed, AI-ready data appears at the end.

But that model is slow, expensive, and often demoralizing.

In our blog Data Mesh Is the Future, What's Stopping Enterprises from Adopting It Successfully?, we explore how outdated centralization strategies and technical dependencies kill transformation momentum.

"Many organizations claim they want to decentralize, but still hold onto centralized control structures that require technical handoffs at every step. That's not Data Mesh. That's just outsourcing the delay."

Even with the best intentions, many organizations fall into the same pattern, waiting on IT, waiting on migrations, waiting on results.

And as Jessie Moelzer warned in Noise, Confusion and the Reality of Data Mesh, many so-called modern platforms are just repackaged consulting-heavy models with no real empowerment for business teams.

"If your 'self-service data mesh' depends on 12 engineers building APIs and data pipelines just so a business team can ask a question, you don't have a mesh, you have a bottleneck wearing a different hat."

The AI Readiness Gap

Enthusiasm ≠ Execution

Employees are already using AI tools, 90% of them, but only 13% of organizations are ready for AI adoption. This mismatch isn't about intent. It's about capability.

Sketch infographic split by a vertical line — on the left a dense crowd of stick figures with chat speech bubbles, on the right a single small office building sitting on an amber wash
Fig 3. Everyone's already using AI. Almost no organization is actually ready for it. Source: McKinsey Global Survey on AI.

AI can't thrive in an environment where data remains inaccessible, ungoverned, locked behind engineering bottlenecks, and lacking context. Without governed access, experimentation stays shadowed and unsupported.

At Data Tiles, we believe there is a disconnect between AI hype and operational reality.

"AI isn't magic, it's fueled by clean, contextual, accessible data. The disconnect isn't due to bad models; it's due to inaccessible foundations." — Cameron Price.

Organizations want AI, but they're still solving yesterday's data problems with a continual revolving door of tools.

Our Position

Don't Let Data Be Your AI Blocker

We created Latttice to solve this dilemma, bridging the gap between business ambition and technical execution.

Data + AI

Reimagined Together with Latttice

With Latttice, you don't have to wait. You can:

Sketch infographic of four hand-drawn icons in a row connected by arrows — a magnifying glass over data tiles, a stopwatch, a smiling stick figure with a lightbulb, and a rocket taking off
Fig 4. Four moves to flip the equation — access, build, empower, launch.
  • Access data in-place — no migration required.
  • Build governed, trusted, and secure data products in minutes, not months.
  • Empower business users — no code, no backlogs.
  • Launch AI initiatives today, not three years from now.

Instead of data holding up AI, AI drives better data practices, now.

The Cost of Waiting

$12.9M Per Enterprise, Every Year

$12.9M
Annual cost per enterprise. Gartner reports that poor data quality costs the average enterprise $12.9 million per year.
Now
The world has changed. We're in the era of data products — governed, reusable assets that drive decisions, with AI as co-pilot, not a backroom experiment.

That's a cost of inaction, yet most firms still treat data readiness as a years-long engineering project.

The Call

You've Waited Long Enough

Still waiting to "finish" your warehouse or cloud migration before launching AI?

You're already behind.

Break the cycle. Own your data. Start your AI journey now.

Latttice by Data Tiles: giving every team the power to have a conversation with their data.

Join a Data Conversation

Lili Marsh

References

External Reference List

  1. Gartner via Salesforce Ben, Tech Monitor, IT Pro — "Only 48% of digital initiatives meet intended business outcomes"
  2. Gartner via Datamine.com — "85% of big data projects fail to deliver"
  3. Gartner via Salesforce Ben & Designingforanalytics.com — "87% of data science projects never reach production"
  4. McKinsey & Company — "80% of analytics time is spent on data prep"
  5. TDWI via McKinsey & Salesforce Ben — "Over 30% of data pros say data prep is a major challenge"
  6. McKinsey Global Survey — "90% of employees use GenAI, but only 13% of organizations are ready"
  7. Gartner via Forbes & Mannara-Tech — "Poor data quality costs $12.9M annually"