Data Product Lifecycle Management
QuantHub offers a full-stack solution for organizations that create data products, such as statistical agencies or any other entities that collect, process, and periodically publish large datasets.
With powerful QuantHub tools, you can create data products with minimal effort and effectively support them throughout the entire lifecycle. Automation tools let you optimize the work of data owners and analysts by avoiding repetitive manual and error-prone tasks.
Collect, Process, and Disseminate Data
Data Modeling
QuantHub natively supports and uses the full power of the SDMX 3.0 information model to structure artifacts for metadata and definitions of your data product.
With our data management tools, you can seamlessly map raw input data onto a structured dataset, described by an SDMX data model, and then transform it into a resulting dataset specified by a statistical compilation methodology.
Data Collection and Warehousing
QuantHub features no-code powerful tools to collect your data regardless of its origin or format using pluggable data connectors. We can integrate with literally any data source and handle your bulk data ingestions in a configurable and automated fashion. A sophisticated survey management tool allows for building and collecting structured, formatted, and ready-to-use data from individual surveys all way to nationwide polls.
Store your datasets in a secure repository meeting the best industry-standard data protection practices and accessible via open API by external systems. We natively support end-to-end data encryption and securely handle PII data. Govern your data and ensure its compliance and integrity with bi-temporality, entitlements, reporting and other powerful features.
Data Transformation
QuantHub offers powerful features out of the box to perform digital transformations of data products starting from basic calculations all the way to complex statistical and mathematical functions.
Visit Data Engineering to learn more.
Data Dissemination
Distribute your data to the target audience either via SDMX open API or create data-driven Portals. We also offer integrations with third-party services such services as Jupyter and Power BI to build and share sophisticated data analytics.
Visit Dissemination to learn more.
Manage Data Product
Quality Assurance
Product owners can run data and metadata quality checks at any step in a data product lifecycle, by creating and assigning validation reports that update automatically when new data becomes available and require sign-off to continue the data product release process.
Compliance and Versioning
Fully compliant with SDMX 3.0 specification, QuantHub ensures data lineage by supporting versioning of system artifacts and tracing of referential dependencies. Data lineage explains data alteration in terms of changes to system artifacts used to generate this data.
Versioning allows changes to system artifacts not to have an impact on the existing data product generation lifecycle until it is explicitly needed. Data product owners have the flexibility to decide when to incorporate changes to system artifacts into their data product lifecycle.
Collaboration and Maintenance
The creation and maintenance of data products require teamwork on one hand and autonomy to make changes on the other. QuantHub Workspaces support various team roles with different access rights.
This allows us to automatically isolate changes made by any team member, then test and post them to QuantHub Global Catalog for the benefit of other teams, who may decide to merge these changes into their own data product generation process.
