More than ever, the value of data to the business bottom line is patent. However, in most enterprises data is often siloed and fragmented across various complex systems. This poses a daunting challenge, preventing businesses from fully realizing its value.

One of the most important ways to address this challenge is data fabric.

Gartner defines data fabric as a design concept that serves as an integrated layer (fabric) of data and connecting processes. A data fabric utilizes continuous analytics over existing, discoverable and inferenced metadata assets to support the design, deployment and utilization of integrated and reusable data across all environments, including hybrid and multi-cloud platforms.

In practice, a data fabric solution starts with the capability of automatically collecting all forms of metadata, be it technical, business, operational, or social. An organization can build a catalog automatically on the basis of these facts of the data assets. This bottom-up, fact-based approach is essential to a self-defined data fabric with discoverable, trusted, and reusable data.

There are many benefits of such self-defined data fabric. The key ones are:

1. Boosting productivity via self-service

Self-defined data fabric offers data transparency of all your information assets in a visual knowledge graph. It combines cataloging, impact analysis, process dependencies, lineage, quality monitoring, and trust propagation. As a result, you can easily find the data you need and trust, get the insights, and make informed decisions, all in a self-service manner. Productivity is therefore greatly enhanced and operational efficiency increased.

2. Reducing costs with automation

Automation, applied in all the key processes, from data ingestion, cataloging, to analytics, is the foundation of any successful data fabric. Enabled by AI-powered automation, you can do more with less. Not only can you perform some critical tasks which were impossible to do without data fabric, but also you can reduce up to 90% of the manual efforts. That is a tremendous saving of human resources. Now you can easily and automatically identify duplicate data assets. And you can save money by eliminating them and thus reduce the footprint of IT systems used to store and manage them.

3. Complying with regulations through traceability of all your information assets

Data related regulations such as GDPR, BCBS-239, CCPA, and HIPPA all require that you know your data, understand where sensitive data comes from, where it is located, how it flows through various systems, and where it shows in a report. With self-defined data fabric, you can trace information across all your IT landscape, from source to target and throughout the entire data supply chain. You are just a few clicks away to show an auditor what he or she is looking for. Compliance officers can also use data fabric to tie up institutional compliance to laws, policies and/or regulations more easily and systematically as part of the information governance regime.

4. Accelerating modernization with better planning and execution

Modernizing legacy systems by migrating to a new, often cloud-based platform is part of a digital transformation initiative for many enterprises. Data migration in such cases is complex and risky. Data fabric can be leveraged to help the planning and execution of this effort in various ways including:

  • Establish hierarchy of data according to its value and set up migration priorities accordingly.
  • Gain comprehensive view into the current environment. This may entail auto-generated documentation for creating and maintaining a technical asset inventory across multiple platforms. This capability helps organizations estimate time and cost for the migration project.
  • Leverage detailed lineage to determine dependencies, identify and remediate process and data redundancy.
  • Provide impact analysis to enable seamless transition to the desired target state.
  • Identify all sensitive and personal data. Set up policies about such data during the migration process to reduce risk exposure.
  • Enable traceability of critical data elements to ensure comprehensive and proactive governance.

In summary, self-defined data fabric simplifies data management, data governance, data integration, and data consumption. It helps organizations access trusted data more easily, gain insights more quickly, and unlock the value of their data more productively.

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