Why Gartner’s Data Fabric Graphic Puts The Horse Before The Cart

Here is the link to today’s Gartner post on LinkedIn regarding the Data Fabric graphic. My comment is below.

I will use these three terms: agent-based model, metaprise, and then – only then, as you call it, data fabric.

Without the first two being in place, the data fabric map described above is incomplete and has limited value. Everything begins at the point of new data inputs. A major flaw is the belief that Generative AI or Agentic AI will address the challenges with the data feed.

What is an agent-based model? – https://youtu.be/49BS-MkGoak

What is a Metaprise? – https://bit.ly/3Oo0y16

My theory of strand commonality, in which seemingly disparate data streams have attributes that are actually connected and collectively impact desired outcomes, was formulated in the late 1990s to support the IT infrastructure of the Department of National Defence/Ministère de la défense nationale. Within this agent-based model, internal and external stakeholders and related systems (or agents) are identified and captured via a Metaprise operating system, becoming the real-time “new source” of data.

The challenge with your data fabric map is that internal stakeholder (agent) communication and collaboration are poor in the real world, let alone when you extend that model to include external stakeholders, such as suppliers, shippers, and customs.

What Does ChatGPT Say?

The difference between a metaprise and a data fabric lies in their scope, purpose, and implementation within an organization. While they both deal with the organization and management of data, they address different aspects of a business’s digital infrastructure.

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1. Metaprise

• Definition:
  • A metaprise refers to the concept of a digitally interconnected enterprise. It is a holistic framework that encompasses the seamless integration of people, processes, data, and technologies across the entire business ecosystem, including internal operations, external partners, and customers.
• Scope:
  • Focuses on the enterprise-level integration of systems and workflows.
  • Aims to break down organizational silos to create a unified digital business ecosystem.
• Purpose:
  • To enable better collaboration, decision-making, and adaptability by interconnecting all components of an enterprise.
  • Extends beyond data to include processes, applications, and relationships between various enterprise systems and stakeholders.
• Key Features:
  • Comprehensive digital transformation strategy.
  • Enhanced interoperability between departments and partners.
  • Real-time collaboration and workflow automation.
• Examples of Use:
  • Creating a connected ecosystem where supply chain data, customer insights, and operational data are unified for strategic decision-making.

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2. Data Fabric

• Definition:
  • A data fabric is an architectural approach to managing and accessing data across disparate systems and environments. It is primarily focused on integrating, organizing, and governing data to enable seamless access, regardless of where the data resides.
• Scope:
  • Specifically deals with data integration, management, and access.
  • Focuses on enabling consistent and real-time data availability across on-premises systems, cloud platforms, and hybrid environments.
• Purpose:
  • To solve data silos and ensure data consistency, quality, and accessibility for analytics, AI, and decision-making.
  • Provides a metadata-driven framework to automate and optimize data discovery, integration, and governance.
• Key Features:
  • Metadata-driven automation.
  • Integration across multi-cloud, hybrid, and on-premises environments.
  • Real-time data access and self-service capabilities.
• Examples of Use:
  • Enabling consistent access to customer data for real-time personalization across marketing, sales, and service teams.

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Key Differences Between Metaprise and Data Fabric

Aspect	Metaprise	Data Fabric
Scope	Enterprise-wide integration of people, processes, and technology.	Integration and management of data across systems.
Primary Focus	Holistic digital transformation of the organization.	Seamless and consistent access to data for analytics and decision-making.
Key Components	Applications, workflows, people, and data.	Data integration, metadata, and governance.
Purpose	To unify and streamline the entire business ecosystem.	To solve data silos and ensure efficient data access and management.
Technology Use	May involve multiple technologies like ERP, CRM, workflow automation, and collaboration tools.	Focuses on data-related technologies, such as AI, ML, and metadata management.
Outcome	Improved collaboration, workflow automation, and ecosystem connectivity.	Improved data availability, quality, and governance for actionable insights.

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How They Complement Each Other

• A data fabric can be a foundational component of a metaprise. While the metaprise focuses on achieving enterprise-wide digital connectivity, the data fabric ensures that the underlying data infrastructure is integrated, consistent, and accessible, supporting the broader goals of the metaprise.
• Together, they enable organizations to optimize not just their data but also their processes, decisions, and ecosystem collaboration.

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