--- title: "How to Package Data Products Inside a Data Catalog" url: "https://atlan.com/know/ai-agent/data-for-ai/how-to-package-data-products-data-catalog/" description: "How to package data products in a data catalog in seven steps: scope, output ports, documentation, contracts, owners, a pilot release and an AI agent test." author: "Emily Winks" author_role: "Data Governance Expert" published: "2026-11-01" updated: "2026-11-01T00:00:00.000Z" --- ## How to package data products in a data catalog? To package data products in a data catalog, bundle the assets that answer one consumer question with what a consumer needs to use them unaided: a stated purpose, a few output ports, documentation, a data contract or written promise per port, a named business owner, access policies and a status. In Atlan that runs as seven steps: scope, choose ports, document, attach contracts, assign owners, publish to a pilot group, then test discovery with people and with an AI agent before widening access. ## Key takeaways - Scope each data product around one consumer question; that boundary sets how many ports and contracts you maintain. - Expose consumption-layer assets as output ports and keep staging and intermediate tables internal. - Write one data contract per output port; where contracts are unavailable, put the promise in the README. - Test the product with an AI agent signed in as a pilot consumer before you widen visibility. --- Packaging a data product in a data catalog means bundling assets with what a consumer needs to use them without asking anyone: a purpose, a few output ports, documentation, a contract, an owner and a status. In Atlan that takes 7 steps, ending with a test by people and an AI agent before access widens. ### Audit a Data Product Before You Package It Give it the tables, owners, definitions and access rules behind one product. It returns twelve checks marked pass, fail or unknown, with the artifact to fetch for every unknown. [Read the skill](/skills/ai-ready-data-audit.md). *Paste into a new chat* ``` Use the skill at https://atlan.com/skills/ai-ready-data-audit.md to audit whether the data behind our data product is ready for AI agents. Ask me for whatever it needs. ``` *Run once in a terminal* ``` curl -fsSL --create-dirs \ -o ~/.agents/skills/ai-ready-data-audit/SKILL.md \ https://atlan.com/skills/ai-ready-data-audit.md ``` *For an agent* ``` curl -fsSL https://atlan.com/skills/ai-ready-data-audit.md ``` Human Agent ClaudeChatGPTGeminiCursorOther Copy --- The Open Data Product Standard (ODPS) describes the package, and the Open Data Contract Standard (ODCS) the promise behind each port. In Bitol's words, "data products encompass multiple contracts along with additional metadata" ([Bitol, 2025](https://bitol.io/bitol-announces-odcs-v3-1-0-stronger-smarter-and-stricter/)). A [data catalog](https://atlan.com/what-is-a-data-catalog/) makes those parts browsable, and an [enterprise context layer](https://atlan.com/know/what-is-the-enterprise-context-layer/) makes them readable by agents. | Field | Detail | |---|---| | Difficulty | Intermediate | | Prerequisites | Products module enabled by an admin, domain policies, cataloged source assets, a business owner | | Optional | Data contracts (private preview in Atlan, enabled on request) | | Tools needed | A data catalog with a data products module, a YAML editor, an MCP client that signs in with OAuth | | Output | A published, certified data product with explicit output ports, a contract or documented promise per port, and a passed agent test | | Standards referenced | ODPS v1.1.0, ODCS v3.2.0 (both released 2026-09-08) | --- ## Why does packaging a data product matter when AI agents are consumers? An agent has no colleague to ask, so the package has to answer its questions in advance. For the definition itself, see [what a data product is](https://atlan.com/know/what-is-a-data-product/). The idea predates agents. In 2020, Zhamak Dehghani, then Director of Emerging Technologies at Thoughtworks North America, wrote that "data mesh introduces the concept of data product as its architectural quantum" ([martinfowler.com, 2020](https://martinfowler.com/articles/data-mesh-principles.html)), the root of [data mesh and AI](https://atlan.com/know/ai-agent/data-for-ai/data-mesh-and-ai/). ODPS v1.1.0 now adds an optional `context` block of instructions and constraints for AI agents ([Bitol](https://github.com/bitol-io/open-data-product-standard/blob/main/docs/context.md)). | Package element | Question a consumer asks without it | ODPS v1.1.0 field | Where it lives in Atlan | |---|---|---|---| | Purpose | What is this for? | `description.purpose` | Product description | | Output ports | Which table do I query? | `outputPorts` | Output ports | | Documentation | What does `net_revenue` mean here? | `description.usage`, `context` | README, linked glossary terms | | Contract and SLA | Can I rely on it tomorrow? | `outputPorts[].contractId` | Contracts tab (private preview) | | Owner | Who do I ask when it breaks? | `team` | Owners | | Access | Am I allowed to see it? | not covered | Visibility, domain policies | | Status | Is it ready? | `status` | Draft or Published, certificate | In Atlan, "from a single data table to a collection of data assets, anything can be a data product" ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/concepts/what-are-data-products)). A person can ask what a column means; an agent works only from what the package says. See how [agents and humans discover data differently](https://atlan.com/know/ai-agents-vs-humans-data-discovery/), [why AI agents need an enterprise context layer](https://atlan.com/know/why-ai-agents-need-an-enterprise-context-layer/) and [what makes data AI-ready](https://atlan.com/know/ai-agent/data-for-ai/what-makes-data-ai-ready/). --- ## What do you need before you package a data product? **Organization** - A data domain that matches a business function, such as "finance and sales" or "regions of operation" ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/how-tos/create-data-domains)). Domains act as [bounded context spaces](https://atlan.com/know/bounded-context-spaces/). - A business owner, a technical owner and an agreed first use case. - Two success measures, such as Atlan's suggested "reduction in time to find data, and reduction in questions routed to engineering" ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/best-practices/design-and-roll-out-data-products)). **Platform** - The products module enabled by an admin, plus create, update and delete permissions through domain policies ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/how-tos/create-data-products)). - Source assets already cataloged with lineage. - Optional: data contracts, supported only on Tables, Views and Materialized views ([Atlan docs](https://docs.atlan.com/product/capabilities/governance/contracts/how-tos/create-data-contracts)). An [AI readiness assessment](https://atlan.com/know/ai-agent/data-for-ai/ai-readiness-assessment/) helps you pick a first domain whose assets are already in decent shape. --- ## Step 1: Scope the data product around one consumer use case Scope the product around one consumer question. That boundary sets how many ports and contracts you will maintain. **How to do it** 1. Write the question in one sentence, for example "How many orders shipped each day, by region?" 2. Pick the domain that owns the answer. A shared asset can still appear in several domains and products ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/best-practices/design-and-roll-out-data-products)). 3. Name the product so the question is obvious. Names cap at 80 characters ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/how-tos/create-data-products)). 4. If you keep an ODPS descriptor, fill in `id`, `name`, `version`, `domain` and `status`. Only `apiVersion`, `kind` and `id` are required ([Bitol](https://github.com/bitol-io/open-data-product-standard/blob/main/docs/fundamentals.md)). **Check before moving on**: someone outside the team, given only the name and description, writes the same purpose as the owner. **Common mistakes**: Atlan's rollout guide lists "one product per report or dashboard" and "spending the whole session on definitional debate" as pitfalls ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/best-practices/design-and-roll-out-data-products)). Let several reports read one product. See [how to build data products](https://atlan.com/how-to-build-data-products/) for the wider sequence. --- ## Step 2: Bundle the assets and choose a few output ports Output ports are the product's public interface. Consumers query them; everything upstream stays internal. In Atlan, "output ports determine how data products connect to one another" ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/how-tos/create-data-products)). You cannot set input ports by hand, because other products' output ports become your inputs in business lineage ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/concepts/what-is-business-lineage)). **How to do it** 1. Under *Select assets*, choose **Add via filters**. "Match all" joins criteria with AND, "Match any" with OR, and **View assets** previews the result ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/how-tos/create-data-products)). Write the rule against the consumption-layer schema so `stg_` and `int_` tables stay out. 2. Mark each consumption-layer asset as an output port. More than one is fine, for example "a dashboard for visualization plus a materialized view for extraction on the same product" ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/best-practices/design-and-roll-out-data-products)). 3. Open the Lineage tab and confirm upstream products show up as inputs. In the running example, `fct_orders_daily` and `orders_kpi_view` are ports; `stg_orders_raw` and `int_orders_deduped` stay internal. Here is the same product as an ODPS v1.1.0 descriptor (media type `application/odps+yaml;version=1.1.0`, per the [ODPS README](https://github.com/bitol-io/open-data-product-standard)). It validates against the [v1.1.0 JSON Schema](https://github.com/bitol-io/open-data-product-standard/blob/main/schema/odps-json-schema-v1.1.0.json): ```yaml apiVersion: v1.1.0 kind: DataProduct id: 3f9c2a10-6b4e-4d2a-9e71-0c5d8b7a1f42 name: Daily shipped orders version: 1.0.0 status: draft domain: sales description: purpose: Daily shipped orders by region, for fulfillment and finance usage: Query fct_orders_daily for counts; use orders_kpi_view for dashboards context: instructions: Count shipped orders from fct_orders_daily. Do not query staging or intermediate tables. outputPorts: - name: fct_orders_daily description: One row per region per ship date type: table version: 1.0.0 contractId: 9a7d0e21-4c3b-4f8e-a1d2-5e6f7a8b9c01 - name: orders_kpi_view description: Dashboard view with metric logic applied type: view version: 1.0.0 contractId: 2c8e4b6d-1f3a-4e5b-8d7c-9a0b1c2d3e45 team: name: Sales data products members: - username: jdoe role: business owner - username: asmith role: technical owner support: - channel: "#orders-data" tool: slack url: https://example.slack.com/archives/orders-data ``` Keep the descriptor in Git as the tool-neutral record, and set the same facts on the product in Atlan. It omits `inputPorts` because Atlan derives inputs from lineage. **Check before moving on**: every output port answers part of the Step 1 question, and no `stg_` or `int_` asset is a port. **Common mistakes**: "marking every lineage asset as an output port" floods "the consumer view with upstream technical tables" ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/best-practices/design-and-roll-out-data-products)). See [data lineage for AI](https://atlan.com/know/ai-agent/data-for-ai/data-lineage-for-ai/) and [MCP for data lineage](https://atlan.com/know/mcp/mcp-for-data-lineage/). --- ## Step 3: Document the product to a good-enough bar Atlan's rollout guide sets "a minimum viable bar" of "description, owner, domain assignment, and certificate status," and says to enrich to "good enough," not perfect ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/best-practices/design-and-roll-out-data-products)). **How to do it** 1. Write the description as the question the product answers. 2. Add a README with "links to the support/intake channel so consumers know how to request changes," and add business terms from the underlying tables up to the product level ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/best-practices/design-and-roll-out-data-products)). 3. Record the metric definitions, or point to your [semantic layer](https://atlan.com/know/semantic-layer/). See [managing KPI definitions and a business glossary](https://atlan.com/know/ai-agent/how-to-manage-kpi-definitions-and-a-business-glossary-in-a-data-catalog/). 4. Put the rules an agent must follow ("count orders from `fct_orders_daily` only") in the description and README. In ODPS v1.1.0 they go in `context.instructions`, "natural language guidance for AI agents and tools on how to use this entity" ([Bitol](https://github.com/bitol-io/open-data-product-standard/blob/main/docs/context.md)). **Check before moving on**: a new analyst can say what the product is for from the description alone, and every metric name resolves to one definition. **Common mistakes**: "requiring 100% enrichment before rollout" stalls launches ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/best-practices/design-and-roll-out-data-products)), and column docs pasted into the description bury the purpose. The [context layer glossary](https://atlan.com/know/context-layer-glossary/) and [business context for AI](https://atlan.com/know/business-context-for-ai/) help you choose product-level terms. --- ## Step 4: Attach a data contract and SLA to each output port A data contract is the written promise behind one output port: schema, quality rules and freshness. ODCS v3.1.0 deprecated the contract's top-level `dataProduct` field ([Bitol, 2025](https://bitol.io/bitol-announces-odcs-v3-1-0-stronger-smarter-and-stricter/)); v3.2.0 still accepts it, marked "DEPRECATED since v3.1.0" ([Bitol](https://github.com/bitol-io/open-data-contract-standard/blob/main/schema/odcs-json-schema-v3.2.0.json)). The product points to each contract through `outputPorts[].contractId` instead ([Bitol](https://github.com/bitol-io/open-data-product-standard/blob/main/schema/odps-json-schema-v1.1.0.json)). ODCS v3.2.0, released on 2026-09-08, is current ([GitHub](https://github.com/bitol-io/open-data-contract-standard/releases/tag/v3.2.0)). **How to do it** 1. Write one contract per output port. This one covers `fct_orders_daily`; `orders_kpi_view` gets its own. It declares every column the SLA references and validates against the ODCS v3.2.0 schema, following the [SLA](https://github.com/bitol-io/open-data-contract-standard/blob/main/docs/service-level-agreement.md) and [data quality](https://github.com/bitol-io/open-data-contract-standard/blob/main/docs/data-quality.md) docs: ```yaml apiVersion: v3.2.0 kind: DataContract id: 9a7d0e21-4c3b-4f8e-a1d2-5e6f7a8b9c01 version: 1.0.0 status: active schema: - name: fct_orders_daily properties: - name: ship_date logicalType: date required: true - name: region logicalType: string required: true - name: order_count logicalType: integer required: true quality: - metric: nullValues mustBe: 0 slaProperties: - property: latency # data no older than 1 day, measured on ship_date value: 1 unit: d element: fct_orders_daily.ship_date ``` 2. Check whether contracts are on for your tenant. They are in private preview, "enabled for a larger cohort of customers on request" ([Atlan docs](https://docs.atlan.com/get-started/references/product-release-stages)). Only Table, View and MaterialisedView assets support them. Iceberg tables and columns do not, and neither do non-SQL ports such as API specs, reports and streams ([Atlan docs](https://docs.atlan.com/product/capabilities/governance/contracts/how-tos/create-data-contracts)). 3. If they are on, create a contract from the asset's Contracts tab, which drafts it "based on asset metadata," or import one with the Atlan CLI "from your local machine to Atlan directly or through a CI/CD pipeline." The CLI uses "Atlan's YAML contract template" and the docs do not mention ODCS, so map your ODCS fields into that template ([Atlan docs](https://docs.atlan.com/product/capabilities/governance/contracts/how-tos/create-data-contracts)). Set the port's `contractId` to the ODCS `id`. 4. If they are off, or the port is not a SQL asset, request the preview. Meanwhile, copy the columns, null rule and freshness target from the ODCS file into the README. **Check before moving on**: every output port has a contract or a README promise. **Common mistakes**: one contract for the whole product, or contracts on staging assets. See [data contracts for AI](https://atlan.com/know/ai-agent/data-for-ai/data-contracts-for-ai/) and [data quality for AI agents](https://atlan.com/know/data-for-ai/data-quality-for-ai-agent/). --- ## Step 5: Name a business owner and set domain policies Ownership decides who answers when the product breaks. Domain policies decide who can change and read it. Atlan's owner test is "who is most impacted if this data disappeared," not "who built the tables," with "one named owner per product" ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/best-practices/design-and-roll-out-data-products)). Pair that business owner with a technical owner. In ODPS, both go under `team.members` with a free-text `role`. **How to do it** 1. Assign the business owner and the technical owner on the product. 2. Set domain policies, which "control which users can (or can't) take certain actions within a data domain or subdomain," including creating, updating and deleting products ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/how-tos/create-domain-policies)). 3. Give consumers read access: "grant read on the product's domain through a domain policy," and "verify query-access policies" before inviting users ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/best-practices/design-and-roll-out-data-products)). **Check before moving on**: the owner field names a person, not a team alias, and a pilot consumer can open the product's assets. **Common mistakes**: "defaulting ownership to data engineering" leaves "consumers with no accountable business contact" ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/best-practices/design-and-roll-out-data-products)). See [who owns the context layer](https://atlan.com/know/context-layer-ownership-data-vs-ai-teams/), and [agent access control](https://atlan.com/know/ai-agent-access-control/) and [role-based access control](https://atlan.com/know/ai-agent/context-layer/context-layer-role-based-access-control/) for how the same permissions apply to agents. --- ## Step 6: Publish to a pilot group and check the product score A Draft product "is only visible to product owners"; a Published one is "for users to consume" ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/how-tos/create-data-products)). Publish to a small group first so Step 7 can test real consumer identities. **How to do it** 1. Set visibility to "Private to selected members" and add your pilot consumers. 2. Set sensitivity (Public, Internal, Confidential) and criticality (High, Medium, Low). 3. Apply the certificate in the Details section, then change status from Draft to Published. Later states are Sunset, "planned for retirement," and Archived ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/how-tos/create-data-products)). See the [data product lifecycle](https://atlan.com/know/data-product-lifecycle/). 4. Review the product score once it refreshes. Atlan scores each product "on a scale of 0-5" across six principles (Discoverable, Understandable, Addressable, Secure, Interoperable, Trustworthy), "updated daily at 4:00 AM UTC" ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/concepts/what-is-a-product-score)). Expect a 0 at first: the score "shows 0 right after setup," and "scores live at the asset level today" ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/best-practices/design-and-roll-out-data-products)). Use it to find the weakest principle; the [AI-ready data checklist](https://atlan.com/know/ai-agent/data-for-ai/ai-ready-data-checklist/) adds checks for model-facing products. **Check before moving on**: status is Published, visibility is limited to the pilot group, and the certificate is applied. --- ## Step 7: Test discovery with people and agents, then widen access Test the package with a consumer who was not in the room, human or agent, before you open it up. **How to do it** 1. **Connect an MCP client as a pilot consumer.** Atlan's hosted MCP server is "available on every Atlan tenant with nothing to install" ([Atlan docs](https://docs.atlan.com/product/capabilities/atlan-ai/how-tos/atlan-mcp-overview)) at `https://mcp.atlan.com/mcp`. Use OAuth, where "each person signs in with their own Atlan account," not an API key, which "acts as one shared identity." In Claude Code, run `claude mcp add --transport http atlan-oauth https://mcp.atlan.com/mcp`, then `/mcp` to sign in ([Atlan docs](https://docs.atlan.com/product/capabilities/atlan-ai/how-tos/remote-mcp-overview)). 2. **Ask the Step 1 question** in plain words: "How many orders shipped yesterday in EMEA?" The agent should call `search_assets`, which filters by "type, certification status, tags, domains, glossary terms, and dates" ([Atlan docs](https://docs.atlan.com/product/capabilities/atlan-ai/references/mcp-tools)). 3. **Read what came back.** `get_assets` "takes GUIDs from a prior search result" and "returns catalog metadata only" ([Atlan docs](https://docs.atlan.com/product/capabilities/atlan-ai/references/mcp-tools)). That metadata can include owners, `certificateStatus`, the description and README, and "data products the asset belongs to" ([Atlan docs](https://docs.atlan.com/product/capabilities/atlan-ai/references/mcp-asset-attributes)). 4. **Score the run.** Pass: the agent lands on `fct_orders_daily`, names its owner, certificate and product, and follows the usage rule. Fail: it picks `stg_orders_raw`, returns nothing, or cannot say what the table is for. Each failure points back to ports (Step 2), documentation (Step 3) or access (Step 5). 5. **Repeat as someone outside the pilot group.** MCP "only returns metadata the connected user is permitted to see in Atlan" ([Atlan docs](https://docs.atlan.com/product/capabilities/atlan-ai/references/mcp-asset-attributes)), so this user should not see the product. 6. **Widen access.** Change visibility to domain members or public, and configure a persona landing page so each group "lands on their product on login." Atlan's guide calls that "the single highest-impact lever for non-technical adoption" ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/best-practices/design-and-roll-out-data-products)). See the [data products marketplace](https://atlan.com/data-products-marketplace/). Know the limit. Of the 41 MCP tools listed as of 2026-11-01, none mentions contracts or output ports ([Atlan docs](https://docs.atlan.com/product/capabilities/atlan-ai/references/mcp-tools)). Put anything an agent must obey in the description or README, which `get_assets` returns. See the [Atlan MCP server](https://atlan.com/know/what-is-atlan-mcp/), an [MCP-connected data catalog](https://atlan.com/know/mcp-connected-data-catalog/), [how MCP delivers business context](https://atlan.com/know/mcp-delivers-business-context/) and [giving AI agents access to enterprise data](https://atlan.com/know/ai-agent/how-to-give-ai-agents-access-to-enterprise-data/). **Check before moving on**: the pilot agent passes item 4 and the outsider does not see the product. --- ## What changes after a data product is published? "Products stay editable after publishing" ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/best-practices/design-and-roll-out-data-products)), so plan for change. - A breaking port change gets a new port version. In ODPS, "the combination of the name and version is the key," and the old port can be flagged `deprecated: true` ([Bitol](https://github.com/bitol-io/open-data-product-standard/blob/main/schema/odps-json-schema-v1.1.0.json)). Announce it in the support channel. - A contract change gets a new contract version. Atlan lets you "generate new versions" and "Compare with published version" ([Atlan docs](https://docs.atlan.com/product/capabilities/governance/contracts/how-tos/create-data-contracts)). - An SLA breach needs an alert owner, set in your [data observability for AI pipelines](https://atlan.com/know/ai-agent/data-for-ai/data-observability-for-ai-pipelines/) and [context observability](https://atlan.com/know/ai-agent/ai-agent-observability/context-observability-for-ai-agents/) setup. - Retirement runs from Sunset to Archived, which is "no longer visible to users" ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/how-tos/create-data-products)). Atlan's guide also warns against "creating a duplicate 'leadership-suffixed' product instead of using a persona" ([Atlan docs](https://docs.atlan.com/product/capabilities/data-products/best-practices/design-and-roll-out-data-products)). An [agentic data catalog](https://atlan.com/know/agentic-data-catalog/) can draft descriptions and terms as the product changes, with the owner signing off. --- ## How does Atlan package data products as context for AI? Without a shared home, descriptions sit in wikis, contracts in repos and access rules in tickets. In Atlan, domains carry policies, products expose output ports linked through business lineage, and contracts (in private preview) attach to Tables, Views and Materialized views. Over MCP, an agent reads a product's descriptions, owners and certificates within the signed-in user's permissions. Each product becomes a governed unit in the [context graph](https://atlan.com/know/what-is-a-context-graph/). [Implementing an enterprise context layer for AI](https://atlan.com/know/how-to-implement-enterprise-context-layer-for-ai/) covers the wider rollout. Start with one product, and run the Step 7 test before you build the second. --- ## Real stories from real customers: Shared language for AI agents Workday uses the same MCP route for its shared business language: "Atlan captures Workday's shared language to be leveraged by AI via its MCP server. As part of Atlan's AI labs, we're co-building the semantic layer that AI needs." - Joe DosSantos, VP, Enterprise Data and Analytics, Workday Workday's session on its data product standards shows product standards and a shared glossary working together, the pairing Steps 3 and 4 rely on: --- ## Is your data product ready to open up to every consumer? Open the product up when every row passes. | Check | Pass condition | |---|---| | Scope | One consumer question, one owning domain | | Output ports | Consumption-layer assets only | | Minimum bar | Description, owner, domain, certificate set | | Contract | One per SQL output port, or a README promise where contracts are unavailable | | Owner | A named person who passes the "most impacted if this data disappeared" test | | Access | Visibility and read policy set for the intended consumers | | Classification | Sensitivity and criticality set | | Score | Weakest principle reviewed after the first refresh | | Agent test | Step 7 passes under a pilot consumer's OAuth sign-in, and an outsider does not see the product | --- ## FAQs about packaging data products in a data catalog ### 1. What should a data product include in a data catalog? A purpose, a few output ports, documentation, a contract or written promise per port, a named owner, access settings and a status. Atlan's minimum bar to publish is description, owner, domain and certificate status. ### 2. What is an output port in a data product? An asset the product exposes for consumers to query, such as a fact table or reporting view. Staging assets stay internal. In Atlan, one product's output ports become another product's input ports in business lineage. ### 3. What is the difference between a data product and a data contract? The product is the whole package: purpose, ports, team, support and status. A contract is the promise behind one port: schema, quality rules and SLAs. The product points to each contract through contractId on the output port, and ODCS v3.1.0 deprecated the contract's own dataProduct field. ### 4. Who should own a data product? The person most affected if the data disappeared, usually on the business side, paired with a technical owner who maintains the pipelines. Atlan's rollout guide lists defaulting ownership to data engineering as a pitfall. ### 5. Can a single table be a data product? Yes. Atlan's documentation says anything from a single table to a collection of assets can be a data product. It still needs a description, owner, domain, certificate and a contract or written promise. ### 6. How do AI agents discover and use data products? In Atlan, an agent connects to the MCP server, searches assets by domain, certification status and glossary terms, and reads each asset's description, owners, certificate and data products. It sees only what the signed-in user may see. No MCP tool is described as returning contract terms. ### 7. What is the Open Data Product Standard (ODPS)? An open YAML standard from Bitol for describing a data product. Version 1.1.0, released on 2026-09-08, covers fundamentals, input and output ports, support, team and an optional context block for AI agents. --- ## Sources 1. [What are data products, Atlan docs](https://docs.atlan.com/product/capabilities/data-products/concepts/what-are-data-products) 2. [Create data products, Atlan docs](https://docs.atlan.com/product/capabilities/data-products/how-tos/create-data-products) 3. [Design and roll out data products, Atlan docs](https://docs.atlan.com/product/capabilities/data-products/best-practices/design-and-roll-out-data-products) 4. [What is a product score, Atlan docs](https://docs.atlan.com/product/capabilities/data-products/concepts/what-is-a-product-score) 5. [Create data domains, Atlan docs](https://docs.atlan.com/product/capabilities/data-products/how-tos/create-data-domains) 6. [Create domain policies, Atlan docs](https://docs.atlan.com/product/capabilities/data-products/how-tos/create-domain-policies) 7. [What is business lineage, Atlan docs](https://docs.atlan.com/product/capabilities/data-products/concepts/what-is-business-lineage) 8. [Create data contracts, Atlan docs](https://docs.atlan.com/product/capabilities/governance/contracts/how-tos/create-data-contracts) 9. [Product release stages, Atlan docs](https://docs.atlan.com/get-started/references/product-release-stages) 10. [Atlan MCP overview, Atlan docs](https://docs.atlan.com/product/capabilities/atlan-ai/how-tos/atlan-mcp-overview) 11. [Set up MCP, Atlan docs](https://docs.atlan.com/product/capabilities/atlan-ai/how-tos/remote-mcp-overview) 12. [MCP tools reference, Atlan docs](https://docs.atlan.com/product/capabilities/atlan-ai/references/mcp-tools) 13. [MCP asset attributes, Atlan docs](https://docs.atlan.com/product/capabilities/atlan-ai/references/mcp-asset-attributes) 14. [Open Data Product Standard README, Bitol](https://github.com/bitol-io/open-data-product-standard) 15. [ODPS fundamentals, Bitol](https://github.com/bitol-io/open-data-product-standard/blob/main/docs/fundamentals.md) 16. [ODPS Context section, Bitol](https://github.com/bitol-io/open-data-product-standard/blob/main/docs/context.md) 17. [ODPS v1.1.0 JSON Schema, Bitol](https://github.com/bitol-io/open-data-product-standard/blob/main/schema/odps-json-schema-v1.1.0.json) 18. [ODCS v3.2.0 release, Bitol on GitHub](https://github.com/bitol-io/open-data-contract-standard/releases/tag/v3.2.0) 19. [ODCS v3.2.0 JSON Schema, Bitol](https://github.com/bitol-io/open-data-contract-standard/blob/main/schema/odcs-json-schema-v3.2.0.json) 20. [ODCS service-level agreement, Bitol](https://github.com/bitol-io/open-data-contract-standard/blob/main/docs/service-level-agreement.md) 21. [ODCS data quality, Bitol](https://github.com/bitol-io/open-data-contract-standard/blob/main/docs/data-quality.md) 22. [Bitol announces ODCS v3.1.0, Bitol](https://bitol.io/bitol-announces-odcs-v3-1-0-stronger-smarter-and-stricter/) 23. [Data Mesh Principles and Logical Architecture, Zhamak Dehghani, martinfowler.com](https://martinfowler.com/articles/data-mesh-principles.html)