Forrester on Data Fabric: Approach, Characteristics, Tooling Capabilities, Use Cases, and the Future

Updated November 10th, 2023
Forrester Data Fabric

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Leverage Forrester’s research to understand data fabric and use it for your data and analytics ecosystem.

Forrester is a research and consulting firm that specializes in offering insights on technology, marketing, customer experience (CX), product, and sales functions. Forrester runs annual surveys of 675,000+ consumers, business leaders, and technology leaders worldwide to get these insights, along with its Forrester Wave™ evaluations.

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The data fabric can help in setting up a connected, end-to-end data management platform, eliminating silos and supporting real-time decisions.

In this article, we’ll review how Forrester perceives data fabric and its impact on data and analytics use cases.

Table of contents

  1. Forrester on data fabric
  2. The data fabric platform, according to Forrester
  3. Data fabric use cases as per Forrester
  4. What’s the way forward for data fabric?
  5. Related reads

Forrester on data fabric

Data fabric is a modern architecture for the data stack to access real-time, consistent, connected, and trusted data.

According to Forrester, data fabric orchestrates disparate data sources on demand and in real-time, across hybrid and multi-cloud environments.

Here’s how Noel Yuhanna, VP, Principal Analyst at Forrester, describes the data fabric:

A data fabric delivers a unified, integrated, and intelligent end-to-end data platform. It automates all data management functions — including ingestion, transformation, orchestration, governance, security, preparation, quality, and curation.”

Also read → What is data fabric and how does it work?

The emergence of a need for data fabric: Forrester’s take

In the Forrester report titled Data Fabric 2.0 for Connected Intelligence, analysts explore the events that led to the data fabric going mainstream.

Earlier, enterprises used system integrators (SIs) to integrate, transform, prepare, or secure data. This required manually weaving together multiple products and technologies to create a fabric-like architecture.

As data and analytics technology and platforms became commoditized, the need for a unified and end-to-end data platform that supported all kinds of cloud environments became evident.

The report notes that, as a result, data fabric platforms have been launched and the market has matured. These platforms also “embrace emerging trends like graph engine, streaming, data intelligence, distributed in-memory, and more robust integration with cloud-native.”

The data fabric platform, according to Forrester

Forrester’s Tech Tide™ assessment of cloud data platforms (Q4, 2022) describes data fabric platforms as “solutions that create a semantic layer by automating the ingestion and orchestration of data sources.”

Such platforms reduce the complexity of the various data management functions by integrating data through intelligence.

To this end, Forrester’s Wave™ assessment of enterprise data fabric vendors (from Q2, 2022) report highlights three characteristics that an ideal data fabric platform must have:

  • It accelerates use cases with built-in automation, intelligence, no-code/low-code development, and integration with master data management (MDM)
  • It delivers built-in, end-to-end data management capabilities, enabled through a common UI and integrated API framework
  • It partners with professional services vendors for large and complex deployments

Forrester’s view on data fabric platform architecture

Forrester’s Data Fabric 2.0 for Connected Intelligence explores the architecture of a modern data fabric (i.e., data fabric 2.0) as a technology with:

  • The ability to source data in real-time and near real-time from databases, data warehouses, lake houses, devices, applications, and other data platforms
  • Real-time management of data, metadata, governance, data quality, lineage, and more
  • Automation and intelligence embedded at every layer of the data management lifecycle — from ingestion and processing to transformation and modeling
  • Domain-driven, globally distributed approach with self-service and embedded collaboration

We recommend checking out the complete report to delve into the specifics of the architecture.

Forrester’s view on data fabric platform capabilities

Forrester’s Data Fabric 2.0 for Connected Intelligence report identifies the following capabilities for data fabric platforms, in addition to the three core characteristics mentioned earlier:

  • Establishing consistency and trust for distributed and edge data
  • Protecting sensitive data to meet tougher regulatory and compliance mandates
  • Automating and connecting data at scale with graph engines
  • Supporting real-time data quality and transformation
  • Enabling global transaction management
  • Providing prebuilt multiple domains to accelerate business use cases
  • Integrating the growing ecosystem of external and third-party data

We recommend checking out the complete report to grasp the above capabilities.

Data fabric use cases as per Forrester

Data fabric is an enterprise architecture capable of supporting use cases such as:

  • Data mesh
  • Data science (AI/ML)
  • Data integration
  • Global operational insights
  • Distributed transactions
  • Data sharing and collaboration
  • Real-time analytics
  • IoT analytics
  • Fraud detection
  • Customer 360

So, what’s the way forward for data fabric?

Data fabric architecture would be complex as it integrates numerous data management functions. As a result, the data fabric market will see rapid evolution and innovation.

Noel Yuhanna predicts that the space will reach the next level with Generative AI and LLMs. This would include capabilities, such as “automation of processes, pipelines, workflows, code generation, integration with natural language query, and enabling data intelligence through adaptive learning.”

According to Yuhanna, generative AI and LLMs in a data fabric can support:

  • Natural language querying to access data, thereby democratizing it
  • Automated, real-time integration of data
  • Similarity searches with vector databases, thereby offering recommendations, smart search, or filters to find data faster
  • Automated anomaly detection, data cleansing, validation, and more to improve data quality in real-time
  • Automated discovery, classification, categorization, and data access based on policies in real-time, thereby simplifying data governance

As the space evolves, it would be interesting to observe how data fabric solution providers adapt to the market requirements.

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