AI Doesn't Need More Data. It Needs the Right Context.
Solving the data plane for enterprise AI.
Your enterprise has already invested in data. AI creates a new challenge: turning that data into trusted, governed context that can be used at the point of decision, without replacing the technology you already own.
The enterprise has invested in data. Now AI needs to use it.
01 / Existing investment
The foundation remains
Organizations have invested in cloud platforms, data warehouses, governance, catalogs, ERP, CRM, BI and analytics. Those investments remain important.
02 / New requirement
Trust at the point of use
AI needs the right meaning, context, governance, permissions and evidence when a decision needs to be made.
03 / Boardroom implication
The operating gap
Most of that data was never designed to be understood by AI. The gap sits between what you own and the decisions you need to make.
The conclusion
The question is no longer whether to invest in AI. It is what that investment will return.
The board will ask what it returns
You've invested in data. You're investing in AI. What will it return?
This is a business performance conversation, not an infrastructure conversation. Every AI investment will be judged in four places.
01
Revenue
Faster, better-informed decisions on pricing, customers and growth.
02
Cost
Less time spent assembling and reconciling the same answer again and again.
03
Risk
Evidence, permissions and policy that can be shown when AI supports a decision.
04
Accountability
Clear ownership of the data, the definitions and the decisions that rely on them.
The Decision-Driven approach
Start with the decision.
Pick one important business decision. What context does it require? What evidence can be trusted? What governance needs to apply? Then work backwards to the data.
Not a data project. A decision.
D
Start here
Decision
01
What context does this decision require?
02
What evidence can be trusted?
03
What governance needs to apply?
∎
Arrive here
Data
Work backwards↓
01
Start with one decision
Choose the decision that matters most to revenue, cost or risk.
02
Define its context and evidence
Agree what it means, what can be trusted and what governance applies.
03
Work backwards to the data
Use the data you already own, governed once and reused everywhere.
Business knows the decision.
Data knows the evidence.
Decision-Driven outcomes
The destination isn't more data. It isn't more AI. It's better decisions.
01
Faster decisions
Trusted data at the point of decision.
02
Lower risk
Governance and appropriate access carried into use.
03
Higher performance
Business teams can create the data products they need.
04
More effective AI
AI receives trusted business context rather than disconnected data.
05
Greater return on existing investment
Get more from the data and technology you already own. No rip and replace.
Modernize the operating model, not necessarily the entire estate.
A question for your leadership team
Q
If your AI needed the information behind one of your organization's most important decisions right now, could you prove that the data was trusted, current, governed and appropriate for that decision?
If the answer takes multiple systems, multiple teams and days of work, most enterprises don't have an AI problem. They have a data permafrost.
The boardroom conclusion
That's the data permafrost.
Trusted data exists inside the enterprise, but much of its value is frozen beneath fragmentation, technical complexity, lost context and disconnected governance. Decisions happen above. Evidence remains locked below.
Decisions above
PricingCustomerSupplyRisk
Fragmentation
Technical complexity
Lost context
Disconnected governance
Evidence locked below · the value already exists
Data plane ↑
01–03
Why the permafrost forms
01
Fragmentation
Every system froze its own version of the truth. Revenue, cost and risk each get reported three different ways, and no one can prove which number a director should trust.
02
Lost meaning
Numbers travel without their definitions. Two teams quote different margins from the same data, and every meeting starts by arguing over which figure is real.
03
Broken control
No one can prove who used what, for which decision, under which policy. When the board or a regulator asks, the evidence takes days to assemble, if it exists at all.
The data plane is the layer that lets enterprise data be understood and safely used by people, BI, applications, workflows, AI and agents. Your data, cloud, governance and analytics investments stay in place. They simply return more.
Existing enterprise systems flow through the Latttice trusted data plane into people, BI, applications, AI and agents, with governance spanning every authorized experience.
Context changes everything
Giving AI access to data is not the same as giving AI data it can trust.
01
What does this data mean?
02
Which definition should be trusted?
03
Where did it come from?
04
Who or what is allowed to use it?
05
What policies apply?
06
Is it appropriate for this decision?
AI needs data with business meaning, context, governance and evidence attached.
See what this means in practice in How Does AI Understand Enterprise Data?, including how technically valid SQL can still produce the wrong business answer.
Governance has to travel
Governance cannot stop when the data leaves the platform.
As enterprise data moves into BI, applications, AI and autonomous agents, governance needs to remain active at the point of use.
Governance applies when data is created and again when it is consumed, including access, permissions and business context.
Govern once. Carry trust into every authorized experience.
Meaning, ownership, policy and permissions attach to the product.
↓
At consumption
Trust travels into BI, applications, AI and agents.
Create once. Govern once. Use everywhere.
The same trusted, governed business context supports people, BI, applications, workflows, AI and agents. Solve it once for AI, and every existing data investment is worth more.
Revenue, cost, risk and accountability. Ask how trusted data at the point of decision shows up on your P&L. Answers reflect the Data Tiles point of view.
An outcome conversation, not a technology pitch
Ask in the language you report in: revenue, cost, risk and confidence in the numbers you take to the board. Keep company-confidential information out of the conversation.
04How Data Tiles enables it
Latttice, the Data Product Workbench
The practical way to work backwards from the decision.
Latttice is the zero-code, AI-powered Data Product Workbench that enables business and data teams to create trusted, governed data products using their existing enterprise data environment. Technical complexity stays under the hood. Governance does not.
Connect→Create→Govern→Publish→Use
01
The fragmentation ends
One governed data product, created once with business and data together, becomes the single trusted source that BI, people, AI and agents all read from.
02
Meaning is restored
Every product carries its definitions, evidence and lineage with it, so revenue, cost and risk finally mean the same thing in every room.
03
Control is proven
Permissions, policy and an audit trail are built in at creation, so you can prove who used what, for which decision, in seconds instead of days.
latttice.com · Data Product Workbench
Latttice · live product capture
01Context02Governance03Evidence04Access
Latttice works with the customer's existing technology rather than replacing it.
Build governed data products in minutes, not months.
Build once. Govern once. Reuse from BI to AI.
Active governance at creation and consumption.
Works with your existing data and technology stack.
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From AI ambition to business outcome
Bring us one important decision.
Show us one decision that matters to your business. We'll work backwards with you to identify the context, evidence and governance it requires, and explore how your existing data can support it.
