# Duodata - full site content Source: https://duodata.ai. Pricing: Free ($0, 1 Metric Steward), Teams ($80 / Metric Steward / month, billed annually), Enterprise (from $50,000 / year). --- URL: https://duodata.ai/ # Duodata — Business-Approved Metrics for AI, semantic layers, and data platforms. **The Business-Approved Metrics and Metrics Ontology.** Duodata is where business and data teams agree on what a metric means, then push that definition into AI assistants, Snowflake, Databricks, Microsoft Fabric, and BI tools. Talk to your data and get one answer everywhere. [Talk to us about your metrics problem](/#contact) --- ## Metrics chaos is slowing every decision. Without a [Metrics Ontology](https://duodata.ai/glossary#metrics-ontology) and an approved definition layer, the same KPI means different things in AI tools, data platforms, semantic layers, and dashboards. - **AI answers without approved context.** When users talk to your data through Copilot, ChatGPT, or custom agents, the tools make up metric logic. Answers erode trust in AI and contradict the dashboard. - **KPI disputes in every meeting.** Finance, sales, and product show different numbers for the same KPI. QBRs and board reviews stall while teams reconcile live. - **Manual reconciliation consumes analyst capacity.** Data teams spend roughly 30% of their time reconciling definition drift instead of analyzing. Spreadsheets cannot keep pace with enterprise reporting. > **"Organizations that prioritize semantics in AI-ready data will increase their agentic AI accuracy by up to 80% and reduce costs by up to 60% by 2027."** — [Gartner, 2026](https://www.gartner.com/en/newsroom/press-releases/2026-05-11-gartner-says-lack-of-semantics-causes-inaccurate-artificial-intelligence-agents-and-wasted-spending) --- ## What Duodata does A Business-Approved Metrics layer and Metrics Ontology that defines metric logic once, governs it centrally, and projects it into your entire data stack. ### Capture and govern metric definitions - Define metrics, slices, reports, and sources in business language. - Keep ownership, status, and approvals in one place. ### Deploy consistent logic into your stack - Project metric definitions into AI and BI tools, Snowflake, and Databricks. - Avoid drift when new AI agents or dashboards are added. ### Document and explain every metric - Auto-generate human- and agent-friendly documentation and value driver trees. - Show how metrics relate and where they are used. --- ## How Duodata fits into your metrics workflow Duodata introduces new capabilities across the entire metrics lifecycle — from discovery to secure deployment. 1. **Discover** — AI scans docs, SQL, and tools to surface existing metrics. 2. **Manage** — Build your Metrics Ontology: one business-approved source of truth for metric definitions. 3. **Document** — Auto-generate, version-control, and share metric documentation. 4. **Integrate** — Connect Snowflake, Databricks, semantic layers, BI tools, and AI agents. 5. **Deploy** — Publish into Snowflake Semantic Views, Databricks Metric Views, and Microsoft Fabric semantic models. 6. **Secure** — Apply SOC 2–ready controls, SSO, and RBAC to your metrics layer. --- URL: https://duodata.ai/pricing # Pricing for Business-Approved Metrics. Start free. Align your team. Scale into governed metric context for AI, data platforms, and dashboards. Viewers are unlimited and free on every plan. Renewal sizing only. No automatic overages. ## Plans ### Free **$0. Free forever.** For individuals defining and sharing business metrics. - 1 [Metric Steward](https://duodata.ai/glossary#metric-steward) - Unlimited Viewers - Unlimited metric definitions - AI Connector (Resolution Packs or pay-as-you-go) - Community support [Start free →](https://app.duodata.ai/register?plan=free) ### Teams (Most popular) **$80 / Metric Steward / month, billed annually.** For teams aligning metric definitions across business and data stakeholders. Everything in Free, plus: - Per-seat Metric Stewards, no minimum - Reviews and approvals across stewards - AI Connector (Resolution Packs or pay-as-you-go) - Priority support [Start Teams →](https://app.duodata.ai/register?plan=teams) ### Enterprise **From $50,000 / year.** For companies governing metric context across data platforms, BI, and AI. Renewal sizing only. No automatic overages. Everything in Teams, plus: - Unlimited Metric Stewards - AI Connector with 50,000 AI Metric Resolutions per year - 1,000 Implementation Mappings included - Git sync, SSO / SCIM, audit logs - Data Platform Connectors available as add-on Talk to sales. --- ## AI Connector is built into every plan Enterprise includes 50,000 AI Metric Resolutions per year. Free and Teams use Resolution Packs or pay-as-you-go. --- ## Add-ons Extend Duodata with connectors, activation, and additional capacity. - **Data Platform Connectors** — Native sync to Snowflake, Databricks, and more. Requires Enterprise. Starts at $25,000 / year. - **Metrics Ontology Bootcamp** — Six-week guided activation. Your first 20 to 25 governed metrics live in production. - **AI Resolution Packs** — Prepaid AI Connector capacity. Available on every plan. - **Additional Implementation Mapping capacity** — Buy in 1,000-mapping increments at renewal. --- ## How Duodata works under the hood - **What is a Metric Steward?** A named user who creates, edits, approves, and publishes metric definitions. Viewers stay unlimited and free on every plan. - **What is an Implementation Mapping?** A link between an approved metric and where it physically lives in your data platform. Enterprise includes 1,000 mappings. - **What is the AI Connector?** AI Connector gives Copilot, Claude, ChatGPT, custom agents, and automation workflows access to governed metric context through MCP/API/CLI interfaces. AI Connector is built into every plan: Free, Teams, and Enterprise. Enterprise includes 50,000 AI Metric Resolutions per year. Free and Teams use AI Resolution Packs (prepaid capacity) or pay-as-you-go. - **What is the Metrics Ontology Bootcamp?** A six-week guided activation. We take 20 to 25 of your most-fought-over metrics live in production. - **What is a Data Platform Connector?** A native sync to Snowflake, Databricks, and other platforms. Optional add-on for Enterprise customers. - **How does renewal sizing work?** Usage is measured during the year. We resize together at renewal. No automatic overages, no surprise invoices. --- URL: https://duodata.ai/about # Why we're building Duodata Enterprises live in metrics chaos, and it blocks trusted reporting, AI, and analytics. Duodata's mission is to be the business-approved metrics layer that bridges business and data teams. ## Where it started At a Fortune 500 retailer, our CTO Luciano watched teams argue over "the right" revenue number before every board meeting. Data teams spent weeks reconciling KPIs instead of improving them. As we spoke with more mid-market and enterprise organizations with modern data stacks or migrations underway, we kept seeing the same thing: no shared, governed metric model that business and data could both stand behind. ## Why we are building now As founders we spent years in the Snowflake, Databricks, and dbt ecosystems and saw the same pattern. Architects and SIs told us they were quietly building their own metric layers because nothing on the market solved the problem. That validation from platform teams and partners convinced us to build Duodata as a business-first conceptual metrics layer instead of yet another bolt-on tool. ## Where we are going We are building industry-specific ontology templates so teams can start with pre-built metric models for retail, healthcare, and financial services. We are expanding platform support and connectors to more data platforms, AI agents, and BI tools. Our goal is simple: when serious data teams decide to fix metrics chaos before scaling analytics or AI, they start with Duodata. --- ## Founding Team ### Andreas Schurch — Co-founder & CEO **Ecosystem and market execution** Built repeatable enterprise growth through the cloud, SI, and technology ecosystems Duodata must win through. - Generated $7.4M in partner-sourced ARR in Matillion's last full year, increasing partner contribution from 17% to 33%. - Grew Google-sourced revenue 47% quarter over quarter during Trifacta and Alteryx's SaaS and Google Cloud transition. - Built VaultSpeed's US operation and partner programs across the modern data stack. - [LinkedIn](https://www.linkedin.com/in/andreas-schurch/) ### Luciano Franceschina — Co-founder & CTO **Problem origin and product architecture** Turned a firsthand enterprise alignment problem into Duodata's core Metrics Ontology. - At Migros, Switzerland's largest retailer, identified the challenge that became Duodata: business and data teams lacked a shared, governed model of what metrics mean. - Designed Duodata's ontology model to connect approved definitions, valid variants, value drivers, and platform implementations. - Previously co-founded Teralytics and helped scale its data product to 65 employees across more than 12 countries. - [LinkedIn](https://www.linkedin.com/in/luciano-franceschina/) ### Georg Polzer — Co-founder & CRO **Company building and enterprise revenue** Knows how to turn a technically complex data product into a trusted global enterprise business. - Co-founded and led Teralytics for 14 years, serving major public and private sector organizations across Europe and the United States. - Built international teams in Zurich, New York, and Singapore. - Raised venture and strategic funding from Bosch, Deutsche Bahn Digital Ventures, Atomico, Lakestar, and others. - [LinkedIn](https://www.linkedin.com/in/georgpolzer) ### Bryan Mull — Co-founder & CCO **Customer delivery and platform implementation** Brings the practitioner depth to connect business-approved definitions to the systems that execute them. - Certified Data Vault practitioner since 2017, with dozens of Snowflake implementations across healthcare, insurance, and enterprise analytics. - Founded Data Wranglers and datastack.cloud, helping enterprises implement modern Snowflake and dbt environments. - Ensures Duodata works in the reality of production data stacks, not only as an abstract governance layer. - [LinkedIn](https://www.linkedin.com/in/bryanmull/) --- URL: https://duodata.ai/resources # Resources Subscribe to receive new posts by email. Browse the published articles and product updates below. - [Metrics layer vs. semantic layer](https://duodata.ai/metrics-layer-vs-semantic-layer) - [Frequently asked questions](https://duodata.ai/faq) - [Metrics glossary](https://duodata.ai/glossary) --- URL: https://duodata.ai/glossary # Metrics glossary Short definitions of the terms used across Duodata and the metrics-layer space. ## Business-Approved Metric A metric whose meaning has been defined, reviewed and approved by the business owner who is accountable for it, not just by the data team. Duodata is the system of record for business-approved metrics. ## Metrics Ontology A governed model of an organization's metrics: their approved definitions, valid variants (slices), owners, value drivers and where each metric is implemented. Duodata builds and maintains the Metrics Ontology and projects it into data platforms, BI tools and AI assistants. ## Metrics layer The layer that defines what a company's metrics mean, once, so every tool calculates them the same way. Duodata is a business-first, conceptual metrics layer above the physical semantic layers in Snowflake, Databricks and dbt. ## Semantic layer A technical layer that maps business terms to tables, columns, joins and aggregations so tools can query data consistently. Examples: Snowflake Semantic Views, Databricks Metric Views, the dbt Semantic Layer (MetricFlow) and Microsoft Fabric semantic models. ## Context layer (AI context layer) The business context an AI needs to answer data questions correctly: approved definitions, owners, valid variants, value drivers and instructions for retrieving the number. Duodata's Metrics Ontology is the context layer for metrics. ## Talk to your data Asking questions of company data in plain language through an AI assistant such as Copilot, ChatGPT, Claude, Snowflake Cortex or Databricks Genie, instead of writing SQL or building a dashboard. The answers are only reliable when the assistant uses approved metric definitions. ## Self-service analytics Business users answering their own data questions without waiting for the data team, increasingly through AI assistants. It only scales when everyone draws on the same governed metric definitions. ## Agentic analytics AI agents that query data, explain results and trigger actions on their own. Without approved semantics, agents invent metric logic and contradict the dashboards. ## MCP (Model Context Protocol) An open protocol that lets AI assistants connect to external tools and data sources. Duodata's AI Connector serves approved metric context over MCP, API and CLI. ## Metric governance (KPI governance) Assigning owners to metrics and reviewing, approving and versioning their definitions, so every team and tool uses the same meaning. ## Agent governance (business meaning) Governing what AI agents mean when they answer: which approved metric, variant and implementation they use, and stopping when a requested metric is not approved. It complements data governance (access, quality, lineage) and runtime agent controls (permissions, identity, guardrails). Duodata governs agents at this business-meaning level. ## Single source of truth for metrics One approved definition per metric that every dashboard, data platform and AI assistant uses, even when the implementations differ. ## KPI dictionary A list of KPI definitions, usually in a spreadsheet or wiki. Unlike a metrics layer, it has no approval workflow and is not connected to the platforms that calculate the numbers. ## Data catalog An inventory of an organization's datasets, owners and lineage. It shows which tables exist, not which number is the approved one. ## Metric Steward A named Duodata user who creates, edits, approves and publishes metric definitions. Viewers are unlimited and free. ## Metric owner The business person accountable for what a metric means, for example a Head of Controlling for gross margin. Owners can be assigned without logging in to Duodata. ## Slice A valid variant of a metric, such as revenue by region or by product line, defined once and governed alongside the metric. ## Value driver tree A map of which metrics influence which others, for example activation influencing churn and churn influencing net revenue retention. It explains why a number moved, which data lineage cannot. ## Implementation Mapping A link between an approved metric and where it physically lives in a data platform, such as a Snowflake Semantic View or a Power BI measure. ## Definition drift When the same KPI is calculated differently across dashboards, platforms and AI tools, so teams get conflicting numbers. ## AI Connector Duodata's interface (MCP, API, CLI) that serves approved metric context to Copilot, Claude, ChatGPT and custom agents, so they answer with approved logic instead of inventing it. ## Apache Ossie An open, vendor-neutral standard for semantic and metric definitions, backed by Snowflake, Databricks, dbt Labs and others. Duodata stores metric definitions as Apache Ossie / MetricFlow YAML in Git. --- URL: https://duodata.ai/metrics-layer-vs-semantic-layer # Metrics layer vs. semantic layer: model metrics apart from the data model The metric estate, how metrics are calculated from each other and which metrics drive which, belongs in its own model, abstracted from the data model. Duodata keeps that metrics model, links it deterministically to the data model, and bakes the result into the [semantic layers](https://duodata.ai/glossary#semantic-layer) of Snowflake, Databricks and Microsoft Fabric. ## Two models, one correct link - **The metrics model (business world):** the metric estate with approved definitions, owners and valid variants; calculation lineage, meaning how metrics derive from other metrics (gross margin from revenue and cost of goods sold); and [value driver lineage](https://duodata.ai/glossary#value-driver-tree), meaning which metrics move which (activation drives churn, churn drives net revenue retention). - **The data model (platform world):** tables, columns, joins, grain and filters in Snowflake, Databricks, Fabric and dbt. - **Duodata links the two:** every approved metric is mapped to its implementation through an explicit, versioned [Implementation Mapping](https://duodata.ai/glossary#implementation-mapping). The same question always resolves to the same approved metric and the same implementation, instead of an AI inferring the link at query time. - **The semantic layer runs the result:** Duodata deploys the linked definitions as Snowflake Semantic Views, Databricks Metric Views and Fabric semantic models, backed by Apache Ossie / MetricFlow YAML in Git. ## Why the metrics model must sit above the data model - **Value driver lineage exists in no data model.** Tables record transactions, not which KPI moves which. Without it, AI can say where a number came from but not why it moved. - **Data models change, metric meaning should not.** Migrations, new platforms and refactored tables would otherwise rewrite what "revenue" means. - **One metric has many implementations.** Gross margin can live in a Snowflake view, a Power BI measure and a dbt model at once. Only a model above them can say they are the same metric. | | Metrics layer (Duodata) | Semantic layer (Snowflake, Databricks, dbt, Fabric) | Data catalog | | --- | --- | --- | --- | | Models | The metric estate: definitions, variants, owners | Measures, joins and dimensions on one platform | Datasets, owners and data lineage. | | Lineage | Calculation lineage and value driver lineage between metrics | How one measure is computed from tables | How data flows between systems. | | Link to data | Deterministic, versioned mapping to every implementation | Is the implementation | Descriptions attached to tables. | | Main users | Business owners, Metric Stewards, data teams | Analytics engineers, BI developers | Data engineers, technical stewards. | | What AI gets | One approved metric, its drivers and how to retrieve it | Queryable logic for that platform | A list of candidate tables. | ## Governing agents, not just data Data governance controls the data: who may access which tables, data quality, classification and lineage. Duodata governs what AI agents mean when they answer: which approved metric, variant and implementation an agent uses, and a clear stop when a requested metric is not approved. It complements data governance and runtime agent controls (permissions, identity, guardrails) rather than replacing them. ## How it fits together 1. Business and data teams model the metric estate in Duodata and approve each definition. 2. Data teams map each approved metric to its implementation in the data model. 3. Duodata deploys the linked definitions into the semantic layers, and the AI Connector serves agents the approved context. ## When you need a metrics layer above the semantic layer - More than one data platform or BI tool holds the same KPI. - AI assistants answer metric questions and contradict the dashboards. - A migration or new platform is coming and metric meaning must survive it. - Leaders ask why a number moved, not only what it is. [Why a data catalog isn't enough for AI to talk to your data](https://duodata.ai/blog/why-a-data-catalog-isnt-enough-for-ai) --- URL: https://duodata.ai/faq # Frequently asked questions Product questions about Duodata. For seats, capacity and billing, see the [Licensing FAQ](https://duodata.ai/licensing). ## What is Duodata? Duodata is a business-approved metrics layer and [Metrics Ontology](https://duodata.ai/glossary#metrics-ontology). Business and data teams agree on what a metric means in Duodata, then deploy that definition into AI assistants, Snowflake, Databricks, Microsoft Fabric and BI tools. ## What problem does Duodata solve? The same KPI means different things in different dashboards, platforms and AI tools. Duodata gives every tool one approved definition, so teams stop reconciling numbers in meetings and AI stops inventing metric logic. ## Is Duodata a semantic layer? No. Duodata models the metric estate apart from the data model, links each metric deterministically to its implementation, and deploys the result into Snowflake Semantic Views, Databricks Metric Views and Microsoft Fabric semantic models. ## Is Duodata a data catalog or a data governance tool? No. Data governance and catalogs control the data: access, quality and lineage between tables. Duodata governs what AI agents mean when they answer: which approved metric, variant and implementation they use. It complements your catalog and data governance. ## How does Duodata help AI assistants give correct answers? The AI Connector serves approved metric definitions and retrieval instructions to Copilot, Claude, ChatGPT, MCP clients and custom agents. The assistant uses the approved logic instead of guessing which table counts. ## Which platforms does Duodata work with? Snowflake (Semantic Views, Cortex), Databricks (Metric Views in Unity Catalog, Genie), Microsoft Fabric and Power BI, dbt / MetricFlow, and other warehouses such as BigQuery and Redshift through the Duodata Agent. ## Does Duodata lock metrics into a proprietary format? No. Definitions are stored as Apache Ossie / MetricFlow YAML in Git, so they stay portable across warehouses, BI tools and AI agents. ## Who uses Duodata? Business owners approve definitions, [Metric Stewards](https://duodata.ai/glossary#metric-steward) create and maintain them, and data teams map them to implementations. Viewers are unlimited and free, and owners can be assigned without logging in. ## How long does it take to get started? The Metrics Ontology Bootcamp is a six-week guided activation that puts 20 to 25 of the most-disputed metrics live in production. There is also a free plan. ## Where does Duodata run, and is my data safe? The control plane runs in Duodata. The Duodata Agent runs in your own Snowflake account or warehouse, or as a managed service. See the [Security](https://duodata.ai/security) and [Trust](https://duodata.ai/trust) pages for controls, SSO and RBAC. ## What does Duodata cost? Free ($0, one Metric Steward), Teams ($80 per Metric Steward per month, billed annually) and Enterprise (from $50,000 per year). Details are on the [Pricing](https://duodata.ai/pricing) page. --- URL: https://duodata.ai/partners # Partners — Partnering for your success Duodata works with specialist system integrators to deliver a governed metrics layer above Snowflake, Databricks, and your semantic tools. Together we help you deploy, integrate, and scale metrics with confidence. ## Our trusted implementation partners Work with certified system integrators who specialize in metrics governance, semantic layers, and data platform modernization. ### Data Wranglers Data Wranglers builds Snowflake-centric cloud data solutions for mid-sized teams, combining catalog and governance, ingestion, orchestration, and Git-based deployment into a single automated workbench for high-value data products. They are especially strong at turning scattered KPIs and report logic into a governed metric layer. ### Callista Callista is a Swiss data and AI consultancy headquartered in Zug, with 150+ specialists and 700+ projects delivered since 2010. The firm helps enterprises move AI initiatives from strategy to production across banking, insurance, and industrial sectors, including a dedicated practice in agentic engineering. Together, Callista and Duodata help clients establish business-approved metric definitions so dashboards, semantic layers, and AI agents stay aligned around the same governed meaning. ### Formative Group Formative Group is a data modernization and data management company powered by the ADEPT platform. They help enterprises modernize legacy data systems, migrate to cloud platforms, and implement governed data warehousing using approaches such as Data Vault 2.0. ### INFOMOTION INFOMOTION is a leading data and analytics consultancy in Central Europe. They enable enterprises to unlock the full potential of their data, from strategy to execution. Their 750 specialists build scalable data platforms, semantic models, and KPI frameworks on technologies like SAP, Microsoft Azure, Snowflake, Databricks, and dbt, combining deep expertise with innovation to help businesses trust their data and accelerate AI-driven transformation. ### Nexus Data Nexus Data is a global data analytics consulting firm that designs scalable data architecture, engineering, analytics, and governance solutions so enterprises can turn data into measurable growth across fintech, SaaS, retail, manufacturing, mining, and financial services. Their teams focus on Microsoft and Databricks-based governed data platforms. --- ## Technology alliances & programs ### Snowflake Partnership - Registered Snowflake Partner - Snowflake Star Drop accelerator program member - Native Snowflake Semantic Views support - Coming soon to Snowflake Native App Marketplace ### Databricks Partnership - Registered Databricks Partner - Databricks Startup Accelerator program member - Unity Catalog and AI/BI Dashboard integration - Launch partner for Databricks Marketplace ### dbt & MetricFlow - Native dbt Semantic Layer compatibility - MetricFlow YAML format support - Bi-directional sync with dbt projects ### Git Integration - Git-native metric definitions stored in YAML - Bring your own Git with enterprise-grade security - Branch and merge workflows enable collaborative, auditable changes --- ## For system integrators and data consultancies ### De-risk & Align Give clients a governed metrics layer above Snowflake, Databricks, and semantic tools so KPIs stay consistent across dashboards and AI initiatives. ### Lead with Value Start engagements with a Duodata-powered metrics workshop that clarifies KPIs and success metrics before you talk platforms or implementation details. ### Grow with Duodata Expand accounts with implementation, ongoing governance, and AI-readiness services built on top of Duodata. --- ## Partner program benefits - **Sandbox environments** — Get dedicated demo and dev environments for your team. - **Co-marketing** — Joint case studies, event sponsorships, and content collaboration. - **Certification** — Earn Duodata Partner certification through training and joint delivery. - **Lead registration** — Register deals and collaborate on pipeline through the partner portal. - **Workshops** — Access Duodata's metrics workshop framework for client engagements. - **Dedicated support** — Priority partner support and architecture advisory. Contact us to become a partner: [partner application form](https://duodata.ai/partners#become-a-partner) --- URL: https://duodata.ai/security # Security At Duodata, we take security seriously. This page outlines our [security practices](/trust) and provides information on how to report vulnerabilities responsibly. ## Vulnerability Disclosure Policy If you believe you have discovered a security vulnerability in Duodata's products or services, we encourage you to report it to us responsibly. We appreciate your efforts to help keep our platform and users safe. ### How to Report Email: [security@duodata.ai](mailto:security@duodata.ai) ### What to Include - A detailed description of the vulnerability - Steps to reproduce the issue - Potential impact of the vulnerability - Any proof-of-concept code (if applicable) - Your contact information for follow-up ### Our Commitment - We will acknowledge receipt of your report within 48 business hours. - We will investigate and work to validate the vulnerability. - We will keep you informed of our progress. - We will not pursue legal action against researchers acting in good faith. Please also review our [Terms of Use](/terms). ## Responsible Disclosure Guidelines We ask that security researchers: - Avoid accessing or modifying data that does not belong to you. - Do not perform actions that could harm our users or services. - Do not publicly disclose the vulnerability before we have addressed it. - Make a good faith effort to avoid privacy violations and disruptions. - Only test against accounts you own or have explicit permission to test. ## Our Security Practices - All data transmitted via HTTPS with TLS 1.2+ encryption. - Data at rest encrypted using industry-standard encryption. - Regular security assessments and penetration testing. - Secure development practices and code review. - Employee security training and access controls. ## Contact For security-related inquiries or to report a vulnerability: [security@duodata.ai](mailto:security@duodata.ai) For general inquiries: [contact@duodata.ai](mailto:contact@duodata.ai). You can also review our [Privacy Policy](/privacy), [Trust Center](/trust), [Terms of Use](/terms), and [California Privacy Notice](/california-privacy). --- URL: https://duodata.ai/trust # Trust Last updated: December 5, 2025 Your data's security, privacy, and reliability are our top priority. Duodata is built to give you a business approved metrics layer across platforms like Snowflake and Databricks. That only works if you can trust how we handle security and privacy. This page describes our security and trust posture at a high level. It complements, but does not replace, our [Terms of Services](https://duodata.ai/terms) and [Privacy Policy](https://duodata.ai/privacy). ## 1. Our approach to security and architecture - Duodata follows a control plane and data plane model. Duodata Cloud hosts the user interface, governance workflows, and Git integration. - Helper components run in your data platforms, such as Snowflake or Databricks, to generate or validate semantic objects and keep metric definitions in sync with implementations. - Your warehouse or lakehouse data stays in your own accounts. Duodata Cloud works primarily with metadata, definitions, and configuration, not raw business data. - For Snowflake, our helper agent runs inside your Snowflake account. It uses Snowpark Container Services or similar technology to work with your semantic objects while Duodata Cloud interacts through Git and metadata, not direct access to your warehouse data. - For Databricks, our integrations keep metric definitions aligned with notebooks, queries, Unity Catalog, and dashboards without requiring Duodata to ingest your underlying data. ## 2. Encryption and infrastructure - We host Duodata on reputable cloud infrastructure providers. - We encrypt data in transit using TLS and encrypt data at rest using industry standard encryption mechanisms where appropriate. - We separate production and non production environments and apply least privilege access to infrastructure resources. - We maintain regular backups and disaster recovery procedures designed to protect availability of the service. ## 3. Access control and operational security - We enforce role based access control for the Duodata application and supporting systems. - Access to production systems is restricted to a small number of authorized personnel based on job role and business need. - We support SSO options for customers on appropriate plans. - We maintain logging and monitoring for key systems and review alerts for unusual or suspicious activity. - Engineers and staff receive security awareness training and are expected to follow our internal security policies and guidelines. ## 4. Privacy and data protection - Duodata follows a privacy by design approach, considering privacy and data protection in product and feature design. - We collect and use personal information only as described in our [Privacy Policy](https://duodata.ai/privacy). - We keep personal information only as long as necessary for the purposes described there or as required by law, and we support deletion and export on request where applicable. - We do not sell personal information and we do not share personal information with third parties for their own marketing purposes. - For California residents and others covered by local privacy laws, we provide additional details and rights in our Privacy Policy and [California Privacy Notice](https://duodata.ai/california-privacy). ## 5. Reliability and uptime - Our infrastructure is designed for high availability and resilience. - We monitor key health and performance metrics and use alerting to detect incidents quickly. - We plan and test our incident response processes so that we can investigate and resolve issues and communicate status to customers as appropriate. - Any specific uptime commitments or service credits, if applicable, are defined in customer contracts or separate Service Level Agreements, not on this page. ## 6. Team and culture - We have a dedicated security function that works closely with product and engineering teams. - Security is part of our onboarding and ongoing training for employees and contractors. - We encourage all team members to raise potential security or privacy issues quickly so they can be investigated and resolved. ## 7. Transparency and communication - We believe trust is earned through openness. - If you have questions about how we safeguard your information or want to report a potential security issue, we encourage you to contact us. - Where appropriate, we will communicate security related updates or incidents to affected customers. ## 8. Compliance and SOC 2 - We align our controls with industry best practices and SOC 2 requirements. - We are actively working toward formal SOC 2 Type II certification. - As we achieve new compliance milestones, we will update this page and, where appropriate, provide additional documentation to customers under NDA. ## 9. Contact If you have questions about security, privacy, or our compliance program, please contact: Email: [security@duodata.ai](mailto:security@duodata.ai) We appreciate responsible disclosure of any potential security issues and will make every effort to investigate and remediate them promptly. --- URL: https://duodata.ai/privacy # Privacy Policy Last updated: December 5, 2025 Duodata Inc. ("Duodata", "we", "us", or "our") respects your privacy and is committed to protecting it. This Privacy Policy explains how we collect, use, disclose, and safeguard your personal information when you use our websites, products, and services (together, the "Services"). By accessing or using the Services, you agree to this Privacy Policy. If you do not agree, please do not use the Services. If you are a California resident, please also read our separate [California Privacy Notice](https://duodata.ai/california-privacy) for information about your rights under the California Consumer Privacy Act and related laws. ## A. Information we collect ### 1. 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Contact Information Duodata Inc. 1700 Westlake Ave N Seattle, WA 98109 Email: [contact@duodata.ai](mailto:contact@duodata.ai) --- URL: https://duodata.ai/california-privacy # California Privacy Notice Last updated: March 3, 2025 This California Privacy Notice ("Notice") supplements the information in Duodata's general Privacy Policy and applies solely to individual residents of California ("consumers"). We adopt this Notice to comply with the California Consumer Privacy Act of 2018 as amended by the California Privacy Rights Act (collectively, "CCPA"). ## Scope Duodata is a B2B software provider. This Notice applies to personal information that Duodata collects and uses in our capacity as a "business" under the CCPA. ## 1. 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Attn: Privacy Team - California Privacy Notice 1700 Westlake Ave N Seattle, WA 98109 Email: [privacy@duodata.ai](mailto:privacy@duodata.ai) --- URL: https://duodata.ai/licensing # Licensing FAQ Common questions about how Duodata licensing works. ## What is a Metric Steward? A Metric Steward can create, edit, approve, publish, or administer metric definitions. ## Are metric definitions capped? No. Free, Teams, and Enterprise allow metric definitions to grow inside Duodata. Enterprise governed-object capacity applies to metrics, slices, reports, and sources included in the Enterprise governed content set. ## What is a governed object? A governed object is a metric, slice, report, or source included in the Enterprise governed content set. Governed implementation links are tracked separately and do not count against governed-object capacity. ## What is a Data Platform Connector? A connector automates sync, deployment, validation, deep links, and drift workflows for supported platforms. ## What is AI Connector? AI Connector gives Copilot, Claude, ChatGPT, custom agents, and automation workflows access to governed metric context through MCP/API/CLI interfaces. AI Connector is built into every plan: Free, Teams, and Enterprise. Enterprise includes 50,000 AI Metric Resolutions per year. Free and Teams use AI Resolution Packs (prepaid capacity) or pay-as-you-go. ## Can an executive be the owner without logging in? Yes. Business owners and executives can be assigned as metric owners for governance and accountability without ever logging in. Ownership is a metadata attribute, not a login requirement. ## Where do business users see the benefit if they never log in? Business users benefit from consistent, governed metrics surfaced in their BI tools, AI assistants, and reports. They consume the output without needing to access Duodata directly. ## Is Duodata a data catalog? No. Duodata is a business-approved metrics layer. It focuses on metric definitions, ownership, lifecycle, and governance, not on cataloging tables and columns. It complements your existing catalog by providing the business-approved metric context. --- ## Pricing examples Teams is billed per Metric Steward, with no minimum. ### Example A: 1 Metric Steward on Teams 1 × $80 / Metric Steward / month, billed annually = **$960 / year** (equivalent $80 / month) ### Example B: 5 Metric Stewards on Teams 5 × $80 / Metric Steward / month, billed annually = **$4,800 / year** (equivalent $400 / month) ### Example C: 10 Metric Stewards on Teams 10 × $80 / Metric Steward / month, billed annually = **$9,600 / year** (equivalent $800 / month) --- URL: https://duodata.ai/blog/why-a-data-catalog-isnt-enough-for-ai Published: 2026-09-13 | Author: Georg Polzer # Why a data catalog isn’t enough for AI to talk to your data Five things AI needs to talk to your data platforms — and that a data catalog does not deliver. Every enterprise rolling out AI analytics eventually hears the same suggestion: just connect the model to the data catalog. The catalog already lists your tables, owners and lineage. It already has a glossary. So why wouldn’t that be the context layer AI needs? It is the right instinct. And it is not enough. A catalog helps people find datasets. That is not what makes an AI answer accurate. To answer “How did revenue trend last quarter?” the AI needs one approved revenue number — a catalog will list every table that looks like revenue and will not say which one counts. The AI needs a definition the business will actually maintain — catalogs are built for technical stewards, so the people who know what the KPI means never write it down. The AI needs an understanding of how the business works — catalogs show how data flows from source systems into warehouse tables and dashboards, not which levers move the result. The AI needs the retrieval spelled out for a machine — a catalog describes a table and assumes an analyst already knows how to query it. And the AI needs that definition running on the data platform — a catalog can document a table, even monitor it; it does not create and maintain a semantic layer AI can query. Those five things are what Duodata is built for. ![Three tiles in a row; only the middle one is filled Duodata blue.](https://sjlngcebnsalknianmlw.supabase.co/storage/v1/object/public/blog-images/why-a-data-catalog-isnt-enough-for-ai%2Fblog-section-1-one-number.png) ## Why can’t you just point AI at the catalog? A catalog tells you which tables exist. AI answering a metrics question does not need an inventory. It needs one approved number. “How did revenue trend last quarter?” is not a request for every asset that looks like revenue. The model has to know what counts as revenue, and which of those tables is the one to use. A catalog will happily show `finance_monthly_reporting.revenue_final`, `sales_mart.gross_rev`, and a Power BI margin measure as three available assets. All three “exist.” Only one of them is the approved monthly revenue number — and the catalog does not say which. It also will not say whether `revenue_final` treats returns or trial customers. You still have an inventory. You do not have a metric. The same gap repeats wherever the number shows up. Catalogs are built around datasets, so “revenue” gets a glossary entry on the table, another on the dashboard, another on the semantic model. Each can be locally correct and still disagree with the others. AI then has three plausible answers and no way to know which one the company uses. Duodata models the metric directly. There is one definition of gross margin, reused across every dataset, report and platform where that metric appears. The implementations can differ — Snowflake here, a Power BI measure there, a dbt model somewhere else. The meaning does not. That is the difference between documenting where numbers live and governing what the numbers mean. ![A dense grid of tiny hollow tiles beside one large blue tile.](https://sjlngcebnsalknianmlw.supabase.co/storage/v1/object/public/blog-images/why-a-data-catalog-isnt-enough-for-ai%2Fblog-section-2-business-owns.png) ## Why can’t business users just maintain metrics in the catalog? Even a metric-shaped glossary fails if the people who decide the meaning never touch it. AI then answers from a definition nobody in the business has approved. Catalogs are built for technical users: technical stewards, engineers, people who already think in tables and schemas. Asking a Head of Controlling to “own the glossary entry for gross margin” usually means they never open the tool. The definition stays in a dashboard, a spreadsheet, or someone’s head. The catalog looks complete. The meaning AI needs was never written down by the people who know it. Duodata is built for the business. Finance, operations and product teams define and maintain metrics in business language — without needing to understand fact tables, joins or warehouse schemas. They assign ownership, review changes and approve what “customer churn” or “production volume” actually means. Data teams then map that approved meaning to the right implementation. If the people who take the business decision cannot easily write it down and sign it off, AI will keep guessing. ![A gray linear conveyor of tiles under a blue branching tree that feeds one KPI.](https://sjlngcebnsalknianmlw.supabase.co/storage/v1/object/public/blog-images/why-a-data-catalog-isnt-enough-for-ai%2Fblog-section-3-value-drivers.png) ## Why isn’t data lineage enough for “why did this number move?” Catalogs are strong at showing how data moves through the estate: from a source system into a warehouse table into a dashboard. That lineage is useful for technical stewards. It is not an understanding of how the business works. When someone asks “Why did gross margin improve?”, a map of Salesforce → Snowflake → Power BI is not an answer. The model needs to know which metrics move which others — directly and indirectly. Onboarding time influences activation. Activation influences churn. Churn influences net revenue retention. That chain is a value driver tree, not a lineage diagram. Calculation lineage tells you how a metric is computed. Value driver trees tell you what the business believes will change the outcome. Both belong in the Metrics Ontology. Neither is what a catalog stores when it traces tables across systems. Without that business model, AI can tell you where the data came from. It cannot tell you which lever to pull. ![Scattered hollow catalog tiles, with a blue wash connecting a handful that turn blue — the correct retrieval path.](https://sjlngcebnsalknianmlw.supabase.co/storage/v1/object/public/blog-images/why-a-data-catalog-isnt-enough-for-ai%2Fblog-section-4-retrieval.png) ## Why can’t the catalog just write the query? Knowing *what* gross margin means is not the same as knowing *how* to get the number. AI will not infer that the way an analyst does. A person can read a table description and still know, from experience, that monthly revenue should come from this view, at month and region, excluding cancelled contracts. The catalog never had to spell that out, because it was written for that person. AI does not have that experience. If you only give it a table blurb, it will assemble a plausible query — and a plausible query is how you get a confident wrong number. Duodata spells the retrieval out for a machine: which columns, at what grain, with which joins and filters, for this metric. That is not catalog documentation of a table. It is the instruction an agent needs before it touches the database. And when someone asks for a metric that is not in the ontology yet, Duodata does not improvise a definition. It starts a definition workflow: a draft for a human owner to review, correct and approve. That is how the Metrics Ontology stays current as new questions appear, instead of silently accumulating wrong answers. ![A blue tile hovering above the grid beside the same tile stamped into the grid.](https://sjlngcebnsalknianmlw.supabase.co/storage/v1/object/public/blog-images/why-a-data-catalog-isnt-enough-for-ai%2Fblog-section-5-semantic-layer.png) ## Why isn’t a glossary enough if you already have a data platform? A catalog documents. Some catalogs also monitor when a table or pipeline changes. Neither of those puts an approved metric onto Snowflake, Databricks, BigQuery or Microsoft Fabric as something AI can actually query. A glossary entry, however complete, still leaves the model guessing at runtime. The definition lives in the catalog. The data lives on the platform. Nothing executable connects the two. Duodata forward-engineers the Metrics Ontology into semantic layers on your data platforms. The approved definition becomes an object AI and BI tools can query — not a paragraph next to a table. When sources change, Duodata does not only flag the asset. It maintains the semantic layer it created, so the metric AI is using stays aligned with the platform instead of going stale in a glossary. You keep the catalog for finding data. You use Duodata to turn approved metric meaning into something the platform can run. ![Hollow catalog tiles remain, with one blue tile sitting among them.](https://sjlngcebnsalknianmlw.supabase.co/storage/v1/object/public/blog-images/why-a-data-catalog-isnt-enough-for-ai%2Fblog-section-6-keep-catalog.png) ## Do you need to replace your catalog? No. Use the catalog for what it is good at: discovering datasets, tracing data from source systems into warehouse tables and dashboards, and helping technical stewards navigate the estate. Many catalog or glossary entries are useful starting material. They can bootstrap a draft Metrics Ontology. A human still has to review those drafts, add the missing business meaning, and sign them off. What you should not do is treat the catalog as the semantic layer for AI. Catalogs were not built to pick which revenue number counts, to let the business own that definition, to capture how the business works, to spell out retrieval for a machine, or to create and maintain a semantic layer on the data platform. Those five things are what AI needs to talk to your data platforms. They are what Duodata is built for. ## How to get started If you want AI that can ask “How did revenue trend last quarter?” and “Why did gross margin improve?” against the same approved meaning, start with the Metrics Ontology — not with another pass over the catalog. Create your first metrics on [https://app.duodata.ai](https://app.duodata.ai/) or reach out at [contact@duodata.ai](mailto:contact@duodata.ai). --- URL: https://duodata.ai/blog/the-great-spreadsheet-wars-why-your-modern-data-stack-still-needs-a-translator Published: 2025-12-29 | Author: Shawn Johnson # The Great Spreadsheet Wars: Why Your Modern Data Stack Still Needs a Translator In this post, Shawn Johnson explains why analytics problems are really semantic problems. Different teams define the same metrics differently, dashboards drift, and trust erodes. He shows how a business-approved metrics layer brings shared meaning back to data, reduces governance friction, and gives AI the context it needs to produce reliable answers. The takeaway: speed without shared meaning is expensive chaos. Fix the context, not the pipes. If you have been in data as long as I have, you remember the "Before Times". I am talking about the days before the cloud made everything instantaneous. Back when I was cutting my teeth at Huron and OppenheimerFunds, "Big Data" mostly meant "Big Excel File that crashes your laptop if you look at it wrong". I have a distinct memory of sitting in a conference room, watching two VPs nearly come to blows over a quarterly report. The VP of Sales had a spreadsheet saying we made $10M. The VP of Finance had a printout saying we made $8.5M. Neither of them was lying. They just had different definitions of "Revenue". One was counting booked contracts, the other was counting recognized cash. And me? I was the poor twenty-something systems analyst in the corner, furiously trying to reverse-engineer two different SQL scripts to figure out whose number was "more right". --- ## Speed Did Not Fix the Argument We have spent the last decade building incredible technology to fix the movement of data. At Fivetran, I helped companies move mountains of information. We solved the pipeline problem. We can now spin up a Snowflake warehouse in the time it takes to brew a coffee, a task that used to take me six weeks of paperwork and begging for server rack space. But despite all this speed, we are still having that same argument in the conference room. We just have faster dashboards to argue over. The problem is not the plumbing anymore. **It is the semantics.** --- ## The "Tower of Babel" Problem Here is the dirty secret of the modern data stack: we made it too easy to hoard data without explaining what it means. When I was moving data at **VaultSpeed** or **SqlDBM**, we focused heavily on the structure, tables, keys, and columns. But the database schema is cold. It does not care about business logic. A column named `Total_Amt` looks innocent enough, but: - Does it include tax? - Does it include shipping? - Does it subtract returns? Without a semantic layer, every single analyst in your company has to decide on that definition for themselves. I call this **"The Great Interpreter Tax"**. Every time a data scientist writes a SQL query, they are translating raw data into business logic. If you have 50 analysts, you have 50 different translators. And just like a game of Telephone, by the time the data reaches the CEO, the message is garbled. --- ## A Rosetta Stone for Metrics This is where tools like **Duodata** are becoming the new heroes of the stack. It is not just another catalog. It is a business-approved metrics layer where teams define and govern metric definitions in one place, then project them into downstream platforms so everyone measures the business the same way. Not by dumping tables and joins on business users, but by making the business meaning explicit and portable. --- ## How Semantic Modeling Stops the Arguments Semantic modeling is essentially building a layer of **Business English** on top of your **Technical Gibberish**. In my experience, 90 percent of "Data Quality Issues" are not actually broken data. The pipeline did not fail. The API did not time out. The data is fine. The context is missing. I once spent three days debugging a "critical error" where a dashboard showed zero growth. Turns out, the data engineer had filtered out "Test Accounts" using a flag `is_test = 1`, but the Marketing team was filtering them using emails like `%@test.com`. The data was not broken. The definition of a "Real Customer" was. --- ## Define Once, Use Everywhere A tool like Duodata, together with the semantic layers it feeds, fixes this by centralizing metric definitions in one governed place. You define **"Revenue"** once in Duodata's conceptual metric layer, then project it into the semantic layer and downstream tools. - The BI tool can consume that definition. - The Python script can reference that same governed logic. - The AI agent can use it too, with guardrails. If you change the logic and promote it, it updates everywhere that consumes that semantic definition. No more hunting through 400 SQL scripts to find where someone hard-coded a `WHERE` clause. --- ## Reducing the "Governance Tax" (And Saving Your Sanity) Let us be honest. Nobody likes "Data Governance". In the old days, governance meant a poor soul named "The Data Steward" walking around with a clipboard, yelling at people for not filling out metadata fields. It was a thankless job that cost companies millions in headcount and slowed everything down. During my time in sales engineering, I learned that the only way to sell governance is to make it invisible. Semantic modeling reduces governance costs because it automates the heavy lifting. - **Documentation happens automatically** Instead of writing a Word doc that nobody reads (and I have written hundreds of those), the semantic model is the documentation. - **Lineage is instant** When a number looks weird, you can trace it back through the metric definition, upstream metrics, and the declared source systems. You stop paying high-salaried engineers to be data janitors and start paying them to actually build things. --- ## The AI Elephant in the Room There is another reason this matters right now, and it is looming large: artificial intelligence. Everyone wants to point an AI agent at their database and say: > "Hey Computer, how can we save money next quarter?" But if you point an LLM at a raw data warehouse, you are asking for trouble. The AI does not know that the table `legacy_sales_2019_do_not_use` contains bad data. It just sees "Sales" and does the math. Without semantic modeling, your AI is far more likely to hallucinate. It will confidently tell you that your most profitable product is a test SKU created by a developer named Steve in 2021. Tools like Duodata provide the context and guardrails that AI needs to be useful. It helps ensure the robot is reading from the same dictionary as the CEO. --- ## The Bottom Line I have worn a lot of hats in this industry, from the guy writing scripts at 2 AM, to the architect designing cloud migrations, to the guy selling the vision. If there is one lesson I have learned, it is this: **Speed without direction is just expensive chaos.** We have the speed. The modern data stack is a Ferrari. But if you do not have hardened business concepts feeding a semantic model, a clear, agreed-upon map of what your data actually means, you are just driving that Ferrari in circles in a parking lot. It is time to stop fixing the pipes and start fixing the context. Your data team, and your sanity, will thank you. --- URL: https://duodata.ai/blog/creating-a-new-category-the-hardened-conceptual-metrics-layer Published: 2025-11-04 | Author: Andreas Schurch # Creating a New Category: The Hardened Conceptual Metrics Layer Metric chaos isn’t a dashboard problem; it’s a meaning problem. Learn why semantic drift happens, why AI amplifies it, and how a hardened conceptual metrics layer creates business-approved metrics everyone trusts. Metrics are the quiet foundation of every data-driven organization. They decide how revenue is reported, how performance is judged, and how AI systems interpret the state of the business. Yet in most companies, metric definitions are fragmented, inconsistent, and fragile. Over the past decade, I have watched data teams invest millions into modern data platforms, BI tools, and AI initiatives, only to get stuck on the same problem: No one can agree on what the numbers actually mean. ## The real cost of misaligned metrics It usually starts with a simple question. > "What is revenue this quarter?" Two dashboards show two different answers. The room goes quiet. Then the work begins. - Analysts trace SQL - Engineers check pipelines - Business teams argue over edge cases - System integrators rebuild dashboards - Projects slow down because trust disappears This pattern shows up everywhere: - Executive reviews stall - Data products fail to scale - AI pilots surface contradictions instead of insight - Governance teams become blockers instead of enablers The problem is not a lack of tools. The problem is meaning. ## Metrics are not charts, they are agreements Most organizations treat metrics as byproducts of implementation. A chart in a BI tool. A calculation in SQL. A semantic model buried in code. But a metric is not a visualization. A metric is an agreement: - What counts - What does not - Which source is authoritative - How it can be sliced - Who owns it - When it is approved - When it changes When these agreements are implicit or undocumented, semantic drift is inevitable. Every new dashboard, data product, or AI agent introduces a slightly different interpretation. That drift compounds over time. ## Why modern stacks did not solve this Cloud data warehouses, semantic layers, and analytics engineering have made data more accessible and scalable. They did not solve metric alignment. In many organizations today: - Metric definitions live in wikis, tickets, emails, and code - Business users cannot validate or approve definitions without reading SQL - Engineers become translators instead of builders - Governance is applied after the fact, not at the point of definition AI makes this gap impossible to ignore. When AI systems consume inconsistent metrics, they do not fail quietly. They confidently return the wrong answer. ## The missing layer: a business-approved metrics layer What is missing is a conceptual layer that sits above tools and implementations. A place where metrics are treated as first-class business objects, not just calculations. This is what I call a business-approved metrics layer. In this layer: - Metrics are defined in business language - Ownership and status are explicit - Lineage shows how metrics relate to each other - Value-driver relationships explain why metrics matter - Definitions are approved before they are implemented - Changes are governed, not accidental Technical teams still implement metrics in Snowflake, Databricks, dbt, and BI tools. But they do so from a shared, approved contract. Define once. Implement many times. No drift. ## From legacy chaos to AI-ready clarity Legacy systems often hide the problem because inconsistencies are buried in reports. AI exposes them immediately. If your organization cannot answer these questions clearly, AI will amplify the confusion: - Which definition of revenue should the model trust? - Is this metric experimental or approved? - Does this number come from billing, CRM, or accounting? - Has this definition changed since last quarter? A business-approved metrics layer creates a single point of clarity before metrics are used downstream. AI becomes safer because it is grounded in shared meaning. ## How this changes governance and delivery When metrics are managed conceptually: - KPIs workshops become faster and more productive - System integrators deliver reusable assets, not one-off dashboards - Governance teams enable progress instead of blocking it - Data products scale without re-litigating definitions - Executives regain trust in numbers This is not about replacing your data stack. It is about stabilizing it. ## Why this matters now The industry is converging on open semantic standards and multi-platform analytics. Snowflake, Databricks, and dbt are all moving in this direction. But without a business-first layer, semantic models will continue to drift. AI makes metric alignment non-optional. Organizations that treat metric definitions as governance artifacts, not implementation details, will move faster and with more confidence. ## The goal Go from messy KPIs to trusted business metrics. Not by adding more dashboards. Not by rewriting every pipeline. By hardening the meaning layer. --- URL: https://duodata.ai/product-updates/self-service-analytics-with-ai Published: 2026-09-02 | Author: Georg Polzer, Co-founder & CRO How to enable self-service data analytics with AI By Georg Polzer, Co-founder & CRO How to enable self-service data analytics with AI Anthropic published a fantastic post about how they enable self-service data analytics with AI. They show why a human-curated semantic layer is crucial in order to increase the answer accuracy of AI analytics agents from 21% to 95%. Self-service analytics powered by AI promises to revolutionize the way decision makers across every enterprise gain a deep understanding of the state of their business. Until now, answering questions like “How is our customer churn developing in the last quarter?” or “Why did our production volume decrease?” required complex chains of custom Excel reports, dashboards and data aggregation pipelines on top of data warehouses. And every one of those elements in the analytical tool chain required careful, brittle synchronization between business and technical users to ensure every calculation and visualization is aligned with how the business defines “customer churn” or “production volume”. With AI, self-service analytics has the chance to become truly self-serve for every user across the enterprise. Just ask any question about any metric or KPI in natural language and the AI will provide a reliable metric analysis and even recommendations on how to improve the metric further. Fulfilling this vision is not easy though. Anthropic finds that by simply connecting their data sources with the latest AI models, the answer accuracy is only 21% - which is completely unacceptable for reliable adoption across the enterprise. Every business rolling out self-service analytics with AI is currently facing this same challenge. And when trying to increase answer accuracy a list of common questions arises: Why can’t you just connect your AI to your data sources? Why can’t you just add some Slack, Google Drive or SharePoint connectors? Why can’t AI build up the context itself from your databases? Why can’t you just write some skill files? Can you create your semantic layer once and be done with it? Do you need to buy new data platforms or AI tools? Ok, it seems you do need a semantic layer. How do you add that to your existing AI and data landscape? Below we will answer each of those questions, leaning heavily on the important findings shared by Anthropic in their article. Every answer will get us closer to the understanding of how every enterprise can reach the 95% answer accuracy Anthropic was able to achieve for their AI-powered self-service analytics. Why can’t you just connect your AI to your data sources? Anthropic’s article clearly shows what happens when you connect your AI agents with your databases without any extra scaffolding: a 21% answer accuracy. The answer to this question is that databases often just don’t contain the necessary context. A database column might be called “revenue_final” inside a table “finance_monthly_reporting”. Nowhere does it say how this revenue number treats returns or trial customers. And nowhere does it say whether this is the right table to pull monthly reporting numbers from in the first place. You will need to provide clear definitions for both to your AI agents to ensure correct answers to the question “How did revenue trend in the last quarter?”. Duodata allows you to cleanly capture exactly those two important context elements - what does each metric mean and which is the canonical source system, table and column or field. We call this the Metrics Ontology. Why can’t you just add some Slack, Google Drive or SharePoint connectors? Your intuition is right: Slack, Google Drive and SharePoint do contain important context for your AI agents. The trouble is that this context is not curated. It may contain outdated or conflicting information. So when leveraging your existing work output as context source, a two-step process is best: let AI comb through all your content across Slack, cloud storage etc. and create candidate metric semantics for your semantic layer. Then a human will need to review those drafts, add or correct definitions and sign them off. Duodata is the right place where your AI agents can create proposed metric definitions and your business users can easily review, edit and approve them before the metric becomes part of your Metrics Ontology. Why can’t AI build up the context itself from your databases? Many data platforms start to offer the feature of automatically creating semantic layers from the data platform schemas. Anthropic tested that approach and it actually reduced analytics query answer accuracy! The reason for that is that - as outlined in the question about querying databases with AI directly above - the data schemas don’t contain the necessary business meaning to enable the correct understanding of business metrics. Whether sales commissions are included or excluded in “gross profit” is something that needs to be written down by the very humans who took the business decision of whether that is the case or not. This can’t be inferred from a database schema. Duodata is supporting such an approach. Business users create and approve the definition of every metric in your Metrics Ontology. Duodata agents then project those definitions tied to the right table and column into your data platforms to ensure your AI can talk to your data. Why can’t you just write some skill files? Anthropic shows how important it is to write good skill files. But likely not in the way you think. Early analytics agent pilots tend to use skill files as a hotch-potch of duct tape, business semantics and data normalization in order to make the agents answer a set of pre-canned analytics queries reliably. This works for a quick pilot, but not for production deployment. As you can see in the Anthropic article, a good skill file consists of a small set of clear instructions around which methods and context sources to use for every query. Every context source itself must live outside of your skill file. First, you want to separate your skill files, which evolve with model changes, from your business context, that holds true no matter which model generation is working with your data. In addition, your business users need a clean user interface to create and maintain their metric meaning definitions together with governance and approval workflows instead of fighting with thousands of lines of complex free text. Duodata is the right place to create and maintain this model- and platform-agnostic semantic layer. Your agents can then easily leverage the Metrics Ontology either via MCP or via semantic layers that Duodata projects across your data platforms and source systems. Can you create your semantic layer once and be done with? No, and Anthropic's numbers show how fast your semantic layer decays. Their answer accuracy dropped from around 95% at launch to around 65% within a month. Data sources, schemas and business definitions change constantly, and every change turns a correct definition into a subtly wrong one. Anthropic’s fix was to make the semantic layer a living asset: definitions live next to the data models they describe, a change to one triggers a review of the other, and scheduled agents scan for wrong answers and propose corrections that a human signs off. This is exactly what Duodata is built for. Business users can easily make updates to existing metric definitions or add new ones to the Metrics Ontology. And every metric definition is linked to its source table and column, allowing Duodata agents to automatically detect when data schemas start to diverge from the metric definitions. Do you need to buy new data platforms or AI tools? No. As Anthropic shows in their article, they are leveraging their existing Claude AI as the interface and reasoning layer. And they are accessing their existing database and data platform landscape. What they are inserting in between those is a thin but well-thought-out set of instructions and most crucially a human-curated semantic layer. We at Duodata believe that you should use your existing AI tooling and your existing data sources - connected with a Metrics Ontology as the semantic layer - to enable self-serve analytics. Your users will not need to learn yet another new tool. And your IT department will thank you for not introducing yet another element into the already complex IT landscape. Ok, it seems you do need a semantic layer. How do you add that to your existing AI and data landscape? Anthropic shows how incredibly simple the integration of a human-curated semantic layer is into AI-enabled analytics workflows. All it takes is a set of simple instructions for the AI model inside the skill file to always consult the semantic layer first before answering any query from the existing data sources. No big migration or engineering required. Duodata offers an MCP interface that is easy to use for every agent via exactly such a simple instruction set in your skill file. Duodata also maintains semantic layers across your data platforms - in that case you don’t even need a single change in your skill file, since your AI agents will access your data platforms via the Duodata-created semantic layer. How to get started with AI-powered self-service analytics In summary, we at Duodata are thrilled about the prospect of AI agents becoming fully capable coworkers in the enterprise, making every employee and business more productive. In order to achieve that goal, the first step is to give every agent a deep and correct understanding of every business metric and where to find the right data source for each metric. This is what Duodata delivers. A powerful, easy-to-use interface for business stakeholders to curate a Metrics Ontology and for IT to link it to the right source. A MCP interface to ground every agent in the correct business understanding. And agents that maintain the semantic layers across all data platforms. If you want to bring your self-service analytics into the AI age, start creating your own Metrics Ontology on https://app.duodata.ai/ or reach out at contact@duodata.ai . --- URL: https://duodata.ai/product-updates/2026-07 Published: 2026-07-28 | Author: Georg Polzer, Co-founder & CRO July 2026 Product Update By Georg Polzer, Co-founder & CRO Duodata turns existing reports and dashboards into governed business context that AI can understand. Monthly product update - July 2026 Turn existing reporting into business context for conversational analytics. Upload the analytics assets your teams already use, such as Power BI, Tableau, or Excel reports. Duodata creates a first version of your Metrics Ontology, then AI helps you complete and improve the definitions behind it. That governed business context enables AI-powered self-service business intelligence and trusted conversations with your data. Open Duodata A faster way to build your Metrics Ontology Start with what your organization has already defined in reports, dashboards, and files. Use AI to turn that starting point into complete, governed business context, then keep the semantic layer aligned inside your data platform. Start with what exists Import your reports and dashboards. Your existing analytics already contain valuable knowledge about what the business measures and how teams talk about performance. Duodata uses those assets to create the first version of your ontology. Upload Power BI models or Tableau workbooks. Bring in additional definitions from Excel, CSV, and PDF files. Review the proposed metrics, slices, reports, and sources before adding them. Import your definitions Turn existing analytics assets into reviewable ontology candidates. Drop a report, dashboard, or file here Duodata identifies definitions and relationships automatically Power BI Tableau Excel PDF 18 Metrics 9 Slices 4 Reports 3 Sources Build and maintain with AI Let AI help you build a clean, complete Metrics Ontology. Duodata AI supports the full lifecycle of your ontology—from the first draft to ongoing refinement—while keeping every proposed change reviewable. Bootstrap: describe your company or domain and let AI draft a starting ontology. Expand: add missing metrics, slices, reports, and sources through natural-language prompts. Maintain: refine descriptions, formulas, dependencies, and source context as the business evolves. Review: inspect AI-proposed changes in Suggestions before they become governed definitions. Build with Duodata AI Draft, improve, and govern your ontology throughout its lifecycle. Ask Duodata “Add the missing profitability metrics and document their dependencies.” 1 Bootstrap 2 Expand 3 Maintain 4 Review 3 suggestions Ready for steward review Review A secure way to activate your Metrics Ontology Snowflake Native App Project your Metrics Ontology into your data platform with a native app—and keep it up to date. We are excited that the Duodata Snowflake Native App is now live and easy to install through the Snowflake Marketplace. It runs inside Snowflake to generate your semantic layer—with the same native-app approach coming to Databricks soon. Keep Snowflake semantic and metric views up to date from the latest governed YAML definition. Detect drift between the expected model and changes in the underlying data-platform schema. Keep Duodata isolated from the data itself: the YAML connects the systems without Duodata accessing data stored inside Snowflake. Duodata Native App Installed through Snowflake Marketplace · Databricks coming soon Governed YAML Definitions only → Snowflake Native App Runs inside your platform Semantic views ● Up to date Schema drift ● No drift detected Duodata data access None Highlights from past product updates A quick look back at recent improvements that make governed definitions executable and easier to understand. Pending MCP Proposals Changes proposed by AI agents via the Duodata MCP server. Nothing applies until you approve. new_metric ACME-FIN-009 5/16/2026 Required dependency for EBITDA. Without a registered D&A metric, EBITDA would have to be derived ad hoc from ERP reports. { "identifier": "ACME-FIN-009", "name": "Depreciation & Amortization", "unit": "USD" } Ground AI agents in governed metric definitions. Through MCP, agents can retrieve approved definitions and context while missing metric needs become proposed updates to the ontology. Metric terminology ⌕ "basket size by region" Synonyms AOV Average order value Revenue per order basket size Average Order Value (AOV) One governed definition · ACME-SALES-0055 Approved Let every department use its own language. Synonyms help people and agents resolve department-specific terminology to one governed metric definition instead of creating duplicates. Try it out with your existing reporting You do not need to model the business from a blank page. Import what already exists, then work with AI to make it complete and trustworthy. 1. Import Upload a Power BI model, Tableau workbook, Excel file, CSV, or PDF. Duodata turns it into ontology candidates for your review. 2. Polish with AI Ask Duodata to write, clarify, and improve the definitions. Review its suggestions and approve what belongs in the governed ontology. Start from your existing analytics Want to govern your analytics stack with the Duodata Metrics Ontology? Reply to this note and we will help you define clean business metrics, implement them across your analytics stack, and make them available to dashboards and AI agents from one governed foundation. Start the conversation Duodata is the Metrics Ontology that makes your data stack AI-ready. duodata.ai · contact@duodata.ai · Unsubscribe --- URL: https://duodata.ai/product-updates/2026-06 Published: 2026-05-28 | Author: Georg Polzer, Co-founder & CRO June 2026 Product Update By Georg Polzer, Co-founder & CRO AI grounding for governed metric answers, synonyms for easier discovery, and where to meet Duodata in June. Monthly product update - June 2026 Latest feature: keep your Metrics Ontology fresh with real agent questions. Now, your AI agents and chatbots can connect to Duodata through MCP, turning missing metric needs into proposed definitions instead of workshop backlog. In the background, Duodata agents keep the analytics stack aligned with the clean business metrics stored in Duodata. Explore Duodata Product Updates Two updates for metric owners, data platform teams, and partners who need AI agents to answer from governed definitions, people to find the right metrics quickly, and systems to stay aligned. AI grounding Agent usage keeps your Metrics Ontology and semantic layer fresh. Duodata exposes governed metric definitions through MCP, so AI agents can ask approved semantic views directly while their missing metric needs become proposed updates to the ontology. Agents can retrieve definitions, formulas, dependencies, owners, and source context from Duodata. Agent questions surface missing or unclear metrics as proposals for definition and governance. Approved updates keep the underlying semantic views, schemas, and ETL transformations aligned with current analytics needs. Pending MCP Proposals Changes proposed by AI agents. Nothing applies until you approve. new_metric ACME-FIN-009 Required dependency for EBITDA. Without a registered D&A metric, EBITDA would have to be derived ad hoc from ERP reports. { "identifier": "ACME-FIN-009", "name": "Depreciation & Amortization", "unit": "USD" } Approve Reject Synonyms and tags Let every department use its own language for the same metric. Finance, sales, and operations often use different terms for the same business metric. Synonyms help people and agents resolve those terms to one governed definition instead of creating duplicates. Add synonyms like AOV, average order value, basket size, or department-specific terms. Use tags to group metrics, slices, sources, and reports by domain, owner, or governance status. Smaller improvements include cleaner PDF exports, easier navigation, and refreshed metric cards. Metric terminology Search: "basket size by region" Synonyms AOV Average order value Revenue per order basket size Average Order Value (AOV) One governed definition · ACME-SALES-0055 Approved Tags Revenue Board reporting Certified Snowflake Highlights from past product updates A quick look back at recent improvements that make governed definitions executable and easier to understand. Generate semantic layers from governed metric definitions. Duodata agents help turn approved metric logic into platform-native semantic assets such as Snowflake Semantic Views, Databricks Metrics, Microsoft Fabric, and dbt. Value driver trees with impact explanation. Metric relationships can now explain why one metric influences another, helping agents reason about how the business works. Try it out yourself You can start building your own Metrics Ontology in Duodata today, then connect agents so real business questions help reveal which definitions are still missing. Sign up for free on duodata.ai Create a workspace and start with the metrics your team already discusses in reports, dashboards, and planning meetings. Connect your AI agents Ground AI agents in Duodata through MCP so they start collecting metric questions that still need definition and governance. Define governed metrics Turn collected metric needs into approved definitions with meaning, formula, owner, source context, and dependencies. Start defining metrics Meet Duodata in June We will be where data, AI, and semantic-layer teams are gathering. If you are attending one of these conferences, book time with us to compare notes on metric governance, Snowflake, Databricks, and how to ground AI chatbots and agents in approved KPI definitions and sources. Snowflake Summit 26 June 1-4, 2026 Moscone Center, San Francisco Book a meeting Databricks Data + AI Summit June 15-18, 2026 San Francisco + virtual Book a meeting Accelerate Tomorrow AI Summit June 2-3, 2026 Berlin Book a meeting TDWI München June 23-25, 2026 MOC München Book a meeting Want to govern your analytics stack with the Duodata Metrics Ontology? Reply to this note and we will help you define clean business metrics, implement them across your analytics stack, and make them available to dashboards and AI agents from one governed foundation. Start the conversation Duodata is the Metrics Ontology that makes your data stack AI-ready. duodata.ai · contact@duodata.ai · Unsubscribe