Employing a Data Fabric can elevate an organization's data management processes and drive digital innovation.
Access to quality data is vital to modern business operations, and integrating data from various sources requires intricate strategy, planning, and execution processes. Data Fabrics weave data points from a multitude of different sources into one unified system, simplifying large-scale data integration. Employing a Data Fabric can position an organization to take a value-driven approach to streamline data management and expedite digital transformation.
What is Data Fabric?
Data Fabric is a holistic approach to integrating various data “threads” end-to-end. These ‘threads’ originate in a variety of locations, such as on-premise legacy databases, cloud data repositories, or in application-specific data stores. The Data Fabric weaves these threads together to create a consistent, secure, and unified experience for a user to access data.
Although a Data Fabric is not always a single tool, some companies have begun marketing unified Data Fabric Platforms. A Data Fabric Platform is a consolidated infrastructure that integrates and manages data access in a cohesive, flexible format. Data virtualization tools, such as Denodo, Tibco DV, or JBoss, play a key role in enabling a Data Fabric by facilitating access to data across disparate sources in a unified and consistent manner.
If a commercial Data Fabric Platform is not the right fit for an organization, stakeholders will need to consider several key features of a traditional Data Fabric. Out of the box virtualization platforms will include these features, so those building their own will need to implement a fabric that addresses each of the following: source data discovery and connection, data engineering, data discovery, data access and metadata management.
As companies emphasize the need for data-driven decision making and digital transformation, access to high quality data is increasingly critical.
Why is Data Fabric Important?
As companies emphasize the need for data-driven decision making and digital transformation, access to high quality data is increasingly critical. Even the most robust analytics platforms require consistent access to high-quality data, and integrating that data requires strategy, planning, and complex implementation. Data Lakes are a good solution for most analytics-focused use cases, but they do not solve the greater problem of large-scale data integration.
The wide variety of data analytics applications results in several options for data storage, including file systems, HDFS (Hadoop Distributed File System), Data Streams, RESTful API endpoints, and O/JDBC databases. Implementing a Data Fabric offers a solution to the variability in data sources by weaving the points together into one integrated, consistent access point. Unlike other solutions where data is physically moved, transformed, and loaded into a single source, a Data Fabric keeps data where it resides and “virtualizes” it. This virtualization means that applications can access data through any required interface, but the data is not physically replicated out of its source.
IT organizations within companies are increasingly looking to position themselves as value-drivers. By spearheading Data Fabric initiatives, IT departments take a value-centered approach to data management and solution architecture. Providing mechanisms for flexible, seamless data access elevates IT organizations to valuable and strategic members of their business communities.
Best Practices for Implementing a Data Fabric
First, an organization implementing a Data Fabric should deliver value early and often by collecting feedback from users and owners of the integration points. These stakeholders can provide valuable insight into how the fabric needs to be customized to the wider organization. Teams employing a Data Fabric should keep communication lines open, allowing for continuous feedback even after the initial implementation and throughout ongoing iterations.
Minimize disruption to end users to deliver functionality gradually, in a “crawl-walk-run” fashion. Similarly to the importance of delivering value early, organizations implementing a Data Fabric shouldn’t try to “boil the ocean” with the Data Fabric. Taking a gradual approach will undoubtedly be a new paradigm for data integrators and consumers, ultimately proving that incremental improvements to data products instead of wholesale refactoring is crucial to their success.
Lastly, assemble a steering committee of enabled decision-makers who own the outcomes driven by the Data Fabric. The committee should be planning, building, and navigating the strategic objectives of the implementation. An all-hands commitment to the success and investment of the Data Fabric will ensure the intended outcomes are met by this multifaceted solution.
