10 Best AI Data Discovery Tools and Platforms in 2026

Finding the right data can be surprisingly difficult in modern data environments. Organizations may have thousands of tables, dashboards, files, databases, cloud warehouses, and other data assets spread across different systems. Even when the required data already exists, analysts, engineers, and business users may struggle to identify the right dataset or understand what it contains.

AI data discovery tools use artificial intelligence, machine learning, natural-language processing, and metadata intelligence to make this process easier. Instead of relying only on traditional keyword searches or manually maintained documentation, these tools can understand user intent, connect related metadata, recommend relevant datasets, and provide additional context around data assets.

AI can also make data discovery more accessible to non-technical users. A user may be able to describe the information they need in natural language rather than knowing the exact database, schema, table, or column name. The platform can then use metadata, business definitions, lineage, ownership, and other contextual signals to help identify relevant data.

This is particularly important for organizations building analytics, machine learning, and generative AI applications. Before data can be used effectively, teams need to know what data exists, where it comes from, who owns it, how it relates to other assets, and whether it is appropriate for the intended use.

What Are AI Data Discovery Tools?

AI data discovery tools are platforms that use artificial intelligence, machine learning, natural-language processing, or intelligent metadata analysis to help users find and understand data across an organization’s data environment.

Traditional data discovery often depends on manually maintained catalogs, keyword searches, documentation, and users knowing the technical names of datasets. AI-powered discovery adds semantic understanding and contextual intelligence, allowing users to search based on what they mean rather than only what the underlying data assets are called.

For example, instead of searching for a specific table name, a user could ask for data related to customers who stopped using a product. An AI-powered discovery system can use metadata, business terminology, relationships, and other available context to identify potentially relevant datasets.

AI Data Discovery Tools vs. Traditional Data Discovery

AI Data Discovery Tools Comparison

The comparison table below provides a quick overview of the best AI data discovery tools, highlighting their AI capabilities, automation features, and primary data discovery use cases.

10 Best AI Data Discovery Tools

Let’s take a closer look at the 10 best AI data discovery tools and explore how their AI capabilities can help teams find relevant data, understand its context, and reduce the time spent searching across complex data environments.

#1. Atlan

Atlan is a modern data and AI catalog platform designed to help organizations discover, understand, and work with data across complex data environments. Its AI capabilities are closely integrated with the data discovery experience, helping users search for relevant data, understand metadata, generate documentation, and connect technical information with business context.

Atlan’s natural-language capabilities allow users to describe what they are looking for rather than relying entirely on exact table or column names. AI can use metadata, lineage, ownership, business terminology, and other contextual information to help surface relevant data assets. This makes discovery more accessible to analysts and business users while still providing the technical context needed by data engineers.

The platform can also assist with understanding discovered data. Users can investigate descriptions, relationships, lineage, ownership, and other metadata around an asset before deciding whether it is appropriate for analytics, machine learning, or AI workloads. This contextual approach helps turn data discovery from a simple search process into a broader data-understanding workflow.

AI Capabilities

• AI-powered data discovery: Helps users discover relevant datasets using natural-language and contextual search.
• Natural-language search: Allows users to describe the data they need without knowing exact technical asset names.
• AI metadata enrichment: Generates or improves descriptions and contextual information for data assets.
• AI-generated documentation: Assists with documenting tables, columns, datasets, and other cataloged assets.
• Semantic data understanding: Uses business and technical context to improve how users discover and interpret data.
• Intelligent recommendations: Helps surface related or potentially relevant data assets.
• AI-assisted data understanding: Provides additional context around datasets, relationships, lineage, and ownership.
• AI-ready discovery: Helps teams identify and evaluate data for analytics, machine learning, and AI workloads.

What You Can Automate

• Data discovery: Help users find relevant datasets based on natural-language requirements.
• Metadata enrichment: Generate and improve descriptions and contextual metadata.
• Data documentation: Reduce manual effort involved in documenting data assets.
• Data recommendations: Surface relevant datasets and related assets.
• Data search: Allow users to search data using natural-language questions.
• Lineage exploration: Help users understand upstream and downstream relationships.
• Data ownership discovery: Surface ownership and stewardship information.
• Data classification: Support organization and categorization of cataloged assets.
• AI data discovery: Help identify relevant data for AI and machine learning workflows.

Best For

Organizations looking for an AI-powered data discovery experience that combines natural-language search, metadata, lineage, documentation, and business context.

AI Verdict

Atlan’s strongest AI value comes from making enterprise data easier to discover and understand through natural language and contextual metadata. Instead of requiring users to know exactly where a dataset lives, AI can help connect a business requirement with potentially relevant data assets and their surrounding context. This makes it particularly useful for modern data teams managing large and distributed data environments.

#2. Alation

Alation is an enterprise data intelligence and catalog platform that helps organizations discover, understand, document, and govern data across their data environment. Its AI capabilities enhance the discovery experience by helping users search for relevant information, receive recommendations, understand metadata, and find context around datasets without depending entirely on manually maintained documentation.

Alation can use metadata, usage patterns, search behavior, and other available signals to improve data discovery. Its intelligent search capabilities can help users find relevant data even when they do not know the exact technical name of a table or column. This is particularly useful in large enterprises where multiple datasets may appear similar but differ in ownership, business meaning, quality, or usage.

The platform also helps connect data discovery with broader context. Users can investigate metadata, lineage, ownership, usage, and other information around a dataset before deciding whether it is appropriate for a particular analytical or AI use case. This makes Alation useful for teams that want discovery to extend beyond simply finding a table.

AI Capabilities

• AI-powered data discovery: Helps users find relevant data using intelligent search and contextual metadata.
• Natural-language data search: Allows users to describe the information they need without relying entirely on technical terminology.
• Intelligent recommendations: Uses available metadata and usage signals to recommend potentially relevant data assets.
• AI-assisted metadata enrichment: Helps improve descriptions and contextual information associated with cataloged data.
• AI-generated documentation: Reduces manual effort involved in documenting datasets and other data assets.
• Semantic data understanding: Helps connect technical metadata with business terminology and context.
• AI-assisted data questions: Supports more natural interaction with data discovery and catalog information.
• Contextual data discovery: Combines metadata, lineage, ownership, and usage information to improve discovery.

What You Can Automate

• Data discovery: Help users locate relevant datasets across enterprise data environments.
• Natural-language search: Allow users to search the catalog using business-oriented questions.
• Data recommendations: Surface relevant datasets and related data assets.
• Metadata enrichment: Improve descriptions and contextual metadata.
• Data documentation: Reduce repetitive documentation work.
• Data classification: Organize and categorize cataloged data assets.
• Lineage discovery: Help users understand relationships between upstream and downstream assets.
• Data ownership discovery: Surface ownership and stewardship information.
• AI data discovery: Help teams identify data relevant to analytics, machine learning, and AI projects.

Best For

Large organizations that need AI-assisted enterprise data discovery, intelligent search, recommendations, metadata management, and broader data intelligence.

AI Verdict

Alation’s AI value is centered on making enterprise data easier to find and understand at scale. Its intelligent search and recommendation capabilities can help users move beyond exact keyword matching and discover relevant data using business context and available metadata. For large organizations with extensive catalogs, this can reduce the time users spend searching through disconnected datasets and documentation.

#3. Collibra

Collibra is an enterprise data intelligence and catalog platform that helps organizations discover, understand, organize, and govern data across complex environments. Its AI capabilities enhance data discovery by using intelligent metadata, classification, relationships, and contextual information to help users identify relevant data assets.

Collibra can bring together technical metadata and business context so users can understand more than simply where a dataset exists. AI-assisted capabilities can help enrich metadata, improve search, identify relationships, and provide additional context around data assets. This is particularly useful for organizations with large and distributed data estates where manually maintaining complete documentation is difficult.

The platform also connects discovery with governance, lineage, ownership, and data quality context. For analytics and AI teams, this broader context can help users evaluate whether a dataset is appropriate for a particular use case before incorporating it into an analytical workflow, machine learning project, or AI application.

AI Capabilities

• AI-powered data discovery: Helps users find relevant data using intelligent search and contextual metadata.
• AI-assisted metadata enrichment: Improves descriptions and contextual information around data assets.
• Intelligent classification: Helps organize data based on available metadata and classification signals.
• Semantic data understanding: Connects technical data information with business terminology and context.
• Intelligent relationship discovery: Helps users understand connections between data assets.
• AI-assisted documentation: Reduces manual effort involved in documenting datasets and catalog assets.
• Contextual search: Uses metadata and related information to improve discovery results.
• AI-ready data discovery: Helps teams identify and evaluate data for analytics and AI workloads.

What You Can Automate

• Data discovery: Identify relevant datasets across connected enterprise environments.
• Metadata enrichment: Generate and improve contextual information associated with cataloged assets.
• Data documentation: Reduce manual documentation work.
• Data classification: Organize and classify supported data assets.
• Relationship discovery: Identify relationships between datasets and other catalog objects.
• Lineage exploration: Help users understand how data moves through the environment.
• Data ownership discovery: Surface ownership and stewardship information.
• Data recommendations: Help users identify related and potentially useful datasets.
• AI data discovery: Support discovery of data for analytics, machine learning, and AI projects.

Best For

Enterprises that need AI-assisted data discovery combined with metadata, lineage, classification, governance, and broader data intelligence.

AI Verdict

Collibra’s discovery value comes from combining AI-assisted search and metadata intelligence with broader enterprise context. Users can investigate data assets alongside ownership, relationships, lineage, and governance information instead of treating discovery as an isolated search activity. This makes the platform particularly relevant for large organizations where finding the right data also requires understanding its business and governance context.

#4. Informatica

Informatica provides an enterprise data intelligence platform with AI-powered capabilities through its CLAIRE AI technology. CLAIRE applies machine learning and intelligent automation across data discovery, metadata management, classification, relationships, and broader data management workflows, helping organizations find and understand data across complex environments.

Informatica can automatically discover and analyze metadata from databases, cloud platforms, applications, data warehouses, and other enterprise systems. Its AI capabilities can enrich metadata, identify relationships, classify information, and provide additional context around data assets. This reduces the amount of manual work required to understand large and constantly changing data environments.

For data and AI teams, this contextual discovery can be particularly valuable. Finding a dataset is only the first step; users also need to understand its source, lineage, quality, ownership, and business meaning. Informatica connects discovery with these broader data intelligence capabilities, helping teams evaluate data before using it for analytics, machine learning, or AI applications.

AI Capabilities

• CLAIRE AI: Uses AI and machine learning across Informatica’s data intelligence and management capabilities.
• AI-powered data discovery: Helps users discover and understand data across enterprise systems.
• Intelligent metadata enrichment: Automatically improves metadata and contextual information.
• AI-assisted classification: Helps identify and organize different types of data.
• Intelligent relationship discovery: Identifies relationships between data assets and metadata.
• AI-assisted data understanding: Connects technical metadata with broader business and operational context.
• Intelligent recommendations: Helps surface relevant data and related assets.
• AI-assisted data intelligence: Combines metadata, lineage, quality, and other signals to improve data discovery.

What You Can Automate

• Data discovery: Discover and inventory data across connected environments.
• Metadata collection: Automatically collect metadata from supported data sources.
• Metadata enrichment: Add descriptions and contextual information to discovered assets.
• Data classification: Automate supported classification workflows.
• Relationship discovery: Identify relationships between datasets, systems, and metadata elements.
• Data lineage: Capture and expose relationships between data assets.
• Data documentation: Reduce manual work involved in documenting enterprise data.
• Data recommendations: Surface potentially relevant data based on metadata and context.
• AI data discovery: Help teams identify data suitable for analytics, machine learning, and AI workloads.

Best For

Large organizations that need AI-powered enterprise data discovery combined with metadata management, lineage, classification, data quality, and broader data intelligence.

AI Verdict

Informatica’s main strength is the breadth of CLAIRE-powered intelligence across the data environment. Rather than limiting AI discovery to search, the platform can connect data discovery with metadata, relationships, lineage, classification, and quality context. This makes it particularly useful for enterprises where users need to understand the full context of a dataset before deciding whether to use it.

#5. Microsoft Purview

Microsoft Purview is Microsoft’s data governance and catalog platform for discovering, cataloging, classifying, and understanding data across enterprise environments. Its intelligent capabilities help users locate data assets, understand their context, identify sensitive information, and explore relationships across Microsoft and supported non-Microsoft data sources.

Purview can automatically discover and catalog data from connected environments while applying intelligent classification and metadata analysis. This allows organizations to build a searchable inventory without relying entirely on manual cataloging. Users can then investigate datasets alongside classifications, lineage, ownership, and other available governance information.

Its discovery capabilities are particularly relevant for organizations using Microsoft’s broader data and AI ecosystem. As enterprises use Azure, Microsoft Fabric, Microsoft 365, and AI services, having a centralized understanding of available data can make it easier for teams to identify relevant information and evaluate its sensitivity and governance requirements before using it.

AI Capabilities

• AI-assisted data discovery: Helps users find and understand data across connected environments.
• Intelligent data classification: Uses intelligent classifiers to identify supported sensitive information.
• Natural-language interaction: Makes data discovery more accessible through natural-language experiences where supported.
• AI-assisted metadata understanding: Helps users interpret and organize information associated with data assets.
• Sensitive-data intelligence: Helps identify sensitive information that may require additional governance.
• Intelligent data context: Connects discovery with classification, lineage, and governance information.
• AI-ready data discovery: Helps teams identify relevant and appropriately governed information for AI workloads.
• Intelligent cataloging: Reduces manual effort involved in discovering and organizing data assets.

What You Can Automate

• Data discovery: Discover data assets across supported enterprise environments.
• Data cataloging: Build and maintain an inventory of discovered data.
• Sensitive-data classification: Identify supported sensitive information automatically.
• Metadata collection: Collect technical metadata from connected sources.
• Data lineage: Track relationships between supported data assets.
• Data documentation: Reduce manual effort involved in documenting data assets.
• Data labeling: Apply supported classifications and sensitivity labels.
• AI data discovery: Help teams locate relevant data for AI and analytics use cases.
• Data inventory maintenance: Keep information about discovered assets updated as the environment changes.

Best For

Organizations that need AI-assisted data discovery and cataloging within a Microsoft-centric data environment, particularly where discovery is closely connected with governance and sensitive-data management.

AI Verdict

Microsoft Purview’s discovery value comes from combining data discovery with classification, lineage, and governance context. This is particularly useful when users need to understand not only where data exists, but also what type of information it contains and what governance considerations apply. Organizations with heterogeneous technology stacks should evaluate the available connectors and specific intelligent capabilities for their environments.

#6. IBM Knowledge Catalog

IBM Knowledge Catalog is a data catalog and governance platform designed to help organizations discover, understand, classify, and manage data across hybrid and multicloud environments. Its AI capabilities help improve data discovery by enriching metadata, identifying relationships, and providing users with additional context around data assets.

The platform can bring together metadata from different data sources and organize it into a searchable data catalog. AI-assisted capabilities can help users find relevant information, understand business terminology, and identify relationships between datasets. This is particularly useful for enterprises where data is distributed across cloud platforms, databases, data warehouses, and other systems.

IBM Knowledge Catalog also connects data discovery with governance and data quality information. Users can investigate available metadata, classifications, ownership, and other context when evaluating whether a dataset is suitable for analytics, machine learning, or AI applications.

AI Capabilities

• AI-assisted data discovery: Helps users find relevant datasets across hybrid and multicloud environments.
• Intelligent metadata enrichment: Improves metadata and contextual information associated with data assets.
• AI-assisted classification: Helps identify and organize data based on available metadata and classification signals.
• Semantic data understanding: Connects technical metadata with business terminology and context.
• Intelligent relationship discovery: Helps identify relationships between data assets and metadata.
• AI-assisted data documentation: Reduces manual effort involved in documenting data assets.
• Contextual data search: Helps users discover data using available metadata and business context.
• AI-ready data discovery: Supports identifying data relevant to analytics and AI workloads.

What You Can Automate

• Data discovery: Discover and organize data from connected environments.
• Metadata collection: Collect metadata from supported data sources.
• Metadata enrichment: Improve descriptions and contextual information.
• Data classification: Automate supported classification processes.
• Data documentation: Reduce repetitive documentation work.
• Relationship discovery: Identify relationships between data assets.
• Data lineage: Help users understand how data moves between connected assets.
• Data recommendations: Surface related or potentially useful datasets.
• AI data discovery: Help teams locate data for analytics, machine learning, and AI projects.

Best For

Enterprises that need AI-assisted data discovery across hybrid and multicloud environments, with cataloging, governance, metadata, and data quality capabilities closely connected.

AI Verdict

IBM Knowledge Catalog is particularly relevant for organizations dealing with distributed and heterogeneous data environments. Its AI-assisted metadata and discovery capabilities can help users find and understand data without relying entirely on manually maintained documentation. The combination of discovery with governance and quality context is useful when teams need to evaluate data before using it for analytics or AI workloads.

#7. BigID

BigID is a data discovery and intelligence platform focused on helping organizations discover, classify, and understand data across cloud, on-premises, and hybrid environments. Its AI-powered capabilities are particularly focused on identifying sensitive, personal, regulated, and business-critical information across large and distributed data estates.

BigID can scan connected data sources and use machine learning and intelligent classification to identify different types of information. Instead of requiring organizations to manually inspect every database, file, table, or data store, its discovery capabilities can help automatically identify sensitive data and provide context around where that information exists.

For data teams, BigID’s discovery capabilities can also help build a broader understanding of an organization’s data environment. Users can investigate data locations, classifications, relationships, and other contextual information to determine what data exists and how it should be managed. This can be especially valuable for organizations preparing data for analytics, AI, privacy, or governance initiatives.

AI Capabilities

• AI-powered data discovery: Uses AI and machine learning to identify and understand data across connected environments.
• Intelligent data classification: Automatically identifies supported sensitive and regulated data types.
• Machine learning-based classification: Uses patterns and context to improve identification of data categories.
• Sensitive-data discovery: Helps locate personal, financial, health, confidential, and other sensitive information.
• Contextual data intelligence: Adds context around discovered data to help organizations understand its significance.
• Entity and relationship understanding: Helps identify relationships between data elements and assets.
• AI-assisted data inventory: Builds a broader view of where important data exists across the organization.
• AI-ready data discovery: Helps teams identify and evaluate data relevant to analytics and AI initiatives.

What You Can Automate

• Data discovery: Scan supported data sources to identify available data assets.
• Sensitive-data discovery: Automatically locate sensitive information across connected environments.
• Data classification: Categorize discovered data based on supported classification methods.
• Data inventory: Build and maintain an inventory of discovered data.
• Data mapping: Identify where specific types of data exist across the environment.
• Metadata analysis: Analyze available information about discovered assets.
• Data relationship discovery: Identify relationships between data elements and assets.
• Risk-oriented discovery: Surface data that may require additional governance or protection.
• AI data discovery: Help teams identify data relevant to AI and machine learning projects.

Best For

Organizations that need AI-powered data discovery with a strong focus on sensitive-data identification, classification, data inventory, privacy, and governance.

AI Verdict

BigID’s AI value is especially strong around understanding what data an organization has and identifying sensitive information within it. Rather than focusing only on helping users search for datasets, its discovery capabilities can help organizations build an inventory of data and understand where sensitive information is located. This makes it particularly relevant for enterprises where data discovery is closely connected to privacy, security, governance, and AI readiness.

#8. Securiti

Securiti is a data and AI security platform that provides capabilities for discovering, classifying, and understanding data across enterprise environments. Its AI-powered discovery capabilities help organizations identify data assets, sensitive information, and relationships across cloud, SaaS, databases, applications, and other connected systems.

Securiti can automatically discover data and apply intelligent classification to help organizations understand what information exists across their environment. This is particularly important as enterprises increasingly use data across analytics, machine learning, and generative AI applications, where teams need visibility into both the data itself and its associated sensitivity and governance requirements.

The platform also connects data discovery with broader data governance and AI governance workflows. This allows organizations to use discovery information to understand where sensitive data exists, how it is being used, and what controls may be required before that information is used in AI or other data-driven applications.

AI Capabilities

• AI-powered data discovery: Helps identify and understand data across complex enterprise environments.
• Intelligent data classification: Uses AI and contextual signals to classify supported data.
• Sensitive-data intelligence: Identifies sensitive and regulated information across connected systems.
• Contextual data understanding: Adds context around discovered data and its relationships.
• AI data mapping: Helps organizations understand where data exists and how it moves.
• Data relationship intelligence: Helps identify connections between data assets and systems.
• AI governance intelligence: Provides discovery context relevant to AI governance and data usage.
• Natural-language interaction: Supports more accessible interaction with data intelligence where available.

What You Can Automate

• Data discovery: Discover data across connected cloud and enterprise environments.
• Data classification: Automatically classify supported data types.
• Sensitive-data discovery: Identify sensitive and regulated information.
• Data mapping: Map where data exists across systems and environments.
• Metadata collection: Gather information about discovered data assets.
• Data inventory: Build a centralized understanding of enterprise data.
• Data relationship analysis: Identify relationships between data assets and systems.
• AI data discovery: Identify data relevant to AI applications and governance workflows.
• Governance workflows: Use discovery and classification information to support broader governance processes.

Best For

Organizations looking for AI-powered data discovery with strong data security, privacy, classification, and AI governance capabilities.

AI Verdict

Securiti’s discovery capabilities are most valuable when organizations need to understand both where data exists and what that data represents from a security, privacy, and governance perspective. Its AI-driven discovery and classification can help reduce the manual effort involved in mapping large data environments. This makes it particularly relevant for enterprises where data discovery is closely connected with responsible AI and data governance requirements.

#9. DataHub

DataHub is an open-source metadata platform that helps organizations discover, catalog, document, and understand data across modern data environments. Originally developed at LinkedIn, DataHub provides a centralized metadata layer that can connect information about datasets, schemas, dashboards, pipelines, ownership, lineage, and other data assets.

DataHub’s AI capabilities are increasingly focused on making metadata easier to interact with and use. Instead of requiring users to navigate through large catalogs manually, AI-assisted experiences can help users search, understand, and interact with metadata using more natural language. This can make data discovery easier for analysts, engineers, and other users who may not know the exact technical names of the assets they need.

Because DataHub is metadata-focused, its discovery capabilities also benefit from relationships between data assets. Users can investigate lineage, ownership, documentation, usage information, and other metadata when evaluating a dataset. This broader context can help teams determine whether a particular asset is appropriate for analytics, machine learning, or AI workloads.

AI Capabilities

• AI-assisted data discovery: Helps users interact with and discover data through metadata.
• Natural-language metadata interaction: Makes it easier to ask questions about available data and metadata.
• AI-assisted documentation: Helps reduce manual effort involved in documenting datasets and other assets.
• Intelligent metadata understanding: Helps users interpret technical metadata and relationships.
• AI-assisted search: Helps users find relevant assets across the metadata environment.
• Contextual data discovery: Combines metadata, lineage, ownership, and other information to provide additional context.
• AI-ready metadata: Provides a structured metadata foundation that can support AI and machine learning workflows.
• Intelligent data relationships: Uses metadata relationships to help users understand how data assets connect.

What You Can Automate

• Metadata ingestion: Collect metadata from supported data sources and systems.
• Data discovery: Search and discover datasets and other data assets through the metadata platform.
• Data documentation: Reduce repetitive documentation work.
• Lineage discovery: Capture and explore relationships between upstream and downstream assets.
• Data ownership discovery: Identify owners and responsible teams associated with assets.
• Metadata management: Centralize and manage metadata across the data environment.
• Data search: Help users locate relevant data assets.
• AI data discovery: Support discovery workflows for analytics, machine learning, and AI projects.
• Metadata-based recommendations: Use available metadata and relationships to help users identify related assets.

Best For

Engineering-led organizations looking for an open-source metadata platform that supports data discovery, cataloging, lineage, documentation, and AI-assisted interaction with metadata.

AI Verdict

DataHub’s biggest advantage for AI data discovery is its metadata foundation and open-source architecture. AI-assisted experiences can make that metadata easier to search and understand, while lineage, ownership, and relationships provide additional context around discovered assets. Organizations should distinguish between DataHub’s core metadata capabilities and its AI-assisted experiences when evaluating it against commercial AI-first data discovery platforms.

#10. Secoda

Secoda is an AI-powered data discovery and catalog platform designed to help teams search, understand, document, and interact with data using natural language. Its AI-first approach focuses on making data discovery more accessible by allowing users to ask questions about their data environment without necessarily knowing the underlying technical structure.

Secoda can connect metadata from data warehouses, databases, BI tools, and other data systems to create a searchable layer across the data environment. AI can then use this metadata to help users discover relevant tables, dashboards, metrics, documentation, and other assets. This can reduce the need for users to manually navigate multiple systems when looking for information.

The platform also uses AI to support documentation and data understanding. Users can interact with the available metadata conversationally, while automated documentation and contextual information can help teams understand discovered assets more quickly. This makes the platform particularly relevant for organizations looking to introduce a more natural-language-driven approach to data discovery.

AI Capabilities

• AI-powered data discovery: Uses AI to help users find relevant data and metadata.
• Natural-language data search: Allows users to describe what they need using conversational language.
• AI data questions: Enables users to ask questions about their data environment using natural language.
• AI-assisted documentation: Helps generate and maintain documentation for data assets.
• Metadata understanding: Uses available metadata to provide context around datasets and other assets.
• Conversational data discovery: Makes catalog information accessible through natural-language interactions.
• Intelligent recommendations: Helps users identify relevant or related data assets.
• AI-assisted data context: Connects metadata and documentation to make discovered assets easier to understand.

What You Can Automate

• Data discovery: Find relevant datasets, dashboards, metrics, and other data assets.
• Natural-language search: Search the data environment using conversational questions.
• Data documentation: Generate and maintain documentation with AI assistance.
• Metadata management: Centralize metadata from connected data sources.
• Data recommendations: Surface relevant data assets based on context.
• Data cataloging: Organize discovered data and metadata into a searchable environment.
• Data questions: Allow users to ask questions about available data and metadata.
• Data understanding: Provide contextual information around discovered assets.
• AI data discovery: Help analysts and business users identify data for analytics and AI projects.

Best For

Data and analytics teams that want an AI-first data discovery experience built around natural-language search, conversational interaction, metadata, and automated documentation.

AI Verdict

Secoda’s main differentiator is its AI-first approach to data discovery and interaction. Natural-language search and conversational capabilities can reduce the technical knowledge required to find and understand data assets. This can be particularly useful for organizations where analysts and business users frequently need to find data but may not know the underlying warehouse structure, table names, or technical metadata.