Data Fabric Market by Type (Disk-Based, In-Memory), Business Applications (Fraud Detection and Security Management, Customer Experience Management, Business Process Management, GRC Management), Service, Vertical and Region - Global Forecast to 2026
[211 Pages Report] The global data fabric market size to grow from USD 1.0 billion in 2020 to USD 4.2 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 26.3% during the forecast period. Various factors such as increasing volume and variety of business data, emerging need for business agility and accessibility, and growing demand for real-time streaming analytics are expected to drive the adoption of the data fabric software and services.
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COVID-19 impact on global data fabric market
The COVID-19 pandemic has forced businesses to find new alternatives for speedy recovery and attention to the urgent need to access enough data in crisis times. Disparate data stores hamper the efforts of business leaders to make fully informed decisions. Using a modern data architecture approach called data fabric, Ernst & Young (EY) developed Business Resiliency Data Fabric that enables access to data wherever it lives. Data fabric supports rapid technological change while increasing data entropy. To help alleviate the consequences of COVID-19, Denodo launched the Coronavirus Data Portal (CDP), a collaborative initiative that leverages data virtualization to unify critical datasets originally exposed in different formats from multiple sources and countries and make the unified data open to everyone. Using the CDP and the data virtualization capabilities of the Denodo Platform, pmOne created detailed reports and AI analysis, seamlessly orchestrating all the information streams in the pmOne Share Cockpit. The collaboration of Denodo and pmOne provided the global community with trustworthy, up-to-date data about COVID-19 that can be used to develop new intelligence about COVID-19 and reduce its impact. Banks have transitioned to remote sales and service teams and launched digital outreach to customers to make flexible payment arrangements for loans and mortgages. Grocery stores have shifted to online ordering and delivery as their primary business. Schools in many locales have pivoted to 100% online learning and digital classrooms. Doctors have begun delivering telemedicine, aided by more flexible regulation. These approaches have resulted in the rise of volume and variety of business data, the rise in need for business agility and data accessibility, and increasing demand for real-time streaming analytics, contributing to the growth of the data fabric market.
Market Dynamics
Driver: Increasing volume and variety of business data
Data fabric solutions and services provide unmatched opportunities to integrate and analyze the structured, semi-structured, and unstructured data sets that otherwise might be disregarded. Not only the business data variety but also the volume of such data sets is increasing day by day due to the evolution of digital and smart technologies across varied business functions. Sensor data, geo-location data, machine data, data generated from social media and weblogs, and data from other sources are increasing tremendously on a daily basis. Storing and gaining knowledge from this data is a matter of concern for most organizations. According to Domo’s eighth-annual Data Never Sleeps graphic, every minute of every day, consumers spend USD 1 million online, make 1.4 million video and voice calls, share 150,000 messages on Facebook, and stream 404,000 hours of video on Netflix. The collective data that needs to be managed by the end of the decade would be huge. Data fabric helps integrate data from various sources, store large amounts of data, and analyze it seamlessly in one place.
Restraint: Lack of integration with legacy systems
The key factor limiting the adoption of data fabric is the organizational culture, which is built upon storing and analyzing business-related data using traditional techniques, such as data warehouse and data marts. Hence, the most significant challenge today is to make businesses more aware of how they can store and analyze real-time critical data coming from various business events for deriving a sustainable impact from data fabric adoption. The adoption of data fabric solutions and services for varied business applications could be exciting, but it is crucial to integrate such data management systems alongside well-established, legacy, and proven systems. Organizations are embedded with multiple levels of systems. There could be major flaws; while legacy systems do not have well-defined interfaces, documentation is scarce, and the IT teams do not possess the required skills. While it is true that data fabric can provide value to various business functions across organizations, the benefits and proposition of such data management technologies are yet to be realized by many organizations.
Opportunity: Increasing adoption of cloud
The adoption of data fabric across various applications is associated with the varying end-user requirements. However, technology advancement also plays a vital role in enhancing the adoption trend by companies and customers. Most of the leading analytics technology vendors are now focusing on developing a complete cloud-based suite that will have the ability to appraise and enhance its digital properties. This model helps organizations in saving time and costs for onsite deployment and management of software solutions. As per an article published by Hosting Tribunal in January 2021, 50% of enterprises spend more than USD 1.2 million on cloud services annually, and 94% of enterprises are already using a cloud service. Hence, the increasing adoption of cloud technologies across industry verticals would create immense potential opportunities for data fabric vendors.
Challenge: Disinclination toward investment in new technologies
The adoption of new technology or changes in the already existing ones requires considerable effort and costs to the company. Adoption of new technology in a company depends on several factors, such as the business value of the technology, compatibility with the existing infrastructure and technologies, complexity, budget constraints, organizational policies, and procedures. Various costs, such as initial set-up costs, including IT, spends and infrastructure requirements, hiring, training, maintenance, and support would further add up to the total cost of ownership of the new technology. Apart from the cost to the company, the interest of the stakeholders and their acceptance toward the change is one of the major factors influencing the probability of adoption of new technology. Further, traditional applications bring a complicated set of interfaces, which, at times, are not compatible with the third-party software, thus causing errors. The integration of data from various sources and analytics can be a daunting task for enterprises and further complicate system performance. The possibility of errors increases manifolds, as the legacy systems sometimes do not have well-defined interfaces to counter with the new Application Programming Interfaces (APIs). These complications make organizations reluctant to adopt data fabric.
Disk-based data fabric segment to have largest market size during the forecast period
Based on type of data fabric, the market has been segmented into disk-based data fabric and in-memory data fabric. Disk-based data fabric provides various features, such as secured, controlled, and governed data. Additionally, it gives access to data whenever it is required by applications; it also gives the flexibility to migrate data and applications, lessen the cost of ownership and data compliance.
Small and medium-sized enterprises to account for highest CAGR during the forecast period
The Data fabric market has been segmented by organization size into large enterprises and SMEs. The market share of large enterprises is higher; however, the market for SMEs is expected to register a higher CAGR during the forecast period. Good data and storage management are a greater concern for business continuity in SMEs. Data fabric solutions help SMEs to increase their productivity, efficiency, marketing, and many other business processes.
On-premises segment to have largest market size during the forecast period
Based on deployment mode, the market has been segmented into on-premises and cloud. The on-premises segment is expected to hold largest market size while the cloud segment is expected to account for higher CAGR during the forecast period. Industries susceptible to data losses, data privacy, and security breaches prefer on-premises deployment of data management solutions contributing to the higher adoption of on-premises deployment mode.
Business process management to account for highest CAGR during the forecast period
The data fabric market, by business application, comprises fraud detection and security management; governance, risk and compliance management; customer experience management; sales and marketing management; business process management; and other applications including supply chain management, asset management, and workforce management. BPM enables organizations to align business functions with customer needs and helps executives determine how to deploy, monitor, and measure company resources. When properly executed, BPM has the ability to enhance efficiency and productivity, reduce costs, and minimize errors and risk – thereby optimizing results.
APAC to account for 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 growing technology adoption rate. The major countries in APAC that are technology-driven and present major opportunities in terms of investments and revenue include Australia, China, Japan, India, and South Korea. This is the major driving factor for the adoption of Data fabric software in APAC.
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Key Market Players
The Data fabric vendors have implemented various types of organic and inorganic growth strategies, such as new product launches, product upgradations, partnerships and agreements, business expansions, and mergers and acquisitions to strengthen their offerings in the market. The major vendors in the global data fabric market include Oracle Corporation (US), IBM Corporation (US), Informatica (US), Talend (US), Denodo Technologies (US), Global IDs (US), NetApp (US), SAP SE (Germany), Software AG (Germany), Splunk (US), Dell Technologies (US), HP Enterprise (US), Teradata Corporation (US), TIBCO Software (US), Precisely (US), Idera (US), Nexla (US), Stardog (US), Gluent (US), Starburst Data (US), HEXstream (US), QOMPLX (US), CluedIn (Denmark), Iguazio (Israel), and Cinchy (Canada). The study includes an in-depth competitive analysis of these key players in the data fabric market with their company profiles, recent developments, and key market strategies.
Scope of the Report
|
Report Metric |
Details |
|
Market size available for years |
2020–2026 |
|
Base year considered |
2020 |
|
Forecast period |
2021–2026 |
|
Forecast units |
USD Billion |
|
Segments covered |
Component, Data Fabric Type, Business Application, Deployment Mode, Organization Size, Vertical, and Region |
|
Geographies covered |
North America, Europe, APAC, Latin America, and MEA |
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Companies covered |
Denodo Technologies (US), Global IDs (US), IBM Corporation (US), Informatica (US), NetApp (US), Oracle Corporation (US), SAP SE (Germany), Software AG (Germany), Splunk (US), Talend (US), Dell Technologies (US), HP Enterprise (US), Teradata Corporation (US), TIBCO Software (US), Precisely (US), Idera (US), Nexla (US), Stardog (US), Gluent (US), Starburst Data (US), HEXstream (US), QOMPLX (US), CluedIn (Denmark), Iguazio (Israel), and Cinchy (Canada) |
This research report categorizes the data fabric market based on components, data fabric type, business applications, deployment mode, organization size, vertical, and regions.
By Component:
- Software
- Services
- Managed services
- Professional services
- Consulting
- Integration
- Support and Maintenance
By Data Fabric Type:
- Disk-based data fabric
- In-Memory data fabric
By Business Applications:
- Fraud Detection and Security Management
- Governance, Risk and Compliance Management
- Customer Experience Management
- Sales and Marketing Management
- Business Process Management
- Other Applications (Supply Chain Management, Asset Management, and Workforce Management)
By Deployment Mode:
- Cloud
- On-premises
By Organization Size:
- Large enterprises
- Small and medium-sized enterprises (SMEs)
By Vertical:
- BFSI
- Telecommunications and IT
- Retail and E-Commerce
- Healthcare and Life Sciences
- Manufacturing
- Government
- Energy and Utilities
- Media and Entertainment
- Other Verticals (Transportation and Logistics, Travel and Hospitality, and Education).
By Region:
- North America
- Europe
- APAC
- MEA
- Latin America
Recent Developments:
- In December 2020, Teradata announced an update for Teradata QueryGrid. It extended the hybrid multi-cloud capability of Vantage and will enable Teradata customers to access data and analytics across heterogeneous technologies and public cloud providers with new cloud-native capabilities. It enables customers to access data and analytics across heterogeneous technologies and public cloud providers with new cloud-native capabilities..
- In December 2020, SAP announced SAP Data Intelligence 3.1. The update includes features such as connectivity and integration, metadata and governance, pipeline modeling, intelligent processing, and deployment and delivery. It is an on-premise edition of the SAP Data Intelligence platform..
- In November 2020, IBM announced new capabilities for IBM Cloud Pak for Data. The update will help companies drive innovation across their expanding environments and accelerate their digital transformations. The platform runs on Red Hat OpenShift. So, it can be deployed and managed in any cloud environment.
- In June 2020, HPE unveiled HPE Ezmeral. It is a new software portfolio and brand that would accelerate data-driven transformation across organizations. It provides a complete portfolio, including container orchestration and management, AI/ML and data analytics, cost control, IT automation, and AI-driven operations, and security. This solution enables organizations to increase agility and efficiency, unlock insights, and deliver business innovation..
- In October 2018, IBM launched AI OpenScale, a data governance platform, which would help organizations build AI-based applications that provide a fair and unbiased outcome.
- In June 2020, Informatica announced an update for the Intelligent Data Platform. The platform was designed to be powered by Informatica’s AI-powered CLAIRE engine. The update would allow businesses to master business-critical data to increase customer retention and loyalty, manage supply chain risk, drive digital commerce, and boost operational efficiency.
- In March 2020, Oracle released Oracle Coherence version 14.1.1. The solution is a part of Oracle's Enterprise Cloud Native Java portfolio and includes Oracle WebLogic Server 14.1.1. Coherence 14.1.1 brings significant new features to the market, representing many man-years of engineering effort. The platform is fully compatible with popular container and orchestration ecosystems such as Docker and Kubernetes.
- In February 2020, Talend released an update for Talend Data Fabric. The update introduced a Talend Cloud Data Inventory, which automatically calculates the Data Intelligence Score of all data across an organization and presents it in a self-service cloud app for every user. The update also includes capabilities such as AI features and cutting-edge cloud connectivity..
Frequently Asked Questions (FAQ):
What is data fabric?
Data fabric is a distributed data management platform that enables organizations to integrate various data management processes, including data access, data discovery, data orchestration, data processing, data ingestion, data analytics, and data visualization. It brings together disparate data sets, both historical and real-time, and automatically processes them in an efficient way to deliver a comprehensive view of customer and business data across an organization.
Which are key verticals adopting data fabric solution and services?
Key verticals adopting data fabric software and services include BFSI, telecommunications and IT, retail and e-commerce, healthcare and life sciences, manufacturing, government, energy and utilities, media and entertainment, and others (transportation and logistics, travel and hospitality, and education).
Which are key business applications ofdata fabric software and services?
Key business applications of data fabric software and services include fraud detection and security management; governance, risk and compliance management; customer experience management; sales and marketing management; business process management; and other applications (supply chain management, asset management, and workforce management).
Which are the key drivers supporting the growth of the data fabric market?
The key drivers supporting the growth of the data fabric market include increasing volume and variety of business data, emerging need for business agility and accessibility, and growing demand for real-time streaming analytics.
Who are the key vendors in the data fabric market?
The key vendors operating in the Data fabric market Denodo Technologies, Global IDs, IBM Corporation, Informatica, NetApp, Oracle Corporation, SAP SE, Software AG, Splunk, Talend, Dell Technologies, HP Enterprise, Teradata Corporation, TIBCO Software, Precisely, Idera, Nexla, Stardog, Gluent, Starburst Data, HEXstream, QOMPLX, CluedIn, Iguazio, and Cinchy. These vendors have adopted different types of organic and inorganic growth strategies such as new product launches, product enhancements, partnerships, and mergers and acquisitions. .
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TABLE OF CONTENTS
1 INTRODUCTION (Page No. - 19)
1.1 INTRODUCTION TO COVID-19
1.2 COVID-19 HEALTH ASSESSMENT
FIGURE 1 COVID-19: GLOBAL PROPAGATION
FIGURE 2 COVID-19 PROPAGATION: SELECT COUNTRIES
1.3 COVID-19 ECONOMIC ASSESSMENT
FIGURE 3 REVISED GROSS DOMESTIC PRODUCT FORECASTS FOR SELECT G20 COUNTRIES IN 2020
1.3.1 COVID-19 ECONOMIC IMPACT—SCENARIO ASSESSMENT
FIGURE 4 CRITERIA IMPACTING GLOBAL ECONOMY
FIGURE 5 SCENARIOS IN TERMS OF RECOVERY OF GLOBAL ECONOMY
1.4 OBJECTIVES OF THE STUDY
1.5 MARKET DEFINITION
1.5.1 INCLUSIONS AND EXCLUSIONS
1.6 MARKET SCOPE
1.6.1 MARKET SEGMENTATION
1.6.2 REGIONS COVERED
1.6.3 YEARS CONSIDERED FOR THE STUDY
1.7 CURRENCY CONSIDERED
TABLE 1 USD EXCHANGE RATE, 2018–2020
1.8 STAKEHOLDERS
1.9 SUMMARY OF CHANGES
2 RESEARCH METHODOLOGY (Page No. - 29)
2.1 RESEARCH DATA
FIGURE 6 DATA FABRIC MARKET: RESEARCH DESIGN
2.1.1 SECONDARY DATA
2.1.2 PRIMARY DATA
2.1.2.1 Breakup of primary profiles
2.1.2.2 Key industry insights
2.2 MARKET BREAKUP AND DATA TRIANGULATION
FIGURE 7 DATA TRIANGULATION
2.3 MARKET SIZE ESTIMATION
FIGURE 8 RESEARCH METHODOLOGY: APPROACH
FIGURE 9 DATA FABRIC MARKET: TOP-DOWN AND BOTTOM-UP APPROACHES
2.3.1 TOP-DOWN APPROACH
2.3.2 BOTTOM-UP APPROACH
FIGURE 10 MARKET SIZE ESTIMATION METHODOLOGY — APPROACH 1 (SUPPLY SIDE): REVENUE OF SOFTWARE/SERVICES OF THE MARKET
FIGURE 11 MARKET SIZE ESTIMATION METHODOLOGY ̶ APPROACH 1 BOTTOM-UP (SUPPLY SIDE): COLLECTIVE REVENUE OF DATA FABRIC VENDORS
FIGURE 12 MARKET SIZE ESTIMATION METHODOLOGY ̶ APPROACH 2 BOTTOM-UP (SUPPLY SIDE): COLLECTIVE REVENUE OF ALL SOFTWARE/SERVICES OF THE DATA FABRIC MARKET
FIGURE 13 MARKET SIZE ESTIMATION METHODOLOGY— APPROACH 3—BOTTOM-UP (DEMAND SIDE): SHARE OF DATA FABRIC THROUGH OVERALL DATA FABRIC SPENDING
2.4 MARKET FORECAST
TABLE 2 FACTOR ANALYSIS
2.5 COMPANY EVALUATION MATRIX METHODOLOGY
FIGURE 14 COMPANY EVALUATION MATRIX: CRITERIA WEIGHTAGE
2.6 ASSUMPTIONS FOR THE STUDY
2.7 LIMITATIONS OF THE STUDY
3 EXECUTIVE SUMMARY (Page No. - 43)
TABLE 3 DATA FABRIC MARKET SIZE AND GROWTH RATE, 2020–2026 (USD MILLION, Y-O-Y%)
FIGURE 15 DATA FABRIC SOFTWARE TO BE A LARGER MARKET IN 2020
FIGURE 16 PROFESSIONAL SERVICES ESTIMATED TO ACCOUNT FOR A LARGER MARKET SIZE IN 2020
FIGURE 17 SUPPORT AND MAINTENANCE SERVICES ESTIMATED TO DOMINATE THE MARKET IN 2020
FIGURE 18 DISK-BASED DATA FABRIC SEGMENT ESTIMATED TO ACCOUNT FOR A LARGER MARKET SIZE IN 2020
FIGURE 19 ON-PREMISES SEGMENT ESTIMATED TO BE A LARGER MARKET IN 2020
FIGURE 20 LARGE ENTERPRISES ESTIMATED TO BE A LARGER MARKET IN 2020
FIGURE 21 FRAUD DETECTION AND SECURITY MANAGEMENT SEGMENT TO ACCOUNT FOR THE LARGEST MARKET SIZE IN 2020
FIGURE 22 BANKING, FINANCIAL SERVICES, AND INSURANCE VERTICAL ESTIMATED TO ACCOUNT FOR THE LARGEST MARKET SIZE IN 2020 47
FIGURE 23 APAC PROJECTED TO ACCOUNT FOR THE HIGHEST CAGR DURING THE FORECAST PERIOD
4 PREMIUM INSIGHTS (Page No. - 49)
4.1 ATTRACTIVE OPPORTUNITIES IN THE DATA FABRIC MARKET
FIGURE 24 GROWTH IN DEMAND FOR ANALYSIS AND STORAGE OF BIG DATA TO BOOST THE MARKET GROWTH
4.2 MARKET, BY APPLICATION
FIGURE 25 FRAUD DETECTION AND SECURITY MANAGEMENT APPLICATION SEGMENT PROJECTED TO HAVE A LARGER MARKET SHARE DURING THE FORECAST PERIOD
4.3 MARKET, BY REGION
FIGURE 26 NORTH AMERICA PROJECTED TO ACCOUNT FOR THE LARGEST MARKET SHARE IN 2026
4.4 NORTH AMERICA MARKET, BY APPLICATION AND VERTICAL
FIGURE 27 FRAUD DETECTION AND SECURITY MANAGEMENT AND BFSI SEGMENTS ESTIMATED TO ACCOUNT FOR THE LARGEST SHARES IN NORTH AMERICA IN 2020
5 MARKET OVERVIEW (Page No. - 51)
5.1 MARKET DYNAMICS
FIGURE 28 DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES: DATA FABRIC MARKET
5.1.1 DRIVERS
5.1.1.1 Increasing volume and variety of business data
5.1.1.2 Emerging need for business agility and accessibility
5.1.1.3 Growing demand for real-time streaming analytics
5.1.2 RESTRAINTS
5.1.2.1 Lack of awareness about data fabric
5.1.2.2 Lack of integration with legacy systems
5.1.3 OPPORTUNITIES
5.1.3.1 Generating positive Return on Investment (RoI)
5.1.3.2 Increasing adoption of cloud
5.1.3.3 Advancement of in-memory computing
5.1.4 CHALLENGES
5.1.4.1 Disinclination toward investment in new technologies
5.1.4.2 Lack of sufficiently skilled workforce
5.2 INDUSTRY TRENDS
5.2.1 INTRODUCTION
5.2.2 DATA FABRIC MARKET: COVID-19 IMPACT
FIGURE 29 MARKET TO WITNESS SLOWDOWN IN GROWTH IN 2020
5.2.3 CASE STUDY ANALYSIS
5.2.3.1 Use Case 1: Ducati and NetApp together build a data fabric solution to boost innovation
5.2.3.2 Use case 2: Bloomreach used Nexla’s solution to enhance the customer-centered data approach
5.2.3.3 Use case 3: Ingenico used HPE Ezmeral Data Fabric solution to develop a single unified data platform
5.2.3.4 Use case 4: Leading healthcare provider used HPE Ezmeral Data Fabric to bring together disparate data sources into one data lake
5.2.3.5 Use case 5: YMCA of Greater Toronto leveraged a Data Fabric to rapidly deliver a solution that allowed members to safely return to their facilities during COVID-19
6 DATA FABRIC MARKET, BY COMPONENT (Page No. - 59)
6.1 INTRODUCTION
6.1.1 COMPONENT: MARKET DRIVERS
6.1.2 COMPONENT: COVID-19 IMPACT
FIGURE 30 SERVICES SEGMENT TO REGISTER A HIGHER CAGR DURING THE FORECAST PERIOD 61
TABLE 4 MARKET SIZE, BY COMPONENT, 2020–2026 (USD MILLION)
6.2 SOFTWARE
TABLE 5 DATA FABRIC SOFTWARE MARKET SIZE, BY REGION, 2020–2026 (USD MILLION)
6.3 SERVICES
FIGURE 31 MANAGED SERVICES SEGMENT TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
TABLE 6 DATA FABRIC SERVICES MARKET SIZE, BY REGION, 2020–2026 (USD MILLION)
TABLE 7 MARKET SIZE, BY SERVICE, 2020–2026 (USD MILLION)
6.3.1 MANAGED SERVICES
TABLE 8 MANAGED SERVICES: DATA FABRIC MARKET SIZE, BY REGION, 2020–2026 (USD MILLION)
6.3.2 PROFESSIONAL SERVICES
FIGURE 32 EDUCATION AND TRAINING SEGMENT PROJECTED TO ATTAIN THE HIGHEST CAGR DURING THE FORECAST PERIOD
TABLE 9 PROFESSIONAL SERVICES: MARKET SIZE, BY REGION, 2020–2026 (USD MILLION)
6.3.2.1 Consulting services
TABLE 10 DATA FABRIC CONSULTING SERVICES MARKET SIZE, BY REGION, 2020–2026 (USD MILLION)
6.3.2.2 Support and maintenance
TABLE 11 DATA FABRIC SUPPORT AND MAINTENANCE MARKET SIZE, BY REGION, 2020–2026 (USD MILLION)
6.3.2.3 Education and training
TABLE 12 DATA FABRIC EDUCATION AND TRAINING MARKET SIZE, BY REGION, 2020–2026 (USD MILLION)
7 DATA FABRIC MARKET ANALYSIS, BY TYPE OF DATA FABRIC (Page No. - 68)
7.1 INTRODUCTION
7.1.1 TYPE OF DATA FABRIC: MARKET DRIVERS
7.1.2 TYPE OF DATA FABRIC: COVID-19 IMPACT
FIGURE 33 IN-MEMORY DATA FABRIC SEGMENT PROJECTED TO HAVE A HIGHER CAGR DURING THE FORECAST PERIOD
TABLE 13 MARKET SIZE, BY TYPE OF DATA FABRIC, 2020–2026 (USD MILLION)
7.2 DISK-BASED DATA FABRIC
TABLE 14 DISK-BASED MARKET SIZE, BY REGION, 2020–2026 (USD MILLION)
7.3 IN-MEMORY DATA FABRIC
TABLE 15 IN-MEMORY MARKET SIZE, BY REGION, 2020–2026 (USD MILLION)
8 DATA FABRIC MARKET ANALYSIS, BY BUSINESS APPLICATION (Page No. - 72)
8.1 INTRODUCTION
8.1.1 BUSINESS APPLICATION: MARKET DRIVERS
8.1.2 BUSINESS APPLICATION: COVID-19 IMPACT
FIGURE 34 BUSINESS PROCESS MANAGEMENT SEGMENT PROJECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
TABLE 16 MARKET SIZE, BY BUSINESS APPLICATION, 2020–2026 (USD MILLION)
8.2 FRAUD DETECTION AND SECURITY MANAGEMENT
TABLE 17 FRAUD DETECTION AND SECURITY MANAGEMENT: DATA FABRIC MARKET SIZE, BY REGION, 2020–2026 (USD MILLION)
8.3 GOVERNANCE, RISK, AND COMPLIANCE MANAGEMENT
TABLE 18 GOVERNANCE, RISK, AND COMPLIANCE MANAGEMENT: MARKET SIZE, BY REGION, 2020–2026 (USD MILLION)
8.4 CUSTOMER EXPERIENCE MANAGEMENT
TABLE 19 CUSTOMER EXPERIENCE MANAGEMENT: MARKET SIZE, BY REGION, 2020–2026 (USD MILLION)
8.5 SALES AND MARKETING MANAGEMENT
TABLE 20 SALES AND MARKETING MANAGEMENT: MARKET SIZE, BY REGION, 2020–2026 (USD MILLION)
8.6 BUSINESS PROCESS MANAGEMENT
TABLE 21 BUSINESS PROCESS MANAGEMENT: MARKET SIZE, BY REGION, 2020–2026 (USD MILLION)
8.7 OTHER APPLICATIONS
TABLE 22 OTHER APPLICATIONS: MARKET SIZE, BY REGION, 2020–2026 (USD MILLION)
9 DATA FABRIC MARKET, BY DEPLOYMENT MODE (Page No. - 79)
9.1 INTRODUCTION
9.1.1 DEPLOYMENT MODE: MARKET DRIVERS
9.1.2 DEPLOYMENT MODE: COVID-19 IMPACT
FIGURE 35 ON-PREMISES SEGMENT TO WITNESS THE HIGHEST CAGR DURING THE FORECAST PERIOD
TABLE 23 MARKET SIZE, BY DEPLOYMENT MODE, 2020–2026 (USD MILLION)
9.2 ON-PREMISES
TABLE 24 ON-PREMISES MARKET SIZE, BY REGION, 2020–2026 (USD MILLION)
9.3 CLOUD
TABLE 25 CLOUD-BASED MARKET SIZE, BY REGION, 2020–2026 (USD MILLION)
10 DATA FABRIC MARKET, BY ORGANIZATION SIZE (Page No. - 83)
10.1 INTRODUCTION
10.1.1 ORGANIZATION SIZE: MARKET DRIVERS
10.1.2 ORGANIZATION SIZE: COVID-19 IMPACT
FIGURE 36 SMALL AND MEDIUM-SIZED ENTERPRISES SEGMENT TO REGISTER A HIGHER CAGR DURING THE FORECAST PERIOD
TABLE 26 MARKET SIZE, BY ORGANIZATION SIZE, 2020–2026 (USD MILLION)
10.2 LARGE ENTERPRISES
TABLE 27 MARKET SIZE IN LARGE ENTERPRISES, BY REGION, 2020–2026 (USD MILLION)
10.3 SMALL AND MEDIUM-SIZED ENTERPRISES
TABLE 28 MARKET SIZE IN SMALL AND MEDIUM-SIZED ENTERPRISES, BY REGION, 2020–2026 (USD MILLION)
11 DATA FABRIC MARKET, BY VERTICAL (Page No. - 87)
11.1 INTRODUCTION
11.1.1 VERTICAL: MARKET DRIVERS
11.1.2 VERTICAL: COVID-19 IMPACT
11.2 DATA FABRIC: ENTERPRISE USE CASES
FIGURE 37 MANUFACTURING SEGMENT PROJECTED TO ACHIEVE THE HIGHEST CAGR DURING THE FORECAST PERIOD
TABLE 29 MARKET SIZE, BY VERTICAL, 2020–2026 (USD MILLION)
11.3 BANKING, FINANCIAL SERVICES, AND INSURANCE
TABLE 30 MARKET SIZE IN BANKING, FINANCIAL SERVICES, AND INSURANCE, BY REGION, 2020–2026 (USD MILLION)
11.4 TELECOMMUNICATIONS AND IT
TABLE 31 MARKET SIZE IN TELECOMMUNICATIONS AND IT, BY REGION, 2020–2026 (USD MILLION)
11.5 RETAIL AND E-COMMERCE
TABLE 32 MARKET SIZE IN RETAIL AND E-COMMERCE, BY REGION, 2020–2026 (USD MILLION)
11.6 HEALTHCARE AND LIFE SCIENCES
TABLE 33 MARKET SIZE IN HEALTHCARE AND LIFE SCIENCES, BY REGION, 2020–2026 (USD MILLION)
11.7 MANUFACTURING
TABLE 34 DATA FABRIC MARKET SIZE IN MANUFACTURING, BY REGION, 2020–2026 (USD MILLION)
11.8 GOVERNMENT
TABLE 35 MARKET SIZE IN GOVERNMENT, BY REGION, 2020–2026 (USD MILLION)
11.9 ENERGY AND UTILITIES
TABLE 36 MARKET SIZE IN ENERGY AND UTILITIES, BY REGION, 2020–2026 (USD MILLION)
11.10 MEDIA AND ENTERTAINMENT
TABLE 37 MARKET SIZE IN MEDIA AND ENTERTAINMENT, BY REGION, 2020–2026 (USD MILLION)
11.11 OTHER VERTICALS
TABLE 38 OTHER VERTICALS: MARKET SIZE, BY REGION, 2020–2026 (USD MILLION)
12 DATA FABRIC MARKET, BY REGION (Page No. - 97)
12.1 INTRODUCTION
TABLE 39 MARKET SIZE, BY REGION, 2020–2026 (USD MILLION)
FIGURE 38 NORTH AMERICA PROJECTED TO HAVE THE LARGEST MARKET SHARE IN THE MARKET DURING THE FORECAST PERIOD
12.2 NORTH AMERICA
12.2.1 NORTH AMERICA: MARKET DRIVERS
12.2.2 NORTH AMERICA: COVID-19 IMPACT
12.2.3 NORTH AMERICA: REGULATIONS
12.2.3.1 Health Insurance Portability and Accountability Act of 1996
12.2.3.2 California Consumer Privacy Act
12.2.3.3 Gramm–Leach–Bliley Act
FIGURE 39 NORTH AMERICA MARKET SNAPSHOT
TABLE 40 NORTH AMERICA: DATA FABRIC MARKET SIZE, BY COMPONENT, 2020–2026 (USD MILLION)
TABLE 41 NORTH AMERICA: MARKET SIZE, BY SERVICE, 2020–2026 (USD MILLION)
TABLE 42 NORTH AMERICA: MARKET SIZE, BY PROFESSIONAL SERVICE, 2020–2026 (USD MILLION)
TABLE 43 NORTH AMERICA: MARKET SIZE, BY TYPE OF DATA FABRIC, 2020–2026 (USD MILLION)
TABLE 44 NORTH AMERICA: MARKET SIZE, BY BUSINESS APPLICATION, 2020–2026 (USD MILLION)
TABLE 45 NORTH AMERICA: MARKET SIZE, BY DEPLOYMENT MODE, 2020–2026 (USD MILLION)
TABLE 46 NORTH AMERICA: MARKET SIZE, BY ORGANIZATION SIZE, 2020–2026 (USD MILLION)
TABLE 47 NORTH AMERICA: MARKET SIZE, BY INDUSTRY VERTICAL, 2020–2026 (USD MILLION)
12.3 EUROPE
12.3.1 EUROPE: MARKET DRIVERS
12.3.2 EUROPE: COVID-19 IMPACT
12.3.3 EUROPE: REGULATIONS
12.3.3.1 General Data Protection Regulation
12.3.3.2 European Committee for Standardization
12.3.3.3 EU Data Governance Act
12.3.3.4 European Technical Standards Institute
TABLE 48 EUROPE: DATA FABRIC MARKET SIZE, BY COMPONENT, 2020–2026 (USD MILLION)
TABLE 49 EUROPE: MARKET SIZE, BY SERVICE, 2020–2026 (USD MILLION)
TABLE 50 EUROPE: MARKET SIZE, BY PROFESSIONAL SERVICE, 2020–2026 (USD MILLION)
TABLE 51 EUROPE: MARKET SIZE, BY TYPE OF DATA FABRIC, 2020–2026 (USD MILLION)
TABLE 52 EUROPE: MARKET SIZE, BY BUSINESS APPLICATION, 2020–2026 (USD MILLION)
TABLE 53 EUROPE: MARKET SIZE, BY DEPLOYMENT MODE, 2020–2026 (USD MILLION)
TABLE 54 EUROPE: MARKET SIZE, BY ORGANIZATION SIZE, 2020–2026 (USD MILLION)
TABLE 55 EUROPE: MARKET SIZE, BY INDUSTRY VERTICAL, 2020–2026 (USD MILLION)
12.4 ASIA PACIFIC
12.4.1 ASIA PACIFIC: MARKET DRIVERS
12.4.2 ASIA PACIFIC: COVID-19 IMPACT
12.4.3 ASIA PACIFIC: REGULATIONS
12.4.3.1 Personal Data Protection Act
FIGURE 40 ASIA PACIFIC MARKET SNAPSHOT
TABLE 56 ASIA PACIFIC: DATA FABRIC MARKET SIZE, BY COMPONENT, 2020–2026 (USD MILLION)
TABLE 57 ASIA PACIFIC: MARKET SIZE, BY SERVICE, 2020–2026 (USD MILLION)
TABLE 58 ASIA PACIFIC: MARKET SIZE, BY PROFESSIONAL SERVICE, 2020–2026 (USD MILLION)
TABLE 59 ASIA PACIFIC: MARKET SIZE, BY TYPE OF DATA FABRIC, 2020–2026 (USD MILLION)
TABLE 60 ASIA PACIFIC: MARKET SIZE, BY BUSINESS APPLICATION, 2020–2026 (USD MILLION)
TABLE 61 ASIA PACIFIC: MARKET SIZE, BY DEPLOYMENT MODE, 2020–2026 (USD MILLION)
TABLE 62 ASIA PACIFIC: MARKET SIZE, BY ORGANIZATION SIZE, 2020–2026 (USD MILLION)
TABLE 63 ASIA PACIFIC: MARKET SIZE, BY INDUSTRY VERTICAL, 2020–2026 (USD MILLION)
12.5 MIDDLE EAST AND AFRICA
12.5.1 MIDDLE EAST AND AFRICA: MARKET DRIVERS
12.5.2 MIDDLE EAST AND AFRICA: COVID-19 IMPACT
12.5.3 MIDDLE EAST AND AFRICA: REGULATIONS
12.5.3.1 ISRAELI Privacy Protection Regulations (Data Security), 5777-2017
12.5.3.2 Cloud Computing Framework
12.5.3.3 GDPR Applicability in KSA
12.5.3.4 Protection of Personal Information Act
12.5.3.5 TRA’s IoT Regulatory Policy
TABLE 64 MIDDLE EAST AND AFRICA: DATA FABRIC MARKET SIZE, BY COMPONENT, 2020–2026 (USD MILLION)
TABLE 65 MIDDLE EAST AND AFRICA: MARKET SIZE, BY SERVICE, 2020–2026 (USD MILLION)
TABLE 66 MIDDLE EAST AND AFRICA: MARKET SIZE, BY PROFESSIONAL SERVICE, 2020–2026 (USD MILLION)
TABLE 67 MIDDLE EAST AND AFRICA: MARKET SIZE, BY TYPE OF DATA FABRIC, 2020–2026 (USD MILLION)
TABLE 68 MIDDLE EAST AND AFRICA: MARKET SIZE, BY BUSINESS APPLICATION, 2020–2026 (USD MILLION)
TABLE 69 MIDDLE EAST AND AFRICA: MARKET SIZE, BY DEPLOYMENT MODE, 2020–2026 (USD MILLION)
TABLE 70 MIDDLE EAST AND AFRICA: MARKET SIZE, BY ORGANIZATION SIZE, 2020–2026 (USD MILLION)
TABLE 71 MIDDLE EAST AND AFRICA: MARKET SIZE, BY INDUSTRY VERTICAL, 2020–2026 (USD MILLION)
12.6 LATIN AMERICA
12.6.1 LATIN AMERICA: MARKET DRIVERS
12.6.2 LATIN AMERICA: COVID-19 IMPACT
12.6.3 LATIN AMERICA: REGULATIONS
12.6.3.1 Brazil Data Protection Law
12.6.3.2 Argentina Personal Data Protection Law No. 25.326
TABLE 72 LATIN AMERICA: DATA FABRIC MARKET SIZE, BY COMPONENT, 2020–2026 (USD MILLION)
TABLE 73 LATIN AMERICA: MARKET SIZE, BY SERVICE, 2020–2026 (USD MILLION)
TABLE 74 LATIN AMERICA: MARKET SIZE, BY PROFESSIONAL SERVICE, 2020–2026 (USD MILLION)
TABLE 75 LATIN AMERICA: MARKET SIZE, BY TYPE OF DATA FABRIC, 2020–2026 (USD MILLION)
TABLE 76 LATIN AMERICA: MARKET SIZE, BY BUSINESS APPLICATION, 2020–2026 (USD MILLION)
TABLE 77 LATIN AMERICA: MARKET SIZE, BY DEPLOYMENT MODE, 2020–2026 (USD MILLION)
TABLE 78 LATIN AMERICA: MARKET SIZE, BY ORGANIZATION SIZE, 2020–2026 (USD MILLION)
TABLE 79 LATIN AMERICA: MARKET SIZE, BY INDUSTRY VERTICAL, 2020–2026 (USD MILLION)
13 COMPETITIVE LANDSCAPE (Page No. - 123)
13.1 OVERVIEW
13.2 COMPANY EVALUATION QUADRANT
13.2.1 STARS
13.2.2 EMERGING LEADERS
13.2.3 PERVASIVE PLAYERS
13.2.4 PARTICIPANTS
FIGURE 41 KEY MARKET PLAYERS, COMPANY EVALUATION MATRIX, 2021
13.3 STARTUP/SME EVALUATION QUADRANT
13.3.1 PROGRESSIVE COMPANIES
13.3.2 RESPONSIVE COMPANIES
13.3.3 DYNAMIC COMPANIES
13.3.4 STARTING BLOCKS
FIGURE 42 STARTUP/SME DATA FABRIC MARKET EVALUATION MATRIX, 2021
13.4 COMPETITIVE SCENARIO
13.4.1 PRODUCT LAUNCHES AND PRODUCT ENHANCEMENTS
TABLE 80 PRODUCT LAUNCHES, 2019–2020
13.4.2 DEALS
TABLE 81 DEALS, 2019–2021
13.4.3 OTHERS
TABLE 82 OTHERS, 2018–2019
14 COMPANY PROFILES (Page No. - 132)
14.1 INTRODUCTION
14.2 KEY PLAYERS
(Business Overview, Products, Key Insights, Recent Developments, MnM View)*
14.2.1 IBM
TABLE 83 IBM: BUSINESS OVERVIEW
FIGURE 43 IBM: COMPANY SNAPSHOT
TABLE 84 IBM: PRODUCTS OFFERED
14.2.2 ORACLE
TABLE 85 ORACLE: BUSINESS OVERVIEW
FIGURE 44 ORACLE: COMPANY SNAPSHOT
TABLE 86 ORACLE: PRODUCTS OFFERED
14.2.3 INFORMATICA
TABLE 87 INFORMATICA: BUSINESS OVERVIEW
TABLE 88 INFORMATICA: PRODUCT OFFERED
14.2.4 TALEND
TABLE 89 TALEND: BUSINESS OVERVIEW
FIGURE 45 TALEND: COMPANY SNAPSHOT
TABLE 90 TALEND: PRODUCT OFFERED
14.2.5 DENODO TECHNOLOGIES
TABLE 91 DENODO TECHNOLOGIES: BUSINESS OVERVIEW
TABLE 92 DENODO TECHNOLOGIES: PRODUCT OFFERED
14.2.6 SAP
TABLE 93 SAP: BUSINESS OVERVIEW
FIGURE 46 SAP: COMPANY SNAPSHOT
TABLE 94 SAP: PRODUCT OFFERED
14.2.7 NETAPP, INC.
TABLE 95 NETAPP INC.: BUSINESS OVERVIEW
FIGURE 47 NETAPP, INC.: COMPANY SNAPSHOT
TABLE 96 NETAPP INC.: PRODUCTS OFFERED
14.2.8 SOFTWARE AG
TABLE 97 SOFTWARE AG: BUSINESS OVERVIEW
FIGURE 48 SOFTWARE AG: COMPANY SNAPSHOT
TABLE 98 SOFTWARE AG: PRODUCT OFFERED
14.2.9 SPLUNK, INC.
TABLE 99 SPLUNK, INC: BUSINESS OVERVIEW
FIGURE 49 SPLUNK, INC.: COMPANY SNAPSHOT
TABLE 100 SPLUNK: PRODUCT OFFERED
14.2.10 HPE
TABLE 101 HPE: BUSINESS OVERVIEW
FIGURE 50 HPE: COMPANY SNAPSHOT
TABLE 102 HPE: PRODUCT OFFERED
14.2.11 DELL TECHNOLOGIES
TABLE 103 DELL TECHNOLOGIES: BUSINESS OVERVIEW
FIGURE 51 DELL TECHNOLOGIES: COMPANY SNAPSHOT
TABLE 104 DELL TECHNOLOGIES: PRODUCT OFFERED
14.2.12 TERADATA
TABLE 105 TERADATA: BUSINESS OVERVIEW
FIGURE 52 TERADATA: COMPANY SNAPSHOT
TABLE 106 TERADATA: PRODUCT OFFERED
14.2.13 PRECISELY
TABLE 107 PRECISELY: BUSINESS OVERVIEW
TABLE 108 PRECISELY: PRODUCT OFFERED
14.2.14 GLOBAL IDS
TABLE 109 GLOBAL IDS: BUSINESS OVERVIEW
TABLE 110 GLOBAL IDS: PRODUCT OFFERED
14.2.15 TIBCO SOFTWARE
TABLE 111 TIBCO SOFTWARE: BUSINESS OVERVIEW
TABLE 112 TIBCO SOFTWARE: PRODUCT OFFERED
14.2.16 IDERA
TABLE 113 IDERA: BUSINESS OVERVIEW
TABLE 114 IDERA: PRODUCT OFFERED
*Details on Business Overview, Products Key Insights, Recent Developments, MnM View might not be captured in case of unlisted companies.
14.3 START-UP/SME PROFILES
14.3.1 NEXLA
14.3.2 STARDOG
14.3.3 GLUENT
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 (Page No. - 186)
15.1 ADJACENT AND RELATED MARKETS
15.1.1 INTRODUCTION
15.1.2 BIG DATA MARKET - GLOBAL FORECAST TO 2025
15.1.2.1 Market definition
15.1.2.2 Market overview
15.1.2.3 Big data market, by component
TABLE 115 BIG DATA MARKET SIZE, BY COMPONENT, 2018–2025 (USD MILLION)
TABLE 116 SOLUTIONS: BIG DATA MARKET SIZE, BY TYPE, 2018–2025 (USD MILLION)
TABLE 117 BIG DATA MARKET SIZE, BY SERVICE, 2018–2025 (USD MILLION)
TABLE 118 PROFESSIONAL SERVICES MARKET SIZE, BY TYPE, 2018–2025 (USD MILLION)
15.1.2.4 Big data market, by deployment mode
TABLE 119 BIG DATA MARKET SIZE, BY DEPLOYMENT MODE, 2018–2025 (USD MILLION)
TABLE 120 CLOUD: BIG DATA MARKET SIZE, BY TYPE, 2018–2025 (USD MILLION)
15.1.2.5 Big data market, by organization size
TABLE 121 BIG DATA MARKET SIZE, BY ORGANIZATION SIZE, 2018–2025 (USD MILLION)
15.1.2.6 Big data market, by business function
TABLE 122 BIG DATA MARKET SIZE, BY BUSINESS FUNCTION, 2018–2025 (USD MILLION)
15.1.2.7 Big data market, by industry vertical
TABLE 123 BIG DATA MARKET SIZE, BY INDUSTRY VERTICAL, 2018–2025 (USD MILLION)
15.1.2.8 Big data market, by region
TABLE 124 BIG DATA MARKET SIZE, BY REGION, 2018–2025 (USD MILLION)
15.1.3 DATA DISCOVERY MARKET—GLOBAL FORECAST TO 2025
15.1.3.1 Market definition
15.1.3.2 Market overview
15.1.3.3 Data discovery market, by component
TABLE 125 DATA DISCOVERY MARKET SIZE, BY COMPONENT, 2014–2019 (USD MILLION)
TABLE 126 DATA DISCOVERY MARKET SIZE, BY COMPONENT, 2019–2025 (USD MILLION)
TABLE 127 DATA DISCOVERY MARKET SIZE, BY SERVICE,2014–2019 (USD MILLION)
TABLE 128 DATA DISCOVERY MARKET SIZE, BY SERVICE, 2019–2025 (USD MILLION)
TABLE 129 PROFESSIONAL SERVICES: DATA DISCOVERY MARKET SIZE, BY TYPE, 2014–2019 (USD MILLION)
TABLE 130 PROFESSIONAL SERVICES: DATA DISCOVERY MARKET SIZE, BY TYPE, 2019–2025 (USD MILLION)
15.1.3.4 Data discovery market, by organization size
TABLE 131 DATA DISCOVERY MARKET SIZE, BY ORGANIZATION SIZE, 2014–2019 (USD MILLION)
TABLE 132 DATA DISCOVERY MARKET SIZE, BY ORGANIZATION SIZE, 2019–2025 (USD MILLION)
15.1.3.5 Data discovery market, by deployment mode
TABLE 133 DATA DISCOVERY MARKET SIZE, BY DEPLOYMENT MODE, 2014–2019 (USD MILLION)
TABLE 134 DATA DISCOVERY MARKET SIZE, BY DEPLOYMENT MODE, 2019–2025 (USD MILLION)
TABLE 135 CLOUD: DATA DISCOVERY MARKET SIZE, BY TYPE, 2014–2019 (USD MILLION)
TABLE 136 CLOUD: DATA DISCOVERY MARKET SIZE, BY TYPE, 2019–2025 (USD MILLION)
15.1.3.6 Data discovery market, by functionality
TABLE 137 DATA DISCOVERY MARKET SIZE, BY FUNCTIONALITY, 2014–2019 (USD MILLION)
TABLE 138 DATA DISCOVERY MARKET SIZE, BY FUNCTIONALITY, 2019–2025 (USD MILLION)
15.1.3.7 Data discovery market, by application
TABLE 139 DATA DISCOVERY MARKET SIZE, BY APPLICATION, 2014–2019 (USD MILLION)
TABLE 140 DATA DISCOVERY MARKET SIZE, BY APPLICATION, 2019–2025 (USD MILLION)
15.1.3.8 Data discovery market, by vertical
TABLE 141 DATA DISCOVERY MARKET SIZE, BY VERTICAL, 2014–2019 (USD MILLION)
TABLE 142 DATA DISCOVERY MARKET SIZE, BY VERTICAL, 2019–2025 (USD MILLION)
15.1.3.9 Data discovery market, by region
TABLE 143 DATA DISCOVERY MARKET SIZE, BY REGION, 2014–2019 (USD MILLION)
TABLE 144 DATA DISCOVERY MARKET SIZE, BY REGION, 2019–2025 (USD MILLION)
15.2 DISCUSSION GUIDE
15.3 KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
15.4 AVAILABLE CUSTOMIZATIONS
15.5 RELATED REPORTS
15.6 AUTHOR DETAILS
The study involved four major activities in estimating the current market size of the data fabric market. Extensive secondary research was done to collect information on the market, peer market, and parent market. The next step was to validate these findings, assumptions, and sizing with industry experts across the value chain through primary research. Both top-down and bottom-up approaches were used to estimate the total market size. After that, the market breakup and data triangulation procedures were used to estimate the market size of the segments and subsegments of the data fabric market.
Secondary Research
In the secondary research process, various sources were referred to for identifying and collecting information for the study. The secondary sources included annual reports, press releases, investor presentations of companies; white papers; journals; and certified publications and articles from recognized authors, directories, and databases.
Primary Research
Various primary sources from both supply and demand sides were interviewed to obtain qualitative and quantitative information on the market. The primary sources from the supply side included various industry experts, including Chief X Officers (CXOs); Vice Presidents (VPs); directors from business development, marketing, and product development/innovation teams; related key executives from data fabric solution vendors, system integrators, professional service providers, industry associations, and consultants; and key opinion leaders. All possible parameters that affect the market covered in this research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the final quantitative and qualitative data.
The following is the breakup of primary profiles:
To know about the assumptions considered for the study, download the pdf brochure
Market Size Estimation
Both top-down and bottom-up approaches were used to estimate and validate the total size of the data fabric market. The top-down approach was used to derive the revenue contribution of top vendors and their offerings in the market. The bottom-up approach was used to arrive at the overall market size of the global market using key companies’ revenue and their offerings in the market. The research methodology used to estimate the market size includes the following:
- The key players in the market were identified through extensive secondary research.
- The market size, in terms of value, was determined through primary and secondary research processes.
- All percentages, shares, and breakups were determined using secondary sources and verified through primary sources.
Data Triangulation
After arriving at the overall market size using the market size estimation processes as explained above, the market was split into several segments and subsegments. To complete the overall market engineering process and arrive at the exact statistics of each market segment and subsegment, data triangulation, and market breakup procedures were employed, wherever applicable. The overall market size was then used in the top-down procedure to estimate the size of other individual markets via percentage splits of the market segmentation.
Report Objectives
- To define, describe, and forecast the data fabric market by component, services, types of data fabric, business applications, deployment models, organization size, industry verticals, and regions.
- To provide detailed information regarding the major factors (drivers, restraints, opportunities, and challenges) influencing the growth of the market
- To analyze the micromarkets with respect to individual growth trends, prospects, and their contribution to the total market
- To analyze the opportunities in the market for stakeholders by identifying the high-growth segments of the market
- To forecast the size of the market segments with respect to five main regions: North America, Europe, Asia Pacific (APAC), Middle East & Africa (MEA), and Latin America
- To profile the key players of the data fabric market and comprehensively analyze their market shares and core competencies.
- To track and analyze competitive developments, such as new product launches, mergers & acquisitions, and partnerships, agreements, and collaborations in the market.
- To analyze the impact of the COVID-19 pandemic on the market
Available customizations
With the given market data, MarketsandMarkets offers customizations as per the company’s specific needs. The following customization options are available for the report:
Product analysis
- Product matrix provides a detailed comparison of the product portfolio of each company
Geographic analysis
- Further breakup of the North American data fabric market
- Further breakup of the European market
- Further breakup of the APAC market
- Further breakup of the Latin American market
- Further breakup of the MEA market
Company information
- Detailed analysis and profiling of additional market players up to 5
Get in-depth analysis of the COVID-19 impact on the Data Fabric Market
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Growth opportunities and latent adjacency in Data Fabric Market