What is a data product?
A data product is a self-contained, governed asset that packages trusted, fit-for-purpose data to answer a specific business question or power a decision — with a clear owner, SLAs, documented semantics, lineage and policy, all behind a published interface.
In short: not a table. Not a dashboard. A product — built, owned, versioned and consumed.
Raw data vs. data products
The value isn't in the ingredients alone — it's in turning trusted ingredients into a packaged product that people can safely and confidently consume.
Like cocoa, sugar, milk, cocoa butter, lecithin and vanilla on a supermarket shelf. Useful in isolation — but the customer has to know what each ingredient is, how they relate, whether they're good quality, and how to combine them correctly. The burden falls entirely on the consumer.
Like a finished, branded chocolate bar. Packaged for purpose, quality assured, versioned, labelled and governed — with clear ownership, usage guidance, business definitions and full lineage back to the source ingredients. Ready to consume with confidence.
From raw beans to a trusted bar
Raw ingredients are useful on their own, but a trusted product is packaged, governed, labelled, versioned, owned and ready to consume. Data is the same.
Cacao beans on the farm. Sugar cane in the field. Milk in the dairy. Vanilla beans on the vine. Cocoa butter and lecithin extracted from their own sources. All raw, all in different places.
CRM, ERP, billing, IoT, spreadsheets, third-party feeds. All raw, all siloed, each owned by a different team in a different system.
Beans fermented, roasted and refined. Milk is pasteurised and vanilla prepared. Each ingredient becomes usable, but they still exist separately.
Each source is cleaned, modelled and quality checked. Useful on its own, but not yet combined into a trusted business asset.
Cocoa, sugar, milk, cocoa butter, lecithin and vanilla — combined in the right ratios, batch-tested, labelled, branded. A bar people trust.
Multiple processed sources fused, governed, owned, versioned and published as a data product — ready to drive a decision.
The real picture only emerges when every ingredient travels its own path and then combines under governance — that's where raw data finally becomes a trusted data product.
The ingredients of a trusted data product
Raw source ingredients — each from a different place, unprocessed, unvalidated and without context until combined.
Data quality checks and validation rules applied at source.
Lineage and metadata — where the data came from and how it was transformed.
Data product ownership and trust — a named, accountable team behind the product.
Business-friendly interface, documentation, and discoverable contracts.
Freshness guarantees and SLA commitments for data consumers.
Versioning — so consumers know exactly which release they're using.
Business definitions and usage context — what this data means and how to use it.
Access controls, sensitivity classifications, and compliance rules.
A governed data product is the finished chocolate bar. A fused or AI-ready data product is the chocolate dessert — combining multiple bars and ingredients into something even more powerful, built on a foundation of trusted, reusable components.
From "where is the data?" to "what decision does this serve?"
Traditional data work starts with sources. Data products start with the business outcome and work back. The market is moving from data-driven to decision-driven — and that is exactly where Latttice operates.
"Give me everything you have on customers." Ship the warehouse and hope the right answer falls out.
"I need to decide which customers to retain this quarter." A data product packages exactly that — trusted, governed, ready to use.
What makes data a product
Industry consensus converges on eight attributes. Miss any one and it's a dataset, not a product.
Has a stable, unique address so any system, person or agent can reliably find and call it.
Available through governed interfaces — APIs, SQL, files — with the right permissions, not buried in a warehouse.
Tied to a real business outcome. If no one would pay for it, it isn't a product.
Published in a marketplace so consumers can find it, understand it and request access.
Documented in business language — definitions, owners, freshness, examples — not raw column names.
Access, masking and audit are policy-as-code, enforced at runtime, not bolted on after.
Standard formats, contracts and identifiers so products compose cleanly across domains.
Quality, lineage and SLAs are observable and certified — consumers can see the trust score.
Foundational. Fused. Analytical.
Most enterprise portfolios mix all three — composed and re-used, not rebuilt for every project.
Canonical entities the whole enterprise depends on — Customer, Asset, Employee, Product, Location.
Multiple sources combined into a governed domain view — e.g. a 360° Customer or an Operations product.
Metrics, scores, features and model outputs that directly inform a decision or action.
Latttice, the Data Product Workbench
Latttice is the workbench teams use to design, build, govern and publish data products — and the marketplace where the business, applications and AI consume them. The 8 tenets aren't a checklist you bolt on at the end; they're how every product is built from day one.
Compose products from sources with AI-assisted modeling and contracts.
Policy-as-code, lineage and trust scoring enforced at runtime.
Versioned, addressable products with SLAs, owners and discoverable metadata.
A governed marketplace for people, applications and AI — one source of truth.
The shift is from data-driven to decision-driven — and that's exactly where Latttice operates.
See data products in your environment
Take the short executive assessment below to see where your business stands.
Where is your business on the path from business-blocked to decision-ready & AI-ready?
Trusted data drives decisions — and AI can't be achieved without it. Ten quick, thought-provoking questions to see where your organization stands today.
Data execs are using this assessment to start honest internal conversations — about why years of data transformation spend still hasn't brought data closer to the business, and what it will take to change that before the next wave of AI investment.
Privacy and Data Use Notice: This assessment does not ask for or collect personal information. Data Tiles may collect anonymous response data points to better understand industry needs, share aggregated insights with our community, and guide the future direction of our products. We listen to what the industry needs and aim to build tools that address those needs.
- 1
Does your business currently treat data as a product — packaged, reusable, governed assets the business relies on?
Not dashboards or pipelines built one-off. Reusable products with an owner, an SLA, and known consumers.
- 2
Who builds the data products your business uses?
Who actually owns and ships them — not who consumes them.
No sign-up. No personal details. Just 8 more quick questions to see where your organization stands.
An executive briefing to take into the room.
A short, eight-page PDF written for executives — a plain-language definition, the shift from data projects to data products, the eight tenets, and a printable worksheet to run the assessment with your own leadership team. No form. No gate.
- Definition & the chocolate-bar analogy
- Data projects vs. data products
- The eight tenets, on one page
- Printable leadership-team worksheet
8 pages · Free to share inside your organization.
Want to discuss your results or your data product strategy?
If you'd like to explore your assessment results, discuss your data product strategy, or understand how trusted data products can accelerate AI readiness, reach out to John Goode, Chief Revenue Officer. We're happy to have an open, practical conversation about where your organisation is today and where it wants to go next.
Please do not include sensitive, confidential, personal, customer, financial, or regulated information in your email. Your details will only be used to respond to your inquiry.