Every day, thousands of new boards are created on monday.com. These boards encompass everything from marketing campaigns to bugs, projects, and enterprise OKRs. But despite this scale, most boards have one thing in common: they lack meaning for monday.com itself.

Boards today are often treated like flexible spreadsheets. While this freedom is powerful, it comes with a cost. When every team reinvents the structure of “a project,” or “a ticket,” or “a contact”, the platform becomes a collection of isolated data; data that is fragmented. Reporting becomes a challenge, and AI agents lack the context to be truly helpful. This issue becomes especially challenging for large organizations aiming to ensure collaboration and process integrity at scale.

Data entities is monday.com’s approach to solving the chaos of generic data with a standardized, semantic layer, not just for the organization, but also for the next generation of AI Agents.

The problem with generic data: boards without meaning

At monday.com, an “item” could be a lead, a task, or a project, and the system wouldn’t know. Without a schema, it’s just a row in a table. That makes it hard to aggregate data across boards and to create insightful reports. And this is just part of the problem. AI tools need “context”. For an AI agent to understand “create a new deal” or “find all critical bugs”, it needs a clear definition of what a “deal” or a “bug” is.

Data standardization at monday.com

To address the problem of generic data, as mentioned above, we introduced Data Entities: structured, reusable models that represent core business objects, such as Projects, Tickets, Deals, or Contacts.

Data Entities are basically composed of fields and policies. These policies allow entity creators to define which attributes can be customized at the board level. For example, allowing teams to define their own status labels (such as “In Review” vs. “Awaiting Approval”) while maintaining the core “Status” field structure.

This flexibility is crucial because not all “Projects” within an organization behave the same way: an R&D Project might need different “priority” levels than a Marketing Project, and policies enable this flexibility without breaking the underlying data model. Additionally, policies protect the structure by defining which core columns cannot be deleted or modified, ensuring that essential fields, such as “Project Owner” or “Due Date,” remain consistent across all project boards.

Data Entities also support hierarchical relationships that work much like object-oriented inheritance in programming. A child entity automatically inherits all fields and policies from its parent, while having the ability to add specialized fields or override certain behaviors. For example, a base “Project” entity might define core fields like “Owner,” “Due Date,” and “Status.” A “Marketing Project” entity inherits these fundamentals but adds marketing-specific fields like “Campaign Type” and “Target Audience.” Going deeper, a “Social Media Project” entity inherits everything from both ancestors while allowing for the addition of more specialized fields related to Social Media.

Scaling entity updates

By using Data Entities, monday.com can significantly improve scalability by enabling more operations to happen at the entity level rather than on individual boards. When an entity is updated, these changes immediately reflect across all associated boards throughout the platform, eliminating the need to manually update multiple similar boards individually. This approach ensures consistent data management, reduces complexity, and enables reliable scaling.

Global Entities with monday.com’s open-platform framework

Monday.com’s open platform enables us to create entity definitions that provide global data standards across all Monday solutions. By leveraging the apps framework, we can define global entity models that are maintained centrally but utilized across all solutions.

This architecture means that when a developer creates a bug board in Monday Dev, it automatically inherits the same semantic structure defined in the solution, enabling unified reporting.

Our new “data-entity” app feature within monday’s open platform provides developers with the tools to define and deploy standardized entity models.

Productizing Data Entities

Data Entities at monday.com currently serve as internal infrastructure, powering Managed Templates alongside another key standardization building block – Managed Columns. Managed Columns standardize individual fields across multiple boards, and Data Entities provide a holistic semantic structure for entire business objects.

While internal teams already leverage their capabilities, these standardized entities aren’t yet directly available to external developers and customers.

Introducing global entities through monday.com’s powerful apps framework marks our first major step toward unlocking many user-facing features in the future. By standardizing and centralizing data structures, we can enable high-scale reporting, seamless access to data, entity-level permissions, relationships between entities, and more. Ultimately, this will allow teams to intuitively interact with their data through familiar concepts like “Bugs,” or “Projects,” rather than having to reference specific boards or isolated structures.

The next step is making this capability available externally, enabling external developers to unlock the power of standardized entities and build custom applications with minimal friction.

The AI Opportunity

Structured Data Entities aren’t just useful for consistent reporting; they’re essential to the success of AI tools. When entities like “Tasks,” “Deals,” and “Contacts” have a standard, predictable structure, AI tools can understand user intent and deliver accurate and helpful outputs.

Beyond improving existing features, standardized entities enable more powerful future possibilities:

Advanced Predictions: AI models can better predict timelines, budgets, and resource allocation when working with standardized project data.

Smarter Automations: Standardized entities allow automation to become context-aware, automating workflows based on entity definitions and semantic relationships.

Natural Language Interfaces: Clearly defined entities open the way for more advanced natural language interactions, allowing users to ask complex questions or request reports in conversational language.

What’s next

As we continue exploring the potential of Data Entities, there are exciting opportunities ahead:

Deeper Integration with Monday’s AI Suite: Exploring ways Data Entities might be leveraged across various AI-powered features, potentially providing more intelligent insights across the platform.

Potential Availability to External Developers: Investigating opportunities to expose Data Entities within the app’s framework, allowing external developers to build standardized, integrated applications more easily.

Considering Custom Entity Creation: Exploring the possibility of enabling users to define their own custom Data Entities within monday.com, potentially increasing flexibility and personalization in managing their workflows.

These possibilities represent directions we’re excited to consider as we further develop the concept of Data Entities.