The language of trusted data and decisions.

The rules determining who can access data, when and under what conditions.

Access policies protect the business from regulatory, reputational and competitive risk. When policies live inside the data product, they are enforced consistently — regardless of who or what is asking.

The layer between governed data and the moments where data is consumed and acted on.

The activation layer is where governed data meets business reality — explored in Convo, organized in Pin Boards, consumed by AI agents and embedded in downstream workflows. It is what makes trusted data useful.

Governance applied at the point of creation and use — not after the fact.

Definitions, quality rules, access policies and lineage live inside the data product as it is built and consumed, so trust is visible to every consumer including AI agents. The opposite of remediation-style governance, which is slow and expensive.

AI systems capable of reasoning, planning and executing tasks with varying levels of autonomy.

Agentic AI moves AI from suggestion to action. That step requires far stronger governance, explainability and human-oversight models than report-style AI — because the consequences are real.

A software system capable of taking action toward a defined objective.

Agents are consumers of data products in their own right. They need the same trust signals, governance and decision context as a human decision-maker — and often more.

An environment for creating, governing and scaling AI agents on trusted data.

An AI factory replaces one-off pilots with a repeatable, governed way to build and operate AI agents — with governed prompts, trusted data products and clear ownership baked in from the start.

Applying policy, explainability and accountability to AI where decisions are actually made.

Governing the model in isolation is insufficient. Governance must travel with the AI into the decision moment — visible to the decision owner, recorded for audit, and enforceable in flight.

The pattern of AI pilots that work in isolation but never reach production.

Pilots rarely fail at the model. They stall on data trust, governance, ownership and workflow integration — the same problems trusted data products are designed to solve.

The state in which AI can produce decisions an executive will trust and act on.

AI readiness is not a model problem. It is a data product, governance and decision-ownership problem. Without trusted data products, AI agents amplify the same risks the business already has.

The gap between AI ambition and the trusted data, governance and decision readiness required.

Most organizations are further from AI value than their roadmaps suggest. The AI Readiness Gap is the distance between what leadership wants AI to do and the trusted data products, governance and decision ownership required to let it.

Guidance generated by AI to support a decision rather than replace it.

Recommendations succeed when they fit the decision workflow, carry trust signals, and respect the decision owner's accountability. The goal is better decisions, not autonomous ones.

AI should redesign decisions and workflows — not automate broken ones.

Pointing AI at a broken process creates faster broken outcomes. Real AI transformation rethinks the decisions, workflows and operating models so humans and agents work together toward better results.

Confidence that AI recommendations are accurate, explainable and governed.

Trust is the bottleneck for AI adoption at executive level. It is built through trusted data products underneath, governance around, and explainability in front of the model.

What data should represent in business terms and why it matters to a decision.

Technical accuracy is necessary but not sufficient. Business meaning is the shared understanding of what a number stands for, how it should be interpreted, and which decision it should inform — the layer where most data initiatives fail silently.

The measurable result a decision is intended to influence.

Outcomes — revenue, retention, risk, margin, time-to-serve — are what executives are accountable for. Data products and AI only matter to the extent they move outcomes through better decisions.

A data product whose accountable owner sits in the business, not the data team.

The business team whose decisions depend on the data product owns it. Data and platform teams enable, govern and operate — they do not own by default. Ownership in the business removes the handoff that creates rework and delay.

The shift from dashboards toward conversational interaction with trusted data.

Executives increasingly want answers, not charts. Chart-to-chat is the move from static visualization to conversational interfaces powered by trusted, governed data products — where every answer carries its lineage and trust signals with it.

A data product created from multiple trusted inputs while preserving governance.

Composition lets organizations assemble new data products from existing trusted ones — accelerating delivery without losing lineage, policy or ownership across the pieces being combined.

The conversational interface enabling interaction with trusted data products.

Convo turns trusted data products into a dialogue. Decision-makers ask, refine and act — without leaving the trust envelope of the data product underneath.

The three forms of waste created by duplicated, ungoverned data work.

Every time the same data is prepared, modelled or queried twice, the organization pays in cost, time and compute. Trusted, reusable data products are the antidote — work is done once, governed once and consumed many times.

Treating data as a managed product with ownership, governance and measurable value.

A mindset shift from projects to products. Projects end; products are owned, maintained, improved and retired. Data-as-a-product makes data accountable to the business outcomes it serves.

Data made available at the moment and place a business decision is being made.

Most data still arrives after the decision has been made. Putting data at the point of decision means delivering it inside the workflow, the conversation and the moment where action happens.

Visibility into where data originated, how it changed and how it is used.

Lineage is the audit trail of trust. Without it, neither executives nor AI agents can explain why a number is what it is — and explanation is a prerequisite for action.

A governed, owned, trusted unit of data built for a specific decision or outcome.

Not a dashboard, not a report, not a raw dataset. A data product has a named business owner, a defined purpose, applied governance, quality guarantees, and consumers — human and AI — who depend on it.

The layer connecting trusted data products to business users, applications and AI systems.

Activation is where data products meet decisions. The activation layer ensures every consumer — Pin Board, Convo, AI agent or downstream application — sees the same governed, trusted view.

The process of creating, governing, maintaining and retiring data products.

Products age. A clear lifecycle — propose, build, govern, publish, monitor, improve, retire — prevents the silent sprawl of orphaned datasets and keeps the marketplace trustworthy.

A searchable environment where trusted data products can be discovered and reused.

A marketplace turns data products into reusable assets. Every reuse multiplies value and reduces duplicate build cost — but only if marketplace entries carry trust signals at the point of discovery.

The roles, ownership, governance and lifecycle that make data products repeatable.

The cost, speed and trustworthiness of data products are determined by the operating model around them — not the platform underneath. Operating model beats tooling.

An environment for creating, governing and sharing trusted data products.

A workbench combines the modeling, governance and publishing capabilities a business team needs to deliver a trusted data product end-to-end — without handing off to a separate data engineering team for every change.

A recurring business choice that influences an outcome.

Decisions — not dashboards or models — are the unit of value in a decision-driven organization. Naming the decisions that matter is the first step toward improving them.

A competitive advantage created through consistently better decisions.

Organizations win not by having more data but by making better decisions, more often, with less effort. Decision advantage compounds — every improved decision improves the next.

The level of certainty a decision-maker has in a recommendation or action.

Confidence comes from visible trust signals — lineage, ownership, freshness, policy — not from prettier charts. Confidence is what turns information into action.

The person or system using information, data products or AI to make a decision.

Consumers can be executives, frontline operators or AI agents. The trust signals, controls and explanations a data product carries must be designed for the consumer who will actually rely on it.

The combination of people, processes, data products, governance and AI supporting decisions.

Decisions are never made in isolation. The ecosystem around a decision determines how fast, how confidently and how consistently it can be made.

The discipline of designing, supporting and improving the decisions that drive outcomes.

Decision intelligence treats the decision — not the dashboard or model — as the unit of value. It connects trusted data products, governance and AI to the moments where decisions are actually made, so improvement is repeatable.

The time required to move from question to decision.

Long decision latency is usually a data trust problem, not a data availability problem. Trusted data products and conversational interfaces compress latency without compromising governance.

The person accountable for the quality and outcome of a recurring decision.

Decision owners sit in the business, not in data or IT. Without a named owner, the data product, governance and AI built to support a decision have no customer — and no one to improve.

A single named owner accountable for the quality and outcome of a recurring decision.

Without decision ownership, the data product, AI agent and governance around it have no customer. Decision ownership sits with the business leader whose outcomes depend on it.

How well a decision was framed, informed and executed — independent of its outcome.

Good decisions can have bad outcomes; bad decisions can get lucky. Decision-driven organizations measure quality of process so they can improve over time, rather than only judging results after the fact.

The sequence of activities required to move from information to action.

Most decisions span people, systems and time. Mapping the workflow exposes where trust breaks down, where latency creeps in and where AI can add the most leverage.

AI transformation designed around the decisions that matter to the business.

Most AI programs start with technology and search for value. Decision-driven AI transformation starts with the decisions that move outcomes, then builds the trusted data products, governance and agents required to improve them.

A data strategy that starts with outcomes and decisions, then works back to data products.

Instead of inventorying data and hoping value emerges, a decision-driven data strategy names the decisions that matter, identifies their owners and works backward to the trusted data products required to support them.

An organization where strategy, data and AI are designed around the decisions that matter.

Decisions are named, owned, instrumented and improved over time. Data products and AI exist to serve those decisions — not the other way round. The opposite of report-driven or dashboard-driven cultures.

A data product designed and governed for a specific recurring decision.

Decision-ready means the data product knows the decision it serves, who owns that decision, and what trust signals the decision-maker needs. The standard for data products in a decision-driven organization.

The Data Tiles belief that data, governance and AI only create value when they improve decisions.

Every Data Tiles capability — trusted data products, active governance, Latttice, Lenz — exists to enable better decisions. Data that does not change a decision does not change an outcome.

Governance evidence automatically generated during creation and consumption.

Evidence by default replaces manual audit prep with a continuous stream of governance artefacts. When evidence is a byproduct of normal work, audits stop being events and become reports.

The ability to understand how an AI recommendation was produced.

Without explainability, an AI recommendation is a black box — and executives will not act on what they cannot defend. Explainability is a prerequisite for accountable AI, not an optional feature.

The first data product an organization builds under a product-led operating model.

The right first use case is valuable, measurable, achievable and reusable. The wrong first use case is the one with the loudest sponsor. The first use case sets the pattern every future product will follow.

The shift from organizing around data availability to organizing around decisions.

Data-driven organizations measure activity — dashboards built, datasets catalogued, pipelines shipped. Decision-driven organizations measure decisions improved. The shift changes the questions leaders ask, the investments they prioritize and the value they capture.

A trusted data product created by combining governed data from multiple sources.

Fused data products preserve lineage, access rules, quality signals and governance context across every input — so the composed result is itself trustworthy and reusable across decisions.

The capability that combines data from multiple governed sources into reusable data products.

Fusion is how Latttice turns scattered, governed sources into composed, trusted data products — preserving lineage and policy across every input so the output is itself trustworthy.

An AI agent operating on trusted data products with governance and decision accountability.

Governed AI agents inherit the trust envelope of the data products beneath them — lineage, policy, ownership, explainability — so business leaders can act on their recommendations and audit them after the fact.

A prompt connected to trusted data, policy, business context and auditability.

Free-text prompts to an AI model are ungovernable. Governed prompts attach the data product, policy, business meaning and audit trail an enterprise needs before it can act on AI output.

A model where humans review and approve AI recommendations before action.

Human-in-the-loop keeps the decision owner in control. Best suited to high-stakes or low-frequency decisions where human judgment is required before commitment.

A model where humans supervise AI operations and intervene when necessary.

Human-on-the-loop scales AI without removing accountability. The agent acts within policy; humans monitor, audit and intervene when patterns or exceptions demand it.

The Data Product Workbench that enables business teams to create trusted, governed data products without code.

Latttice puts data product creation, governance and consumption in the hands of the business — with active governance, trust signals and AI built in. The platform expression of the decision-driven operating model.

The capability that connects Latttice to governed enterprise data sources and consumption tools.

Connect is how Latttice meets the existing enterprise estate — pulling from governed sources and pushing trusted data products into the downstream tools where decisions are actually made.

The guided assistant inside Latttice that helps business users build and govern data products.

CoPilot lets business users create, refine and govern trusted data products without technical handoffs — making Zero Code real for the team closest to the decision.

The capability for shaping and refining data products while preserving governance and trust.

Transform lets business teams prepare and refine data products with governance applied in line — so changes are visible, auditable and never compromise the trust envelope.

The Latttice conversational interface over trusted, governed data products.

LattticeGPT lets executives and business teams ask questions of their data products in natural language — with governance, lineage and trust signals attached to every answer.

The Data Tiles AI Factory for building governed AI agents on trusted data products.

Lenz is where AI agents are designed, governed and deployed on top of trusted data products — with explainability, accountability and the trust envelope of the underlying data carried through into every agent action.

Governance performed after the fact through audits, remediation and manual review.

Passive governance is expensive, slow and chronically out of date. It treats governance as a control layer bolted on top of data — rather than a property of the data product itself.

A business-facing workspace for organizing and interacting with trusted data products.

Pin Boards let business teams assemble the data products relevant to a decision, role or workflow — without rebuilding the underlying products or losing their governance.

Encoding governance policies into technology so they are enforced automatically.

Policy as code makes governance scalable. Instead of relying on memory, training and goodwill, policies travel with the data product and are applied uniformly every time it is used.

Managing data with product principles rather than project principles.

Product thinking introduces ownership, roadmaps, customers, feedback loops and measurement to data. Projects optimize for delivery; products optimize for value over time.

People who bridge business context with data and technology understanding.

Decision-driven organizations are built by Purple People — leaders, product managers and analysts who speak both languages, translate strategy into data products and turn data products into decisions.

A data product designed once and reused across decisions, teams and AI agents.

Reuse is where data product economics work. A reusable data product is built and governed once, then consumed many times — by people, Pin Boards, Convo and AI agents — without rebuilding the trust each time.

The governance, lineage, ownership, policy and quality context that travels with a data product.

Wherever a trusted data product is consumed — Pin Board, Convo, AI agent, downstream tool — its trust envelope travels with it. Trust is not a property of the source system; it is a property of the data product itself.

Visible indicators of freshness, quality, lineage, ownership and policy at the point of use.

Trust is only useful if the consumer — human or AI — can see it without leaving the workflow. Trust signals turn governance from a back-office activity into an executive-facing one.

Trusted, governed, explainable data available where decisions are made.

Trust changes everything. When governed, explainable data shows up at the point of decision, business users and AI agents can act with confidence — without re-asking the data team or second-guessing the number.

A governed, owned and transparent data product that consumers and AI systems can confidently use.

Trust is what separates a data product from a dataset. Trusted data products carry visible lineage, ownership, policy and quality signals — so executives and AI agents can rely on them without re-asking the data team.

Creating and consuming trusted data products without writing code or waiting for handoffs.

Zero Code does not mean no engineering — it means the business team closest to the decision can build, govern and consume data products without queuing behind a technical team. Governance and trust are built in, not bolted on.