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Modernize Data Management with Data Fabrics
Modernize Data Management with Data Fabrics

November 24, 2022

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As a strategic asset, data has become integral to businesses across industries and geographies. While there is a plethora of data available, it is pivotal for firms to consolidate and simplify its management to ensure its most effective use. This is where data fabrics come into play. Just as a physically woven fabric brings together many threads, data fabrics integrate data through intelligent and automated systems, driving significant value for global capability centers.

While managing data, organizations often unintentionally create data silos. As the number of data silos and data volumes grow, it can lead to increased complexities. Data fabrics unlock the full potential of data by enabling automated data integration, embedded governance, and self-service data consumption in a way that siloed repositories do not. Through data fabrics, organizations can speed availability of data, making data available on demand for self-service while increasing agility, security, and productivity for data engineers, data scientists, and business analysts. Benefits of a data fabric approach include:

Eliminating Data Replications: The best way to simplify and govern data is to have less of it. A different version of a customer can exist in the source system, a data lake, a data mart, or a cloud implementation, making it difficult to maintain a single version of truth. Data fabrics help eliminate this by leveraging data virtualization technology to create virtual data views on top of existing data repositories, establishing a single authoritative virtual data source.

Centralizing Data Governance and Lineage: When data is distributed across systems, organizations need to control its access. When centralized data governance and lineage enabled data is brought together by data fabrics, it helps users understand the meaning of data, its origin, and its relationship with other assets.

Securing Data: With a data fabric approach, data security policies can be applied on semantic objects/tags instead of through a traditional database views method, meaning data security policy and data repositories can be decoupled. This makes it significantly faster and easier to update information as a single personally identifiable information (PII) tag can be linked to multiple related PII tables, allowing users to make changes to a single policy for the entire organization. This makes it possible to enforce the same security policies uniformly to both cloud and on-premises environments.

Enriching Metadata: The relationship between metadata is depicted through knowledge graphs, which are consistently refined by built-in metadata engines that leverage usage-based statistics from different environments. This enriches and activates the metadata, aiding in easy discoverability of new data sources and data elements. Thus, the system continuously adapts to changes introduced in the environment through the addition of new data sources or changes in business teams' usage patterns.

With the help of a comprehensive data fabric strategy, firms can build a unified data delivery and self-service platform that solves today’s most complex data management problems, thus providing actionable insights through real-time data integration at a lower cost with higher agility. The result is the ability to leverage the power of data to deliver impactful insights for clients and meaningful innovations for users.

Author: 

Susnata Singh

Director - Data Architecture,  Global Services, Fiserv


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