A New Book: “Data Fabric – An Intelligent Data Architecture for AI”

I recently had the pleasure of co-authoring another book titled “Data Fabric – An Intelligent Data Architecture for AI”. Much has been written and hyped about data fabrics so we pulled this book together into what we believe is a compelling story conveying the importance and value of the data fabric approach – a data management architecture that helps optimize access to distributed data and intelligently curate and orchestrate it for self-service delivery to data consumers.

Some of a data fabric’s key capabilities and benefits are described below:

  • Architected to help elevate the value of enterprise data by providing users with access to the right data just in time, regardless of where or how it is stored
  • Architecture-agnostic to data environments, data processes, data use, and geography, while integrating core data management capabilities
  • Ability to automate data discovery, governance, and consumption, delivering business-ready data for analytics and AI
  • Ability to help business users and data scientists access trusted data faster for their applications, analytics, AI and ML models, and business process automation so as to improve decision-making and drive digital transformation
  • Ability to help technical teams use simplified data management and governance in complex hybrid and multicloud data landscapes while significantly reducing costs and risk.

In essence, a data fabric is an architecture that facilitates the end-to- end integration of various data pipelines and cloud environments via intelligent and automated systems.

Delivering a Data Fabric

The capabilities of the Cloud Pak for Data data fabric architecture are designed to help organizations:

  • Simplify and automate access to data across multicloud and on- premises data sources, without moving data
  • Universally safeguard the use of all data, regardless of source
  • Provide business users with a self-service experience for finding and using data
  • Use AI-powered capabilities to automate and orchestrate the data Lifecycle

The book also discusses several common use cases that illustrate ways in which organizations can successfully leverage a data fabric in different scenarios.

An IA for AI

It’s been said many times that there can be no effective artificial intelligence (AI) without an effective Information Architecture (IA). What we have attempted to demonstrate throughout this book is that the data fabric approach can provide a way through the enterprise jungle of data and applications found in many IT and information architectures, on which previous approaches and paradigms have fallen short of delivering.

IBM designed and delivers its data fabric capabilities as part of a unified, integrated, collaborative data and AI platform known as Cloud Pak for Data. The IBM Cloud Pak for Data platform offers a comprehensive set of capabilities, delivered as containerized services built on Red Hat OpenShift. This helps make the platform portable and scalable across the major industry (and other) hyperscalers and is available and consumable in a range of different form factors and deployment options designed to suit ever-changing business needs and budgets.

A data fabric, if implemented as part of a data and AI enterprise– hardened platform can deliver the following benefits:

  • For technical teams and CTOs:
  • Decreased effort to maintain data-quality standards due to fewer data versions
  • Reduced infrastructure and storage costs (consolidated data- management tools and reduction in data copies)
  • Faster and simplified data-delivery processes because there are fewer targets to reach and advanced optimization of data flows
  • Reduction in efforts for data access management as it gets automated by global data policy enforcement.

For business teams and CDOs:

  • Gaining faster and more-accurate insights due to easy access to high-quality data
  • Ability to focus time on analyzing rather than finding and preparing data
  • Streamlined full self-service data-shopping experience
  • Avoidance of biased analysis due to data restrictions
  • Increased compliance and security despite full analytics utilization

Download the Book

In closing, the authors and contributors of this book hope you succeed in all your data and AI adventures. We hope this book helps you to take that first or next step on your journey.

Having read this summary, if you are interested in reading the whole book you can download it at no cost here

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