Data Productization
Data product vs data productization
Data productization creates and uses data products in a meshed structure to ensure data for business entities is always directly available and easily accessible. Taking the complexity out of operational data management and making it simple.
Data Product
Data product corresponds to a dataset that includes one or more business entities, such as customers, employees, assets, suppliers, orders, campaigns, and so on. The data for these entities usually originate in various siloed source systems that are often different in technology, structure, format, and terminology.
Data Productization
Data productization uses data products to ensure that everything the data consumer needs to derive value from the data of a particular business entity is available and accessible. This includes the product's metadata (both static and active), the raw data, lineage to the source systems, collaboration logs of data changes and access controls.
Value of data productization
Data productization allows for a comprehensive 360-degree view of vital entities such as customers, their orders and the most profitable product lines.
With easy access to data products, teams no longer have to waste precious time searching for and processing data into the right format. A no-code model to manage and change the data ultimately avoids creating an architectural mess and eliminates ongoing governance challenges.
Unleash the true potential of your enterprise data
Break down the data silos and turn your data into a network of reusable products that can be easily accessed by other teams and data technologies.
By adopting a meshed data product strategy, your enterprise can quickly see the value of your data investments and set a path towards even greater value in the future. This strategy involves creating high-quality, readily available data products that can be accessed and applied across your organization to solve various business challenges.
See data collaboration in action!
Connected data without the effort, time, and cost of traditional data integration.