Stop building
data products
in the data team.
Empower business teams to build context-rich, governed data products with the data team, so people and responsible AI can make better decisions without waiting in an engineering queue.
Cameron Price
Founder & CEO, Data Tiles
The decision became the last mile.
80%
of people cannot access the data they need
49%
say the data available is not fit for purpose
- Sources
- Data team
- Product
- Report
- Sarah
- Decision
We designed from the data forward. Every translation, hand-off and delivery layer increased the distance between the business problem and the information needed to resolve it.
The problem is not the data team. It is a supply chain where the decision sits at the end instead of shaping the work from the start.
“The problem isn’t that we don’t have enough data. The problem is that the data is too far from the decision.”
Sarah doesn’t need more data.
She needs context.
Sarah is a claims assessor. To answer one question, “Should this claim be investigated?”, she looks in seven places and asks three people. The evidence exists, but it does not arrive together when she has to act.
7
Places
3
People
1
Decision
The context Sarah needs
01
What happened?
02
What happened before?
03
What looks unusual?
04
What does policy say?
05
What should happen next?
AI changes the decision supply chain.
An AI agent cannot spend three days asking six teams where the right information lives. It needs trusted context, evidence and rules at the moment of use.
Then
Data → Report → Human → Decision
A report informed a person, who interpreted it and decided.
Now
Data + context + rules → Human + AI → Recommend → Decide → Act
AI can accelerate recommendations. Accountable people still make or govern consequential decisions.
AI doesn’t remove the need for governance. It makes it essential.
Start with the decision.
Then build backwards.
01
What decision?
Name the decision, the person or system making it, and why it matters.
02
What context?
Identify what must be known and understood to decide responsibly.
03
What evidence?
Find the data, definitions, history and rules that support the decision.
04
What outcome?
Define the result to improve so value can be measured, not assumed.
A strong candidate is a repeated, consequential decision where context is fragmented, hand-offs are high and the outcome can be measured.
A data product is not a table.
“A decision product is the context, evidence and rules needed to make a decision.”
01
Context
Everything relevant to the decision, assembled once instead of reconstructed each time.
02
Evidence
The trusted facts, history and signals that show what happened and what looks unusual.
03
Rules
Meaning, policy, access and controls that make the product safe to use and explain.
Move from a centralized queue to a decision-led partnership.
Before · centralized
- Sources
- Data team
- Product
- Report
After · decision-led
- Sarah's team
- Governed product
- Decision
The business leads the purpose and workflow. The data team enables source access, reusable foundations, governance guardrails, standards and shared components.
What this does not mean
- It does not eliminate or diminish the data team.
- It does not bypass governance.
- It does not ask business teams to become data engineers.
Design together around the decision
Business understands the decision. Data understands the evidence and foundations. AI and application teams understand how the context will be used.
Want to see where each model fits?
Engineer-built vs business-built data productsGoverned freedom, built on strong foundations.
01
Access
ABAC, FGA and PBAC put the right context in the right hands under the right conditions.
02
Meaning
Semantics and metadata translate source data into shared business language.
03
Fusion
Relevant data and context are assembled around the decision, not around a source system.
04
Reusable foundations
Contracts, shared components and engineer-built services remain reusable.
05
Active governance
Lineage, policy and audit travel with the product from creation through use.
Explore active governance →06
Many experiences
The same governed product can serve a human, dashboard, app, workflow or agent.
Value shows up in the work.
8 hours
5 minutes
Preparation
7
1
Places to look
6
1
Hand-offs
68%
92%
Consistency
16,000
Engineering hours redirected to higher-value platform work
These figures describe the decision example in Cameron’s presentation. They show the shape of change in that context, not guaranteed results. Outcomes depend on the decision, data landscape and operating model.
One governed context. Many authorized decision experiences.
Governance boundary
Decision product
Context · Evidence · Rules
- Human
- Application
- Dashboard / BI
- AI
- Agent
- Workflow
This is what we built Latttice to enable.
Latttice is the governed Data Product Workbench where business and data teams compose decision-ready products on the data platforms they already own, then publish them for people, BI, applications and AI.
- Connect
- Create
- Govern
- Publish
- Use
We don’t replace your data platform. We complete it.
Continue exploring
Download the executive brief.
A six-page summary aligned to Cameron’s keynote: the evidence, Sarah’s decision, the decision-led operating model, governed enablement, measurable impact and the role of Latttice.
Scan to open the full point of view, or download the brief and QR for your team.
“The value of data isn’t the data.
It’s the decision it helps someone make.”
Are you ready to have a data conversation?
Bring your decisions, your data and your AI ambitions to the table. We’ll meet you where you are.