Global Data Fabric Market (2020 to 2026)


Dublin, April 7, 2021 (GLOBE NEWSWIRE) – The “Data Matrix Market by Type (On Disk, In Memory), Business Applications (Fraud Detection & Security Management, Customer Experience Management, Business Process Management, GRC Management), Service, Vertical & Region – Global forecasts until 2026 “ the report was added to from offer.

The global data fabric market size will grow from $ 1.0 billion in 2020 to $ 4.2 billion by 2026, at a compound annual growth rate (CAGR) of 26.3% during the period of forecast.

Various factors such as increasing volume and variety of business data, the emerging need for business agility and accessibility, and the growing demand for continuous analytics are expected. real time lead to the adoption of software and data structure services.

The COVID-19 pandemic has forced businesses to find new alternatives for rapid recovery and to pay attention to the urgent need to access enough data in times of crisis. Disparate data stores hamper the efforts of business leaders to make informed decisions. Using a modern data architecture approach called Data Fabric, Ernst & Young (EY) developed Business Resiliency Data Fabric which enables data to be accessed wherever it resides. The data structure supports rapid technological changes while increasing the entropy of the data. To help mitigate the consequences of COVID-19, Denodo launched the Coronavirus Data Portal (CDP), a collaborative initiative that leverages data virtualization to unify critical data sets initially exposed in different formats from multiple sources and countries and make unified data accessible to everyone. Using the CDP and data virtualization capabilities of the Denodo platform, pmOne created detailed reports and AI analysis, seamlessly orchestrating all information flows in the pmOne Share Cockpit. Denodo and pmOne’s collaboration has provided the global community with reliable and up-to-date data on COVID-19 that can be used to develop new information on COVID-19 and reduce its impact.

Banks have moved to remote sales and service teams and launched a digital approach to customers to implement flexible payment terms for loans and mortgages. Grocery stores have shifted to online ordering and delivery as their core business. Schools in many countries have made the switch to 100% online learning and digital classrooms. Doctors have started offering telemedicine, aided by more flexible regulations. These approaches have resulted in an increase in the volume and variety of business data, an increase in the need for business agility and data accessibility, and a growing demand for real-time continuous analysis, contributing to the growth of the market. of the data structure.

Software segment will have largest market size during the forecast period

The data fabric market has been segmented on the basis of software and service components. The data structure facilitates the movement of data between the cloud, storage systems and data centers, with low latency contributing to the adoption of the data structure software. The services segment, on the other hand, has been divided into consulting, support and maintenance services, and education and training services.

In-memory data structure segment to have the highest CAGR during the forecast period

Based on the type of data fabric, the market has been segmented into disk-based data fabric and in-memory data fabric. The adoption of in-memory data structure is expected to increase dramatically in the coming years, due to the reduction in costs associated with storing and analyzing huge amounts of data.

Fraud detection and security management segment to account for larger market size during forecast period

The data fabric market, by business application, includes fraud detection and security management; governance, risk and compliance management; customer experience management; sales and marketing management; Process management; and other applications, including supply chain management, asset management and workforce management. Data Fabric automates the automatic detection of data anomalies and initiates actions to counter them. This not only minimizes losses, but also improves regulatory compliance leading to the adoption of Data Fabric software for fraud detection and security management.

Among regions, ACPA is expected to represent the highest CAGR during the forecast period

North America is expected to hold the largest market size in the global data fabric market. In contrast, the APAC region is expected to experience growth at the highest CAGR during the forecast period due to its increasing rate of technology adoption. Australia, China, Japan, India and South Korea are the main APAC countries that are technology-driven and present major investment and income opportunities. This is the main determining factor for the adoption of Data Fabric software in the APAC region.

Main topics covered:

1. Introduction

2 Research methodology

3 Executive summary

4 premium information
4.1 Attractive Opportunities in the Data Fabrics Market
4.2 Market, by Application
4.3 Market, by region
4.4 North America Market, by Application and Vertical

5 Market overview
5.1 Market dynamics
5.1.1 Drivers Increase in volume and variety of trade data Emerging need for business agility and accessibility Growing demand for real-time streaming analytics
5.1.2 Constraints Lack of knowledge of the data structure Lack of integration with legacy systems
5.1.3 Opportunities Generate a positive return on investment (King) Growing adoption of the cloud Progress of calculation in memory
5.1.4 Challenges Disinclination towards investing in new technologies Lack of sufficiently skilled labor
5.2 Industry trends
5.2.1 Introduction
5.2.2 Data fabric market: impact of COVID-19
5.2.3 Analysis of the case studies Use case 1: Ducati and Netapp together build a Data Fabric solution to drive innovation Use case 2: Bloomreach used Nexla’s solution to improve the approach to customer-centric data Use case 3: Ingenico used the HPE Ezmeral Data Fabric solution to develop a single unified data platform Use case 4: A leading healthcare provider used HPE Ezmeral Data Fabric to bring together disparate data sources into a single data lake Use Case 5: Ymca of the Greater Toronto Area leveraged a data matrix to quickly provide a solution for members to safely return to their facilities during COVID-19

6 Data Tissue Market, By Component
6.1 Presentation
6.1.1 Component: Market Drivers
6.1.2 Component: COVID-19 Impact
6.2 Software
6.3 Services
6.3.1 Managed Services
6.3.2 Professional services Advisory services Assistance and maintenance Education and training

7 Data Fabric Market Analysis, By Data Fabric Type
7.1 Presentation
7.1.1 Type of data fabric: market drivers
7.1.2 Type of data structure: impact of COVID-19
7.2 Data structure on disk
7.3 Data structure in memory

8 Data Fabric Market Analysis, By Business Application
8.1 Presentation
8.1.1 Business application: market drivers
8.1.2 Commercial application: impact of COVID-19
8.2 Fraud detection and security management
8.3 Governance, risk and compliance management
8.4 Customer experience management
8.5 Sales and marketing management
8.6 Business process management
8.7 Other applications

9 Data Fabric market, by deployment mode
9.1 Presentation
9.1.1 Deployment mode: market drivers
9.1.2 Deployment mode: impact of COVID-19
9.2 On site
9.3 Cloud

10 Data Fabrics Market, by Organization Size
10.1 Presentation
10.1.1 Size of the organization: market drivers
10.1.2 Size of the organization: impact of COVID-19
10.2 Large companies
10.3 Small and medium-sized enterprises

11 Data Fabrics Market, By Vertical
11.1 Presentation
11.1.1 Vertical: market drivers
11.1.2 Vertical: Impact of COVID-19
11.2 Data Fabric: business use case
11.3 Banking, financial services and insurance
11.4 Telecommunications and IT
11.5 Retail and e-commerce
11.6 Health and life sciences
11.7 Manufacturing
11.8 Government
11.9 Energy and utilities
11.10 Media and entertainment
11.11 Other verticals

12 Data Fabrics Market, By Region
12.1 Presentation
12.2 North America
12.3 Europe
12.4 Asia-Pacific
12.5 Middle East and Africa
12.6 Latin America

13 Competitive landscape
13.1 Overview
13.2 Company valuation quadrant
13.2.1 Stars
13.2.2 Emerging leaders
13.2.3 Omnipresent players
13.2.4 Participants
13.3 Startup / SME evaluation quadrant
13.3.1 Progressive companies
13.3.2 Responsive companies
13.3.3 Dynamic businesses
13.3.4 Starting blocks
13.4 Competitive scenario
13.4.1 Product launches and product improvements
13.4.2 Offers
13.4.3 Others

14 company profiles
14.1 Presentation
14.2 Key players
14.2.1 IBM
14.2.2 Oracle
14.2.3 Informatica
14.2.4 Talend
14.2.5 Denodo Technologies
14.2.6 SAP
14.2.7 Netapp, Inc.
14.2.8 Ag software
14.2.9 Splunk, Inc.
14.2.10 HPE
14.2.11 Dell Technologies
14.2.12 Teradata
14.2.13 Precisely
14.2.14 Global IDS
14.2.15 Tibco software
14.2.16 Idera
14.3 Start-up / SME profiles
14.3.1 Nexla
14.3.2 Stardog
14.3.3 Glue
14.3.4 Starburst data
14.3.5 Hexstream
14.3.6 Qomplx
14.3.7 Cluedin
14.3.8 Iguazio
14.3.9 Cinchy

15 Appendix

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