What Is Data Fabric?
Data fabric, which is gaining traction in the market, refers to an agreed-upon process that integrates an organization’s data, processes, analytics, and more into a unified, interconnected architecture. Data fabric standardizes data governance practices across on-prem and cloud environments — including hybrid and multi-cloud.
Purpose of Data Fabric
Up to 68% of the average organization’s data is not analyzed, according to IBM — and up to 82% of organizations run into integration problems due to siloed data and different data types coming from various sources. This spells trouble for data-driven organizations.
With a cohesive and coherent data fabric, enterprises can better manage their data and utilize an integrated data, metadata, and analytics infrastructure — and ultimately unleash maximum value from their data.
Benefits of Data Fabric
“Fabric” refers to the integrated layer of data and connecting processes across all data environments — including hybrid and multi-cloud platforms.
By weaving together continuous analytics, automated technologies, AI models, and machine learning across complex data environments, enterprises can boost data trust, make better decisions, and drive digital transformation. Here are a few ways how:
Business Insights: A unified infrastructure allows for better data visibility — and insights that come from that visibility.
Security and Cost: A data fabric enables businesses to better protect their data and reduce cost from maintaining and managing data — particularly in multi-cloud environments.
Data Fabric Vs. Data Mesh
Data mesh describes another data management process that is often confused with data fabric but tackles the problem of distributed data differently. While data fabric takes a universal interconnectivity approach — weaving together a continuous, unified infrastructure for data management — data mesh is a centrally created architecture for use across distributed data silos. Data mesh does not necessarily act on the issue of interoperability, however.
Ultimately, both approaches make data more accessible and secure, but data fabric alone focuses on a holistic, interactive architecture.
Relationship Between Data Fabric and Data Integration
A data fabric depends on automated, AI-driven integration that improves over time. An effective fabric automates multiple integration styles, scales data management across an enterprise, reduces storage costs, and maximizes performance. The resulting architecture:
- makes difficult-to-locate data easily accessible in multi-cloud and hybrid environments
- eliminates data silos
- eliminates multiple and manual tools
- future-proofs data management practices, as new sources are added
Data Fabric Use Cases
Building a data fabric for your organization helps:
- increase interoperability efforts
- boost security by integrating IT systems
- create unified views of customer data, driving better decisions and customer marketing
- improve targeted marketing
BigID and Your Enterprise Data Fabric — How It Works
BigID introduces an ML-driven, semantic approach to enabling a data fabric for your organization. Here are just a few ways that BigID can help your enterprise future-proof digital transformation and data management practices by building a seamless data fabric.
Cover all your data — everywhere: Automatically connect to all data types — including structured and unstructured data — in on-prem, multi-cloud, and hybrid environments.
Classify data based on deep learning: BigID specializes in classification methods that go beyond pattern-based discovery. Automatically classify more types of data with NLP and NER — and AI insight based on deep learning.
Exchange and share data: Enable collaboration and data sharing among employees in real time.
Add context to data: Layer technical, business, and operational metadata to see data attributes and relationships.
Leverage active metadata for better interoperability: With ML-augmented metadata, gain insights from your metadata, empower your organization to take action on it, and drive better business decisions.