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Data products are well-governed, reusable, and accessible data assets designed to power specific business use cases. They ensure data quality, usability, and availability for their intended consumers, typically within either operational or analytical environments.
Unlike raw datasets, a data product is purpose-built with governance, structure, and maintenance to ensure it is reliable and ready for use within an organization. By utilizing data products, companies can streamline access, enhance data quality, and drive innovation.
As businesses evolve and face an increasing need to respond to changes and opportunities in real time, a new way of envisioning how data products can be leveraged is emerging: universal data products.
The concept of universal data products introduces the possibility for data products to function as highly interoperable data assets, designed to serve multiple business domains, teams, and applications across both analytical and operational environments.
In their ideal state, universal data products are optimized via data contracts to ensure real-time availability and usability for a multitude of use cases across the entire organization with the assistance of a data streaming platform.
Data products that are designed to be universally implemented help organizations move beyond siloed, batch-based data management to a more agile and efficient approach. They enable data practitioners—such as data engineers, analysts, and application developers—to work with consistent, high-quality data that is readily available for operational and analytical use cases.
No, these terms are not interchangeable. While data products refer to specific, structured data assets designed for operational and analytical use, data as a product is a broader mindset that treats data as a valuable, marketable asset, similar to traditional business products.
Much like any other product, data products should be easy to access and use. Some essential features of a data product include:
Self-Service Data products should be designed so that users can confidently find, use, and share data without needing to coordinate with other teams—improving efficiency and collaboration. That requires setting clear responsibilities for data owners, data engineers, and the data platform team.
Interoperability Data products should be readily compatible with other related data products. Using common identifiers (such as accountId, userId, or productId)—governed by well-defined schemas —across data products enables seamless correlation and joining of data products.
Security & Compliance Components such as field-level encryption, encryption at rest, and role-based access controls (RBAC) are essential to protect sensitive data and ensure proper access management.
Legal Compliance Data products must adhere to legal and regulatory requirements, including GDPR compliance, the right to be forgotten, and ensuring data is stored within designated jurisdictions.
The concept of universal data products introduces the possibility for data products to function as highly interoperable data assets, designed to serve multiple business domains, teams, and applications across both analytical and operational environments.
In their ideal state, universal data products are optimized via data contracts to ensure real-time availability and usability for a multitude of use cases across the entire organization with the assistance of a data streaming platform.
Building data products ensures that teams across your organization can accelerate and enhance decision-making, reduce redundant data processing, and much more. Here are some of the key advantages of solving your biggest data challenges with a universal data product approach:
Reliable and easily accessible data products enable faster insights that can be applied across a multitude of use cases like fraud detection and dynamic pricing.
Well-defined data products provide a standardized way for different teams to access and use data without complex integrations.
Streamlining the creation of data products while reducing redundant pipelines and complex data wrangling enables organizations to lower costs and improve data governance.
A robust data product strategy allows businesses to scale new applications and data-driven services efficiently.
Remember: For data products to be effective, they must be well-governed, reusable, and easily accessible. Without these characteristics, data products become another form of data silos, limiting their impact and usability across the organization. Confluent helps organizations build universal data products that put the focus back on innovation over break-fix data management. Step one: Setting your data in motion with data streaming.
Many organizations struggle with a fragmented data landscape—a “data mess” with data locked in silos, inconsistent governance, and slow, batch-based data processing. These challenges make it difficult to create reliable, real-time data products universal data products that support business needs in real time. Without a unified approach, teams waste time wrangling data instead of getting value from it.
A data streaming platform unifies disparate data sources, enabling organizations to build reliable, real-time data products at scale. By moving from batch-based data processing to an event-driven approach using a managed platform for Apache Kafka®, businesses ensure their data products are fueled by real-time data and universally available for use cases across both analytical and operational environments. This shift reduces delays and ensures that data remains fresh, relevant, and ready for immediate use in decision-making by data practitioners and analysts alike.
For data practitioners, a data streaming platform offers several key benefits. It simplifies the integration of various data sources, eliminates data silos, and ensures a continuous flow of high-quality data. This empowers teams to focus on sourcing actionable insights, building applications, and driving innovation, rather than spending time on complex data wrangling and management. As a result, organizations can adapt to change faster, make data-driven decisions in real time, and optimize business processes with greater agility.
For analysts, a data streaming platform ensures access to real-time, high-quality data, eliminating the need to work with outdated or incomplete datasets. With continuously updated insights, analysts can detect trends as they emerge, refine forecasts with greater accuracy, and respond to business needs instantly. This enables more proactive, data-driven decision-making across the organization.
Organizations across industries use universal data products to unlock new efficiencies and competitive advantages. Common use cases include:
Connect data products for customers, accounts, web logins, and transactions to create a 360° view of every touchpoint in a customer account that can be used to create dynamic threat scores and prevent fraudulent activity.
Bring together data products for vehicle telemetry, drivers, customers, and repairs to streamline how fleets deliver essential services – from route optimization, to preemptive maintenance checks, and everything in between.
Mix and match data products such as customers, inventory, purchases, and web clickstreams, to get a holistic view of customer purchase patterns and build a loyalty program that rewards repeat business.
Leverage data products for application performance and infrastructure health by ingesting logs and metrics in real time, allowing IT teams to proactively address issues and ensure system reliability.
As businesses increasingly rely on real-time data, the role of data products will continue to expand. Companies that prioritize a scalable universal data products—powered by a data streaming platform—will gain a significant competitive edge, improving efficiency, customer experiences, and long-term innovation.
Learn how the Confluent data streaming platform helps companies unlock everything their data can do with universal data products.