Some examples of data products are datasets, data streams, data feeds, or APIs; code or data models; analytics models; and dashboards.
Dashboards that provide useful metrics or easily understandable types of data visualization — like the Google Analytics dashboard or many of the digital products empowered by Tableau — are good examples of the use of data products as mentioned above, as they empower even non-technical users to gain valuable insights via subsequent data analytics.
Benefits of Data Products
Organizations that adopt a data mesh approach to data management and build high-quality data products see improved efficiency, collaboration, and data democratization, and their product and data teams are generally better informed as to the value and end use of data.
Teams that use data products spend less time searching for data, ensuring data quality, or building new data pipelines, and those time savings become significant when added up across your data ecosystem and lifecycle.
Additionally, data products speed time to insight because they can be reused and repurposed, increase trust in your organizations’ data, and provide real-time data for in-the-moment decision-making.
How to Create Data Products
At data.world, we’ve spent a lot of time thinking about the best way to go about deploying data products, and we call our method ‘The Data Product ABCs framework.’
This framework provides insight into the types usa whatsapp number data of questions data leaders should be asking when developing data products. These include questions about:
Accountability – e.g., “Who’s responsible for this data?”
Boundaries – e.g., “What is the data?”
Contracts and Expectations – e.g., “What are the sharing agreements, consented uses, and policies?”
Downstream Consumers – e.g., “Who are the current consumers?”
Explicit Knowledge – e.g., “What is the meaning?”
Additionally, domain teams must maintain a consistent and usable interface to their data. Consumers should agree on the style of the interface as it pertains to their needs: well-defined tabular structure, API endpoint, SQL or SPARQL interface, Parquet, Graph, etc.
What’s most important, regardless of the interface, is that the semantics – underlying logic – of the data products are the same. This includes keeping the Contracts and Expectations in place and notifying producers and consumers if something goes wrong.
Future of Data Products
As a critical aspect of a data mesh approach to data governance, data products empower organizations of all kinds to leverage data to achieve business success. And as data mesh itself becomes a more commonly accepted best practice for enterprise data management, the ubiquity of data products is sure to increase.
If you’re interested in adopting the advantages of treating data as a product for your enterprise business, download the Data Product ABCs Worksheet.