A data fabric is a design concept. As we explored in our previous blog, the current data landscape is one of multi-cloud environments with data trapped in data silos. A data fabric is a new approach to data governance, privacy, and compliance, that starts with the assumption that data will not always be centralized. It utilizes machine learning, virtualization, metatdata management and automated data cataloging to connect disparate data sources, regardless of their location. It allows data to be queried where it resides, removing the need to move or duplicate data.
Let us take a closer look at the three key benefits of a data fabric.
Automated governance, data protection and compliance
A data fabric provides an automated governance layer that allows for a centralized definition of polices, the deployment and enforcement of which can then be automated globally. This layer helps to reduce compliance and regulatory risks. By maintaining governance regulations in a single location (as opposed to individual teams applying regulations to their own data) they are easier to manage and maintain. Since they are centralized, the regulations can be updated quickly and easily. This minimizes risk and improves compliance. Updates are automatically applied to the business data assets. In addition, data fabric users can benefit from a higher level of automation. AI and machine learning can be used to automatically extra data governance rules from regulatory documents, providing additional time savings and ensuring data is constantly compliant.
Enable self-service data consumption and collaboration
With the centralized definition of governance and privacy policies established, data consumers have trusted data. An AI-augmented data catalog allows business users to understand and locate the data they need. These elements combine to deliver governed trusted data to those who need it. This self-service access to data supports collaboration as well as providing the platform for better decision-making.
Automate the integration of data
Almost all data access or delivery process can be automated, removing the need for repetitive coding. Machine learning can automate data discovery, leading to faster time-to-value.
Automated governance, automated integration of data, and self-service data consumption all come together in a data fabric and enable organizations to reduce both the cost and risks of data management.