What Are Some of the Challenges Associated With Data Fabric?

Data management is more important than ever for companies of any size and in any sector to give themselves a leg up on competitors. With more data sources impacting decisions than ever before, it’s important to have an infrastructure in place that can handle the demands and deliver the analytics needed to strengthen a business.

Data fabric architecture can help create this competitive advantage, but not without some potential challenges that could sneak up in developing the right structure for your company’s needs.

Understanding Data Fabric

When dealing with all types of data, some business users may not know where to start, and a data fabric system could be the answer. So, what is data fabric? It’s an end-to-end data integration and management solution that consists of architecture and software to handle all data types.

Data fabric architecture is intended to provide a unified, consistent user experience and access to information for any member of an organization at any level, with confidence in the reliability of that data.

Data fabric is designed to help organizations of all sizes solve complex data problems and use cases by managing the various kinds of applications, platforms, and locations where the data is stored. Data fabric architecture enables frictionless access and data sharing in a distributed data environment.

Data fabric is designed and intended to be present wherever an organization’s users are. The business user can be at any place worldwide in this fabric and still access data at any other location without any constraints, in real time and securely.

Implementation of Data Fabric

Data fabric begins with online transaction processing concepts. In this kind of processing, detailed information about every transaction is inserted and uploaded into a data center. The data is structured, cleaned, and stored in silos for further usage.

The purpose of data fabric is to take raw data and derive multiple findings. While this is a great long-term solution for business processes, it is not a quick fix, and there is some time and effort that has to go into helping organizations of all sizes leverage their data to grow, adapt, and improve.

Successful implementation of data fabric requires the development of applications and graphical user interfaces to establish a secure data level with customers to interact with organizations. Data fabric architecture creates a necessary ecosystem for gathering and storing data, with an emphasis on security from all sources to manage properly.

A data management system allows for easier and more secure access in an efficient manner with an allowance to scale when required. The transport of this information from data discovery to analytics can deliver greater insight into business value and the discovery of new solutions.

Data Fabric vs. Data Virtualization

Data fabric often gets confused with data virtualization. Data virtualization creates an abstract layer and is often relied on when you need to integrate data quickly and efficiently.

Virtualization connects through a variety of data sources, whether through on-premises databases or cloud technologies. This serves to build up a structure to create real-time insights. On the other hand, data fabric refers to an overarching data management architecture used for broader use cases.

Analysts recommend using data virtualization as one tool that contributes to your data fabric architecture. As you utilize more and more data integration tools, you can grow your solution into a data fabric that’s specific to your organization’s goals.

Ultimately, a data fabric solution takes time to remove a business of any size from its status quo and achieve advanced analytics. Diverse data, from a variety of platforms, can seem overwhelming for data analysts to have to address. However, carrying over from legacy systems into the future shows that data fabric can be a game-changer.