ManageData Products
Your entire data product lifecyclein a single view
Your One Data home page simplifies the data product lifecycle with a Kanban-style management system and ensures high data quality with automated checks and alerts. Monitor and track data lineage from source to report, improving trust while increasing collaboration and efficiency across teams and with external partners.
Manage the data product lifecycle,Kanban-style
The One Data home page is a Kanban-style board that gives you an easy-to-use and comprehensive view of data products at any stage of the development process. You can see what data products have been requested, what’s in development, and what’s already done.
This allows you to manage the entire data product lifecycle from one central location.
Access data productsfor you use cases
The Data Product Marketplace is designed for cross-functional collaboration. That’s why it’s most effective when it’s accessible to all your departments. Everyone can easily share, access, and use data products within the Marketplace. Working with external partners? No problem. You can grant them access to facilitate greater collaboration and meet business needs.
Data products are designed to be re-purposed to support multiple use cases, so you can scale data usage, gain advanced insights, and make informed decisions. Unlock powerful productivity and potential from your data by reusing and iterating on data products available in your Data Product Marketplace.
Not seeing the data product? Simply request a new data product and get the development process started.
Set the record straightwith data contracts
Data contracts in One Data strengthen collaboration, simplify data governance, and foster trust by offering transparency and accountability.
Through clear definitions and specific building blocks like data ownership, schema types, and data freshness, data contracts facilitate collaboration and align expectations, especially with the involvement of stakeholders like data governance teams and asset managers. Data contracts in One Data are aligned with Open Data Contract Standard (ODCS).
Keep data quality highwith automation
Solve one of your data team’s biggest challenges: ensuring data quality. You can apply automated and customized data quality checks. Use automated tagging and column labeling to make sure your data is consistent, safe, and accurate. Run quality checks across multiple data assets or for individual data products manually or fully automatically, depending on your specific needs. With One Data, it’s easy to validate that your data products are ready for use, ensuring that they are secure, reliable, and sustainable.
Get quality alerts on your scheduleand eliminate errors
Quality Alerts in One Data allow you to schedule automated alerts to ensure that your data meets the quality standards you define. The alerts help you pinpoint data products that deviate from these standards. In addition, data lineage capabilities trace problems back to their source, so you can quickly identify and correct errors where they occur.
One Data’s data quality capabilities ensure that only high-quality, ready-to-use data products are published to your Data Product Marketplace. Here, approved users can easily access them to generate insights based on their needs. Share your data products with other business tools, such as Tableau or Power BI, for maximum impact. Or share with your external partners to foster collaboration and better meet business needs.
Monitor and track data lineageacross systems
Visualizing the end-to-end data lineage from source to data product helps users understand how data products are developed. This becomes a reliable solution for tracing and pinpointing the source of errors when quality issues arise. Cross-system lineage tracking gives you a complete view of the data products used in production across your entire ecosystem, from source to report.
You gain visibility into data lineage and usage, facilitate rapid error identification and resolution, and enable active metadata enrichment. Accurately map data relationships and dependencies, ultimately creating more reliable and valuable data products down to the column.
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