08.15.2022 Executive Data Bytes – Data Fabric: What it is, Why it is, And Do You Need It

Executive Data Bytes

Tech analysis for the busy executive.

Welcome to another edition of Executive Data Bytes! This week, we are discussing a rapidly emerging data management design called data fabric. This new architecture allows companies to seamlessly access, integrate, analyze, and deliver data.

Focus piece: “What Is Data Fabric? Definition, Architecture, and Best Practices”

Executive Summary

A data fabric is an architecture and set of data services that provide consistent capabilities across a choice of endpoints spanning hybrid multi-cloud environments. It enables a unified data management architecture so that enterprises can gain from an extensible and converged data capability. This article from Spiceworks outlines what you really need to know about data fabric.

Key Takeaways

• While we live in a data-driven age, organizations spend a disproportionate amount of time on routine tasks and not enough on value addition. Data fabric rectifies this imbalance by removing back-end bottlenecks in data management.
• Data fabric is an emerging approach to handling data that uses a network-based architecture instead of point-to-point connections.
• Implementing a data fabric allows you to consolidate data governance and data security, integrate new data sources, analytical models, user interfaces, and automation scripts, and process metadata using graph models.
• A data fabric may be hosted on-premise or in the cloud, and may integrate with non-cloud IT tools such as Oracle on-premise, SAP on-premise, etc.
• The global data fabric market is predicted to grow by over 3X times to $3.7 billion by 2026, indicating strong demand in this space.

Focus piece: “Data Fabric Architecture is Key to Modernizing Data Management and Integration”

Executive Summary

Data management is prone to human errors. If data and analytics leaders want to reduce these errors, and overall costs, it’s crucial that they shift toward modern solutions like AI-enabled data integration. In this article, Gartner says the emerging design concept called "data fabric" can be a robust solution to data management challenges.

Key Takeaways

• Data fabric works like a car's autonomous element. It monitors the data pipelines as a passive observer at first, and then starts suggesting alternatives that are far more productive.
• Data fabric is a design concept that changes the focus of human and machine workloads. It is achieved using new and upcoming technologies such as semantic knowledge graphs, active metadata management, and embedded machine learning.
• For frictionless sharing of data, enterprises should activate metadata. This helps AI/ML algorithms to learn over time and make advanced predictions regarding data management and integration.
• Knowledge graphs allow data and analytics leaders to derive business value by enriching data with semantics. Integration standards and tools ensure easy access to and delivery from a knowledge graph.

Focus piece: “Data Fabric Architecture: Advantages and Disadvantages”

Executive Summary

Data fabric architecture has emerged as a way to facilitate data exchange among the many disparate systems that support the business. However, like anything, data fabric architecture has pros and cons. This ITPro Today article explores some of these advantages and disadvantages.

Key Takeaways

• A data fabric is a technology infrastructure used to access and move data, and is often positioned as a disruptive, zero-sum architecture. However, data fabric is a complement to, not a replacement for, data management tools, practices and concepts.
• Digital transformation is not just about digitizing workflows and processes, but also about retrofitting legacy and proprietary systems to participate in an ecosystem of connected systems, applications, and services. Data fabric architecture is one solution to this problem.
• There are at least three prevailing conceptions of data fabric architecture, including a decentralized architecture that rejects the need for centralized access, and a more inclusive architecture that includes centralized repositories but privileges decentralized access.
• Proponents of data fabric architecture emphasize simplified data access, federated access, and ownership of data. However, the technologies that underpin the data fabric have costs and benefits of their own.

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