Data Discovery
Unlock the Power of Information Retrieval with a connector framework that seamlessly connects to any data source, Including geospatial.
1) Expanded Data Accessibility
Our connector framework enables your system to connect to a wide variety of data sources from geospatial, non-spatial, structured, and unstructured data. Expanding the range of data incorporated into your registry provides valuable insights and information that can drive better decision-making and enhance operational efficiency, leading to improved ROI.
2) Time and Resource Savings
Eliminate the need to build and maintain multiple custom connectors for different data sources. This can save significant time, effort, and resources in developing and maintaining individual connectors, resulting in cost savings and improved ROI.
3) Geospatial Data Types Unlocked
Our advanced connector framework not only enables your system to seamlessly integrate with a diverse array of data sources, including geospatial, non-spatial, structured, and unstructured data, but also sets us apart as the only vendor that supports all geospatial formats.
From raster and vector to elevation data, geospatial web services, maps, and hundreds of other formats, our comprehensive support ensures that every type of geospatial data can be utilized. This unparalleled capability expands the range of data incorporated into your registry, providing deeper insights and more comprehensive information.
These enhancements drive superior decision-making, boost operational efficiency, and significantly improve ROI. With our technology, embrace the power to harness the full potential of geospatial data in one seamless integration.
4) Streamlined Data Integration
A unified and streamlined approach to connect to diverse data sources, including geospatial data simplifies data integration, reduces complexities, and minimizes data silos, leading to improved data integrity and consistency. This streamlined data integration can result in cost savings, reduced data integration efforts, and improved ROI.
5) Enhanced Data Insights
By connecting to diverse data sources you gain a holistic view of your data. This provides deeper insights, correlations, and patterns, leading to enhanced decision-making, improved operational efficiency, and better business outcomes, ultimately contributing to improved ROI.
6) Increased Business Agility
Our connector framework allows you to quickly adapt and connect to new data sources, from geospatial data, office files, document management systems, cloud storage, social media and other emerging technologies and data types. This provides a competitive advantage in today's dynamic business landscape. This increased business agility can lead to quicker insights, faster decision-making, and more agile operations, resulting in improved ROI.
7) Geospatial Data Discovery
Voyager enables geospatial data discovery across distributed repositories, allowing users to locate spatial datasets and understand their geographic context.
Frequently asked questions:
What Is Data Discovery in a Federated Geospatial Environment?
Data discovery is the process of locating relevant records — maps, imagery, documents, and operational data — across multiple disconnected repositories through a single search interface, without first consolidating those repositories into one system. In geospatial and mission environments, discovery must account for spatial location, time, security classification, and format differences simultaneously, since the same event (a flood, a construction site, an incident report) generates evidence across GIS platforms, file shares, and operational databases that do not natively talk to each other.
How Does Voyager Index Data Without Moving or Copying It?
Voyager’s connector layer reads source systems directly and writes normalized metadata and index entries to Voyager’s retrieval layer, while the underlying content stays in its original system of record. Vector data is indexed as geometry plus attributes; raster imagery is indexed by footprint, sensor metadata, and quality metadata rather than full pixel data; documents are indexed with extracted text and metadata. This means Voyager reflects the current state of a source system through incremental connector updates rather than through periodic bulk copies, reducing storage duplication and version drift between the index and the source.
What Catalog Types and Repositories Does Voyager Connect To?
Repository Type
Examples
Enterprise GIS
ArcGIS Enterprise, ArcGIS Online, geodatabases, feature and imagery services
Cloud object storage
Amazon S3, Azure Blob
Collaboration platforms
SharePoint document libraries
Databases
Relational databases holding operational or asset records
Web services
REST services, OGC-compliant map and feature services
Because Esri products are frequently the system of record for authoritative geospatial content in government and infrastructure environments, Voyager is built to sit alongside ArcGIS deployments rather than replace them, surfacing ArcGIS content alongside file shares, imagery archives, and databases in one query.
How Do Search Results Show Metadata, Lineage, and Permissions?
Each result returned by Voyager includes citation and source linking back to the originating record, document, or service, along with provenance information describing where the data came from and how it was retrieved. Confidence and relevance signals accompany results so users can evaluate why a given record was surfaced. Access permissions inherited from the source system are enforced at query time, meaning a search result set is automatically filtered to what the requesting user is authorized to see — permissions are not a separate lookup step performed after retrieval.
What Does Cross-Repository Discovery Look Like in Practice?
Consider a disaster response scenario: a flooding event generates flood-extent data in a GIS platform, road-closure records in a transportation database, asset inventories in an operational system, and situation reports as PDF documents in a file share. An analyst querying across these disconnected stores in one search receives a combined result set — flood extent boundaries, intersecting road closures, nearby asset availability, and relevant situation reports — with each result tied back to its source system and citation. In a broader deployment spanning a dozen or more disconnected agency data stores, this same query pattern applies: the analyst does not need to know which system holds which data type, only what question they are trying to answer.
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