Global Data Tissue Market (2020 to 2026)


Dublin, April 07, 2021 (GLOBE NEWSWIRE) – The “data factory market by type (on disk, in memory), business applications (fraud detection and security management, customer experience management, management of business process, GRC management), The Service, Vertical and Region – Global Forecast to 2026 report “has been added to offer.

The global data fabrics 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 the increasing volume and variety of business data, the emerging need for business agility and accessibility, and the growing demand for real-time streaming analytics are expected to drive adoption. Data Fabric software and services.

The COVID-19 pandemic has forced companies to find new alternatives for rapid recovery and 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. Data Fabric supports rapid technological change while increasing data entropy. 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 open to all. Using the CDP and the data virtualization capabilities of the Denodo platform, pmOne created detailed reports and AI analysis, seamlessly orchestrating all information flows in the pmOne sharing 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 have launched digital outreach to customers to set up flexible payment terms for loans and mortgages. Grocery stores have shifted to online ordering and delivery as their core business. Schools in many places have switched to 100% online learning and digital classrooms. Doctors have started to provide telemedicine, aided by more flexible regulations. These approaches have resulted in the increase in the volume and variety of business data, the increased need for business agility and data accessibility, and a growing demand for real-time streaming analytics, contributing to the growth of the data factory market.

The software segment with the largest market size during the forecast period

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

In-memory data matrix segment will 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 the in-memory data fabric 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 represent largest 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 helps automate the automatic detection of data anomalies and trigger actions to counter them. This not only minimizes losses, but also improves regulatory compliance, leading to the adoption of data fabrication software for fraud detection and security management.

Among regions, APAC will 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, APAC is expected to grow at the highest CAGR during the forecast period due to its increasing rate of technology adoption. The main APAC countries that are technology-driven and present major investment and income opportunities are Australia, China, Japan, India and South Korea. This is the main determining factor for the adoption of Data Fabric software in APAC.

Main topics covered:

1. Introduction

2 Research methodology

3 Executive summary

4 premium information
4.1 Attractive Opportunities in the Data Manufacturing 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 awareness about Data Fabric Lack of integration with legacy systems
5.1.3 Opportunities Generate a positive return on investment (King) Growing adoption of the cloud Advancement of in-memory computing
5.1.4 Challenges Refusal to invest in new technologies Lack of sufficiently skilled labor
5.2 Industry trends
5.2.1 Presentation
5.2.2 Data factory market: impact of COVID-19
5.2.3 Analysis of the case studies Use case 1: Ducati and Netapp together create a Data Fabric solution to drive innovation Use case 2: Bloomreach used Nexla’s solution to improve the customer-centric approach to 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 disparate data sources together in a single data lake Use Case 5: Ymca of Greater Toronto took advantage of a data structure to quickly deliver a solution that allowed members to safely return to their facilities during COVID-19

6 Data Factory Market, by Component
6.1 Presentation
6.1.1 Component: Market drivers
6.1.2 Component: Impact of COVID-19
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 Factory Market Analysis, By Data Factory Type
7.1 Presentation
7.1.1 Type of Data Fabric: Market Drivers
7.1.2 Type of Data Fabric: COVID-19 Impact
7.2 Data Fabric on disk
7.3 Data Fabric in memory

8 Data Factory Market Analysis, By Business Application
8.1 Presentation
8.1.1 Commercial application: market drivers
8.1.2 Commercial application: impact of COVID-19
8.2 Fraud detection and security management
8.3 Governance, risk management and compliance
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 Manufacturing 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 Banks, 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 actors
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 IT
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 Cincheux

15 Annex

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