Modern Data fabric architecture

https://lumendata.com/blog/understanding-modern-data-fabric-architecture-and-best-practices-to-modernize-enterprise-data/

Modern Data fabric architecture consolidates knowledge graphs, AI, and metadata capabilities to enable data integration and ensure consistent access and exchange of data across the organization. This blog is in continuation to our previous blog on understanding data fabric and outlines the following points:

• How is data fabric architecture applicable to businesses across domains?
• What are its components?
• Best practices.

Data fabric architecture is an industry-agnostic concept which means it is relevant across domains and can help achieve-

• Enterprise intelligence- a birds-eye view of organizational performance with the help of intuitive tools.
• Operational intelligence- shift from scheduled to requirement-based maintenance (still pro-active) activities for key operations.
• Complete focus on obtaining a 360-degree view of customers by tracking customer activities in customer-touch points.
• Regulatory compliance using AI-enabled data governance policy enforcement, automatic classification of data assets, sensitive data detection, and masking of data.
• Transforming data fabric into an internal search system for relevant access to authorized parties.

Data fabric architecture is more than just a methodology for managing the existing data environment; let us understand its core components first.

Building blocks of Data fabric architecture –

1. Data management practice first defines stakeholders who can access business information; how much of it can be drilled down to detail, how often it would be updated, and details of what will be masked and encrypted and transformation efforts. Knowledge graphs and AI-powered metadata activation allow for unified data governance ensuring the safety and accuracy of organizational data.
2. Data ingestion enables users to connect to all types of business information regardless of its localization and volume allowing combining multiple data types. Video recordings from brick-and-mortar stores with online financial transactions as well as aggregate information in streams or batches in real-time too.
3. Data processing is a staging area for data across types and formats to be filtered for further usage.
4. Data orchestration cleanses, enriches, aggregates and reformats processed business information to meet the requirements of the target data repositories or the applicable software application.
5. Data discovery helps business and IT specialists to apply data for recognizing dependencies and identifying inaccuracies.
6. Data access delivers data to multiple downstream consumer applications or people, or the data marketplace, for business users.