Gartner Research

Data and Analytics
Essentials: How toDefine, Build andOperationalize aData FabricEhtisham Zaidi
16 May 2023

Data and Analytics Essentials: How to Deﬁne, Buildand Operationalize a Data FabricPublished 16 May 2023 - ID G00790376 - 6 min read
Ehtisham Zaidi

Data fabric is an emergent data management design thatpromises to reduce the time to integrated data delivery throughactive metadata-assisted automation. Data and analytics leaders
can use this presentation to explain the data fabric and help build acomprehensive data management infrastructure.
Additional Perspectives■

Summary Translation + Localization: Data and Analytics Essentials: How to Deﬁne,
Build and Operationalize a Data Fabric(18 September 2023)

The Challenge

Most organizations struggle with a comprehensive data management design that allows
them to optimally collect, connect, integrate and deliver distributed data fromheterogeneous data sources and applications for their data and analytics (D&A) usecase demands. The number of data and application silos has surged in the last decade,while the number of skilled personnel in data teams has either stayed constant ordropped. As a result, the time deﬁcit between when a request for integrated data israised to when the request is fulﬁlled is now at an all-time high.
The growing popularity of cloud for data management has only increased the complexityof creating and maintaining a consistent data management architecture that caters to allexisting and upcoming requests for data integration.

Gartner, Inc. | G00790376

Page 1 of 7

The Promise of Data FabricData fabrics have emerged as an increasingly popular design choice to simplify anorganization’s data integration infrastructure and create a scalable architecture that
reduces the technical debt seen in most D&A teams due to the rising integration challenge.
The intent is to not only address cost or create elegant design, but to gradually introduce
data utilization, context analysis and alignment use cases.

A data fabric is a data management design concept for attaining ﬂexible, reusable andaugmented data pipelines and services in support of various operational and analytics
use cases. Data fabrics support a combination of different data integration styles, andutilize active metadata, knowledge graphs, semantics and machine learning to augment
data integration design and delivery (see Figure 1).

Figure 1. Defining a Data FabricGartner, Inc. | G00790376

Page 2 of 7

The promise of the design concept is that once implemented, data fabrics would
signiﬁcantly eliminate manual data integration tasks and augment (and in some cases
completely automate) data integration design and delivery. However, it is important tonote that data fabrics are still not completely mature (see Hype Cycle for DataManagement, 2022). No single vendor currently delivers all the mature components (in anintegrated manner) that are needed to stitch together the data fabric (which means thatdata fabrics cannot be yet purchased from a single vendor). Data fabric also consists of amix of mature and less mature technology components. Organizations will therefore haveto carefully mix and match composable technology components as their use cases
evolve, and would need to know where and how to begin with their data fabric design
planning and implementation.

The Challenges Organizations Face When Planning and Implementing DataFabrics
D&A leaders face challenges beyond just the technological components of data fabric.
These challenges include:■

Deﬁning what data fabric is and therefore failing to demonstrate how it leverages
currently implemented technology solutions.

■Obtaining stakeholder support, which requires demonstrations of incremental
beneﬁts for incremental costs instead of “all automated” or “all new” solutions.

■

Getting started with creating a data fabric and knowing the main steps they need tofollow.

D&A leaders pursuing current implementations report that skills, roles and teammanagement are the key components to success and not necessarily the tools andsoftware. They have learned that tools and platforms are the enabling technology for adata fabric and consist of solutions already deployed, the metadata that represents
experience and learning patterns from those existing use-cases, and adding technology
only when the team and conditions highlight gaps.

How This Presentation Helps
This presentation is a direct response to the challenges described above. It is divided into
three logical segments to address the main challenges around data fabrics:

Gartner, Inc. | G00790376

Page 3 of 7

1.

The ﬁrst segment goes into detail about the “what” and the “why.” Business teams
and key stakeholders will learn the definition and benefits of data fabric and learn
why it is essential to adopt data fabric design, particularly for any organization
struggling with data silos. It also explains how data fabric beneﬁts organizations
that need automation in their data management tasks to support overburdened data
integration and data engineering teams.

2.

The second segment of the presentation addresses the common question, “How do I
stitch together the data fabric, and what are the components needed to do so?”This introduces D&A leaders in their strategic roadmap assessment to the key
composable components and technology parts needed to deliver their data fabric. It
also touches on the discipline (in this case, DataOps) and the team structures andskills needed to make the data fabric more usable. Next, D&A leaders are guided
through three paths to production for common use cases, which will help them getstarted (and get some important wins under their belt to keep the businessinterested). They can then progress to more advanced paths that support use casesthat need knowledge graphs for highly interconnected datasets. Finally, the datafabric can move into the ﬁnal frontier: the automation path that supports
automation of various tedious, error-prone and repetitive data management tasks byactivating metadata and analyzing it.

3.

The ﬁnal part of this presentation — the “where” — will allow D&A leaders tonavigate the complex vendor landscape to select mature technology components
from established vendor markets. We explain which types of technology choices areneeded and how they can be assembled. Finally, the presentation provides somecautions (based on the mistakes that Gartner has noticed early stage adoptersmake) and a representative list of vendors that can be investigated for implementing
data fabrics.

Please review the Notes Section for each slide, which explains and builds on the ideas
represented in each slide in detail.

Gartner, Inc. | G00790376

Page 4 of 7

Recommended Actions for Data and Analytics Leaders■

Use well-established integration technology and standards. Migrating to unknown
standards (without availability of adequate skills) will lead to a poor adoption of thedata fabric.

■

Invest in augmented data catalogs to inventory all types of metadata assets alongwith their associated relationships in a ﬂexible data model on a graph.

■

Ensure that the data fabric allows business teams to contribute and enrich the data
models with semantics and taxonomies/ontologies.

■

Provision a data fabric architecture that has the capability to combine various data
integration styles (such as extraction, transformation and loading with data
virtualization).

■Start by evaluating your existing tools (such as metadata management, data
integration/data virtualization, semantics and database management systems) toidentify the existing, overlapping or missing capabilities needed to deliver the datafabric design.

These presentation slides are most useful to data and analytics leaders who are working
to educate their stakeholders. Gartner clients working to get buy-in for their business caseto modernize the data management infrastructure can use this simple presentation
format to help executives understand the role and importance of data fabric.

Recommended by the Authors
Quick Answer: What Is Data Fabric Design?
Quick Answer: What Is Data Mesh?
Data and Analytics Essentials: Data Fabric and Data Mesh
Quick Answer: Comparing Data Fabric and Data Mesh
5 Ways to Enhance Your Data Engineering Practices
From Logical Data Warehouse to Data Fabric
Magic Quadrant for Data Integration Tools
Gartner, Inc. | G00790376

Page 5 of 7

Evidence
These presentation slides include content from:■2022 Gartner State of Data and Analytics Cloud Adoption Survey (see below)■

Client inquiries, Gartner search analytics, Google trends, Gartner social media
analysis of emerging technology topics in May 2022

2022 Gartner State of Data and Analytics Cloud Adoption Survey: This survey wasconducted to validate and understand how end-user organizations are practicing andplanning their journeys to perform data and analytics in the cloud, and what the key
drivers are for such a transition.The research was conducted online from October throughNovember 2022 among 461 respondents from North America, EMEA and Asia/Paciﬁc.
The respondents were from the manufacturing, natural resources, healthcare provider,banking and ﬁnance, IT, retail and wholesale, government, education, media andcommunications, transportation, and utility industries, in organizations with more than
250 full-time employees.The respondents were screened for being in director roles andhigher, having responsibility for adopting cloud or planning it, and having some visibility
or involvement in ﬁnancial decision making for cloud adoption.

Document Revision History
Data and Analytics Essentials: Data Fabric - 13 July 2021

Gartner, Inc. | G00790376

Page 6 of 7© 2023 Gartner, Inc. and/or its afﬁliates. All rights reserved. Gartner is a registered trademark of
Gartner, Inc. and its afﬁliates. This publication may not be reproduced or distributed in any form
without Gartner's prior written permission. It consists of the opinions of Gartner's researchorganization, which should not be construed as statements of fact. While the information contained inthis publication has been obtained from sources believed to be reliable, Gartner disclaims all warranties
as to the accuracy, completeness or adequacy of such information. Although Gartner research may
address legal and ﬁnancial issues, Gartner does not provide legal or investment advice and its researchshould not be construed or used as such. Your access and use of this publication are governed by
Gartner's Usage Policy. Gartner prides itself on its reputation for independence and objectivity. Its
research is produced independently by its research organization without input or inﬂuence from any
third party. For further information, see "Guiding Principles on Independence and Objectivity." Gartner
research may not be used as input into or for the training or development of generative artiﬁcial
intelligence, machine learning, algorithms, software, or related technologies.

Gartner, Inc. | G00790376

Page 7 of 7

Actionable, objective insight
Explore these additional complimentary resources and tools fordata and analytics leaders:WebinarIs Your Data AI Ready? Critical
Questions You Must Ask Data
Engineers
Explore the interactive relationshipbetween traditional and augmented datamanagement.
Watch Now

eBook

Get AI Ready — What IT Leaders
Need to Know and Do
Ready your enterprise to capture AI
opportunities and bolster your cybersecurity.

Download Now

WebinarGartner 100 Data & Analytics
Predictions Through 2028
Delve into 100 data and analytics strategicplanning assumptions through 2028.

Watch Now

Roadmap

Essential Guide to Data Fabric
Explore this roadmap to find out how toshare the right data at the right time.

Download Now

Already a client?
Get access to even more resources in your client portal. Log InConnect With Us
Get actionable, objective insight that drives smarter decisions andstronger performance on your mission-critical priorities. Contact us
to become a client:
U.S.: 1 855 811 7593
International: +44 (0) 3330 607 044
Become a Client

Learn more about Gartner for Data & Analytics Leadersgartner.com/en/data-analytics

Stay connected to the latest insights© 2024 Gartner, Inc. and/or its affiliates. All rights reserved. CM_GTS_2737624