Robust data management is important for any data-driven organization. But for compliance-conscious organizations (e.g., those in finance or healthcare), it is now bordering on essential.
For this reason, and in these industries, federated data is gaining traction as teams look to bolster their ability to manage and maintain high-quality data at scale.
But what about federated data specifically is making this so? What difference is it actually making in highly compliant, data-driven industries? And will data teams in other industries soon see the federation of their data as a solid option or a serious need?
Let's find out.
What is federated data?
Federated data in data engineering refers to a system that provides unified access to data spread across multiple, often diverse sources without requiring the physical movement or consolidation of that data into a central repository.
Instead, data federation relies on a virtual layer that connects and abstracts these sources, allowing engineering teams and data consumers to work with a unified view of their data—using it just as if it were housed in a single, cohesive system.
This approach enables organizations that rely on high data quality to leverage comprehensive insights while maintaining control over data location, governance, and compliance. For these reasons, data leaders are turning to federated data as an adaptable solution for today’s complex, data-driven environments.
How does federated data work?
The ability to provide unified access and control involving differing data sources (without any need for data consolidation) relies on four key components acting in concert. Let’s walk through them.
Data fabric
Within an organization's overall data environment, the data fabric establishes a singular overarching operational infrastructure. This is important, as even simple data environments (by today's standards) are composed of multiple ecosystems, such as on-premises servers, cloud platforms, and hybrid setups.
Each of these ecosystems, in turn, can host a variety of data sources, including data lakes, warehouses, IoT devices, and enterprise resource planning (ERP) systems. The operative role of the data fabric is to integrate and manage these ecosystems and their underlying data sources, ensuring secure and seamless data integration and comprehensive data governance across the data environment as a whole.
A federation layer
Specific software operating within the data fabric framework introduces a federation layer that acts as a query engine, breaking down user queries into subqueries tailored for each data source within the data fabric.
This engine retrieves the results from these subqueries in real time, aggregating them into a unified response. By doing so, the federation layer ensures that users can interact with distributed datasets as if they were part of a single system.
Data virtualization layers
Typically, this same software will also add abstraction layers above each distributed data source in the data environment. These technical mechanisms (i.e., virtualization layers) first translate each federation query into source-specific formats before abstracting the response back to the federation layer in a standardized format.
As a result, users can access and query data through the federation layer without moving or duplicating the actual data from its source.
Metadata management
Finally, as all this takes place, the data fabric's metadata management maintains consistency while sourcing and using the federated data within an organization.
This is key because the metadata repositories store information about each dataset's structure, format, and governance policies, enabling the federation engine to execute queries accurately across various sources with different schemas.
Federated vs. centralized data management: 5 reasons data federation comes out on top
Now that we’ve discussed how federated data typically works within an organization, we can stress the strategic advantages it has over more traditional, centralized data management.
1. Superior strategic flexibility and scalability
Data leaders and data-adjacent stakeholders (e.g., chief data officers, VPs of data, chief data and analytics officers) face significant challenges in effectively managing the growth and complexity of data assets across multiple ecosystems.
For these reasons, centralized data management practices struggle to keep pace as this growth increases the resources needed to shepherd organizational data into a single location (e.g., a data warehouse or lake). Even for organizations that can afford the ongoing investment to do this, the traditionally centralized approach can’t, by design, accommodate new data sources with the flexibility today’s data-driven organizations require.
Alternately, the flexibility and out-of-the-box agility to scale that’s inherent in data federation is ideal for data leaders, as it provides the adaptability they need to evolve data sources and business needs without additional investment or restructuring of infrastructure.
2. Enhanced compliance, governance, and data security
Compliance-focused executives often struggle with the complex regulatory requirements of enterprise data, which dictate how organizations must store and access their data. And, here, too, the consolidation of information in centralized data management practices can cause issues. Centralization of data creates accessibility challenges (i.e., who may access data, when, and why) and heightens risk exposure, especially when data must travel from region to region or transcontinentally. Moreover, data consolidation in this manner relies on strict, resource-intensive security measures that are robust enough to protect sensitive information, especially for financial and healthcare organizations, where common use cases include real-time analytics, regulatory reporting, and cross-functional data collaboration.
By contrast, data federation allows organizations to keep data in its original location, simplifying compliance by enabling control over data location. This decentralized approach supports more agile, region-specific compliance with regulations like GDPR and HIPAA while still providing (if not improving) accessibility or flexibility.
3. Improved cross-functional data collaboration
The data needs of some organizations are heavily cross-departmental. Within the walls of these large yet dynamic businesses (think Coca-Cola, Netflix, or Amazon), fostering effective data collaboration is a must for operational alignment and productivity.
Here again, centralized models fail to deliver in the world of big data. In these environments, centralized models rely on data flowing through a central data team, which creates bottlenecks in managing access control, as teams must wait for permissions to access consolidated datasets. This centralization also reduces agility as other departments and end users wait for the team to move, prepare, and organize data.
Contrast this with a federated data model that, by enabling decentralized data access, allows departments like finance, marketing, and operations to interact with the data they need in real time without depending on a central team to manage it.
This structure supports a more collaborative and self-sufficient approach to data use, empowering departments with the Netflixes and Amazons of the world to work more effectively while aligning with organizational governance standards.
4. Faster time-to-insight for business agility
Organizations are regaining agility and flexibility while staying compliant, turning this capability into a competitive advantage. By generating and acting on real-time insights faster and more decisively, they enhance the utility of their data environments. Unfortunately, centralized systems stumble and fail when tasked with performing in this way.
Despite everyone's best efforts, time-intensive ETL pipeline processes may create unacceptable delays by routing data through multiple stages of transformation before it reaches its central repository for use.
For financial organizations, data delays can cost millions of dollars by compromising leadership's ability to make effective decisions. This erosion of decision-making impacts customer satisfaction and complicates efforts to remain compliant. In healthcare, these delays can be even more dire, potentially costing lives as they disrupt operational efficiency in care facilities, hinder patient care, and slow the delivery of critical health insights to those who need them most.
In these high-stakes environments, federated data systems free organizations to work at the speed of innovation by reducing latency and accelerating time-to-insight, which empowers teams to act quickly and make data-informed decisions.
5. Optimized costs and resource allocation
Finally, every leader in modern organizations is responsible for justifying budgets and resource allocation, especially as data volumes continue to grow.
Centralized data management requires significant investments in infrastructure for storage, processing, and security, as well as additional costs for ETL operations to consolidate data. When data sources multiply, as they so commonly do, these expenses naturally increase, leading to duplicated storage and higher operational costs.
In contrast, data federation reduces the need for extensive infrastructure as it allows data to remain in its original location. By minimizing data duplication and streamlining resource allocation, federated data creates efficiencies that drive cost savings, which is a valuable benefit for any executive looking to optimize their technology budgets. That is, all data executives, everywhere.
Functionality, flexibility, and future-forward thinking with Gable.ai
Federated systems are just one example of how industry leaders are embracing smarter ways to keep their companies agile and intelligent, as opposed to simply throwing more time, energy, and resources at the event horizon of big data.
While this is imperative for data leaders in industries like finance and healthcare, leaders in every industry should consider how the right strategic investments upstream can have exponentially impactful results as they ripple downstream.
For more about this exact type of thinking, make sure to sign up for our product waitlist at Gable.ai, because while data federation optimizes the use of your data, we’re engineering a better way to ensure that your data delivers.
