> For the complete documentation index, see [llms.txt](https://data-tiles.gitbook.io/latttice-how-to-guide/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://data-tiles.gitbook.io/latttice-how-to-guide/how-to-guides/data-product-workbench-for-collibra/key-capabilities.md).

# Key Capabilities

### **Data Product Creation**

Users can create data products by creating structured data products and having those automatically registered in Collibra. Data products may include:

* Raw or foundation data products
* Derived Fields
* Fused data products combining multiple sources
* AI-enriched data models
* Analytical or operational data products

The workbench enables users to define the structure, fields, and relationships of a data product using metadata rather than complex engineering pipelines.

{% embed url="<https://scribehow.com/viewer/How_to_create_a_Structured_Data_Product_using_the_Data_Product_Workbench_for_Collibra__p3_-rAvZTumwtk5nc6sztg?referrer=workspace>" %}

### Governance Integration

The Data Product Workbench is tightly integrated with Collibra governance capabilities. Each data product inherits governance attributes such as:

* Ownership and stewardship
* Business definitions
* Data classification
* Security policies
* Lineage relationships
* Data quality rules

This ensures that all data products remain aligned with enterprise governance standards.

### Security and Policy Enforcement

Access to data products is controlled through policy-based governance integrated with Collibra. This includes:

* Role-based access controls (RBAC)
* Attribute-based access controls (ABAC)
* Policy-based access controls (PBAC)
* Fine Grained Acccess Controls (FGA)

Policies defined within Collibra can be automatically enforced when users or systems access the data product.

### Metadata-Driven Data Access

The Data Product Workbench uses metadata to dynamically generate the logic required to retrieve and assemble data products from underlying data sources. This approach:

* Reduces the need for complex data pipelines
* Allows faster creation of new data products
* Maintains alignment with governed data definitions
* Supports multi-source data environments

Data remains in its original source systems while being logically composed into data products through the metadata layer.

### Data Product Sharing and Consumption

Once created, data products can be securely shared across the organization. Consumers may include (but not limited to):

* BI tools (PowerBI, Tableau, Omni, Looker etc)
* AI agents
* Gen AI. (LattticeGPT, Claude, Gemini etc)
* Slack / Microsoft Teams
* Data science environments (ML/AI/LLM)
* Operational systems (Salesforce / ServiceNow)

The workbench provides standardized interfaces that allow data products to be consumed consistently across platforms.