Why Effective Data Fabrics Require End-to-End Ecosystem Integration
Follow these three best practices for modern integration to create a solid technology foundation for a cohesive data fabric that connects your entire organization.
- By John Thielens
- September 24, 2021
Data has become critical to providing business value. As a result, a growing number of enterprises are looking to adopt a data fabric architecture. The term data fabric refers to the process of weaving together data from internal silos and external sources to create a network of information to power apps, artificial intelligence (AI), and analytics.
Gartner defines data fabric as a design concept that serves as an integrated layer (fabric) of data and connecting processes. Data fabrics utilize continuous analytics to support the design, deployment, and use of integrated data across hybrid and multicloud platforms. However, it's nearly impossible to achieve a cohesive data fabric without first mastering end-to-end integration across the organization's technology stack.
Recent data shows two-thirds of IT executives agree that automating end-to-end integration processes is their top integration initiative that will drive the most value for their business. Unlike other approaches, a data fabric integrates existing data management systems and applications. For example, with a data fabric, a supply chain leader can add new assets and information to existing data (such as known relationships between suppliers) to detect production delays more rapidly and improve decision making.
For these reasons, data fabrics are quickly becoming viewed as the next step in data integration. However, for customized data fabrics to deliver business value, enterprises must ensure they have a solid technology base with robust data integration.
Following are three best practices your IT team must follow to master integration and power the collaborative, cross-functional workflows necessary for a data fabric.
Best Practice #1: Establish a robust data integration backbone
Because a data fabric spreads across different internal and external platforms, proper ecosystem integration is essential for a cohesive, useful data fabric. A data fabric must be compatible with various data formats because it weaves together data sourced from different platforms. In addition, your data fabric should support all types of users, including IT and business users.
Ecosystem integration plays a key role here. You must orchestrate end-to-end integration with your evolving network of trading partners, applications, suppliers, customers, and marketplaces. By taking a modern, holistic approach to multienterprise integration, your organization will be equipped to drive better business outcomes and value from faster partner and/or application onboarding, business process optimization, and improved agility.
Best Practice #2: Gain full end-to-end visibility for all transactions
Poor integration can also limit transaction visibility if updates and changes are not automatically documented as products and cash move back and forth between partners and customers. These setbacks result in employees spending more time trying to pinpoint the source of transaction errors, which increases the risk of human error and detracts from time these workers could spend doing more productive, revenue-driving activities.
To enable real-time transaction visibility throughout your business network, leverage customized, role-based dashboards with insights that provide context and updates for processes such as ticketing, notifications, and alerts. Role-based dashboards provide your employees with the specific updates pertinent to their roles and duties. This prevents unwanted confusion and unnecessary noise from blocking transaction visibility.
Best Practice #3: Don't balance different integration platforms, the house of cards can topple down
Many organizations' IT strategy for integration is outdated if not broken altogether. Often, these legacy strategies have been built and expanded over many years, evolving in fits and starts as technology progressed to enable new integration capabilities. Due to the relatively stunted expansion of modern integration solutions over recent decades, many enterprises are still managing multiple integration processes with different, siloed solutions. This is an "old school" approach and usually a sign that an organization needs external integration expertise even if it is only on a limited hybrid scale.
Consolidating some or all integrations onto a single, modern platform helps avoid the waste of time and resources caused by having your teams constantly operate different platforms to manage integrations. In addition, a modern, cloud-based user interface will prevent inefficient workflows and operations and curb time-consuming updates to previously existing integrations.
Data Fabrics: The Future of Data Management?
Today, having an agile approach to data management is a top priority for organizations due to the increasingly diverse, distributed, and complex business environment. Although some analysts are calling data fabrics the future of data management, this potential cannot be fully realized without the proper technology infrastructure in place, including integration technology in particular.
With these three best practices for modern integration, enterprises can create a solid technology foundation for a cohesive data fabric that connects the entire organization, however far-flung its business ecosystem extends. Data fabrics enable flexible and cohesive data management, which, in turn, maximizes the value of your enterprise's data and provides significant additional value for partners, customers, and other stakeholders.
About the Author
John Thielens, CTO of Cleo, is responsible for innovation, crafting technology strategy, and architecting enterprise integration solutions to solve complex requirements for multienterprise, cloud, collaboration, mobile, and other integration challenges. John has more than 30 years of experience in the software industry. Prior to joining Cleo, he served as a senior technology leader at Axway, GXS, Inovis, Tumbleweed, and other software technology companies. John holds a mathematics degree from Harvard University. You can contact the author via email or Twitter.