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What are Data Products?

Data products are reusable assets, services, or applications that use data to facilitate a business outcome for users or organizations. Data products integrate data from source systems, process it, ensure compliance, and make it instantly accessible to authorized consumers.

A data product isolates data consumers from the complexities of data pipelines, making the resulting solution/dataset easily discoverable and accessible as a valuable asset.

Empowering data product owners​

DataOps.live provides the tools, resources, and capabilities that enhance data product creation, management, and optimization.

  • Data producers like data engineers and business analysts follow DataOps principles to ensure efficient and collaborative data development, deployment, and operations. The principles involve continuous integration, version control, automated testing of your data, and streamlined deployment processes.
  • A contract holds the data product properties, attributes, requirements, and metadata, improving communication between producers and consumers. The data product contract provides consumers with essential information and assurance to use the data product.
  • Data product owners can then improve data quality over time and give consumers clear assurance via SLO throughout the product's lifecycle.

In DataOps.live:

  • We emphasize the approach and methodology employed in building data products rather than the specific nature of the product itself.
  • Transitioning from theory to reality underscores our powerful automated DataOps.live platform, ensuring the viability and effectiveness of data products.

Kickstart your journey into data product creation with either DataOps.live Create or building your data products step by step using the DataOps.live data product platform. The former offers a quick onboarding experience with just a few clicks through an intuitive process. At the same time, the latter provides a hands-on approach, allowing you to build each data product component from scratch and manage all types of data products. Explore these two methods of data product development to design solutions tailored to meet the needs of your stakeholders — the consumers.

Data product principles, examples, and value​

Discover the basics of data products including their defining principles, treating data as a product, examples showing their diverse applications, and the value they bring to your organization.

Defining principles​

Data products are built on core principles to ensure they are reliable, scalable, secure, and user-friendly. These principles include manageability, backward compatibility, trustworthiness, interoperability, security, accessibility, and discoverability.

Data as a product​

Treating data as a product is an approach that decentralizes data ownership and promotes sharing based on domains. This approach involves treating data with care and investing in its quality and usefulness, like any other product or service. This method treats data as a valuable asset, managed and delivered to meet different consumers' needs and breaks down silos, encourages collaboration.

Data product examples​

Data products vary widely according to organizational needs. They include data analytics platforms, predictive models, recommendation systems, generative AI, visualization dashboards, and data APIs. These tools aim to extract insights, automate tasks, help decision-making, and enrich customer experiences using data-driven methods.

How to create value for your organization​

Following data product principles brings value to organizations by ensuring reliable, scalable, secure, and usable data products. This ensures accessible data products for better insights and decision-making. It also helps data teams scale efficiently and improve productivity, governance, and user adoption.

Take the next step — build a data product in minutes​

Ready to put these principles into action and start creating impactful data products?

Get hands-on with DataOps.live Create, our data product creator, by following the Analytical Data Product QuickStart documentation.

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