Companies that treat data like a product can reduce the time it takes to implement it in new use cases by as much as 90%, decrease their total cost of ownership (technology, development, and maintenance costs) by up to 30%, and reduce their risk and data governance burden.
Enable informed decision-making and strategic initiatives with curated data products
By organizing various data elements—raw data, transformations, data quality rules, contracts, access patterns, and infrastructure—into a single trusted cohesive unit, raw data can be refined and shaped to precisely align with the specific requirements and objectives of the business.
Get data that is reliable, compliant, and transparent
Detailed quality metrics at the data product and dataset levels ensure that users can rely on accurate insights. Data products safeguard proper data handling and privacy protection, allowing organizations to confidently use them in a compliant manner. Detailed lineage creates transparent trust and understanding about the provenance and data curation.
Close the gap between data producers and consumers
Data products bridge the expectation mismatch between data producers and consumers, by aligning domain-specific assets that producers create and consumers want.
With data products, there is clear ownership between data producers and consumers, increasing adoption and accountability, reducing costs, and accelerating time-to-market for data-driven solutions.
From Datasets to Data Products
Examples of Data Products from popular data sources
Why it matters?
Turn Snowflake datasets into domain-specific, trusted data products to maximize value. Optimize data quality with Snowflake’s native push-down features – without moving data. Easily launch Qlik Sense analytics apps from Snowflake backed data products.
Why it matters?
Use pre-built analytics content and integrate data from SAP and other systems for multi-source insights. Transform SAP data into data products for use cases like Quote-to-Cash, and inventory. Get high-quality data insights to drive decision-making.
Why it matters?
Reusable, domain-specific QVD-based data products to maximize efficiency. Control QVD sprawl and governance with ownership, lineage, and pull-up data quality. Bundle QVDs, scripts and model files in a data product to speed-up analytics app development.
Take advantage of diverse, trustworthy, and discoverable data for GenAI development
Data products provide essential metadata and curated datasets that enhance RAG-based applications. Getting diverse, timely, accurate, secure, discoverable, and easily consumable data for machines from data product helps to guarantee that AI outcomes are relevant and reliable.