Global Data Fabric Market Size, Share & Trends Analysis By Component, By Deployment, By Type (Disk-based In-memory), By Enterprise Size, By Business Applications, By Industry, Segment Forecasts And By Region (North America, Europe, Asia-Pacific, Middle East and Africa, and South America) - Forecast (2024-2032)
The Data Fabric Market in terms of revenue was estimated to be worth USD 2.29 billion in 2023 and is poised to reach USD 12.54 billion by 2032, growing at a CAGR of 20.8% from 2024 to 2032.
The digital revolution has increased the number of online platforms at a rapid pace. The companies maintain this data and the resultant knowledge from various remote sources. Hence, providing fast access to the data across the wide ranges of landscapes poses various challenges for the providers of data management services. The dispersion and heterogeneity of business data assets enhance the complexity of data management and data integration, which, in turn, enhances the likelihood of adopting a data fabric in organizations.
Recent times have witnessed a growing interest of both practitioners and researchers in big data analytics. Investments in big data analytics worldwide have mushroomed to a million dollars from private as well as public sources. This is likely to demand technologies of big data analytics because data volumes and complexity would be increasing rapidly with the growth of cloud computing as well as mobile data traffic plus the development and adoption of technology such as artificial intelligence and internet of things. Companies will probably build a data fabric to power enterprise-wide data and analytics and automate most of the work involved in data exploration, integration, ingestion, and preparation.
The pandemic has forced organizations to explore innovative solutions for quick recovery and to give an important priority to the need to access enough data in emergency situations. Banks have also started digital outreach to customers and moved their sales and service teams to work from home, facilitating flexible payment options for loans and mortgages. The grocery businesses mainly rely on online ordering and delivery whereas thousands of schools use digital classrooms and online learning now. These practices have resulted in a vast amount of corporate data, which is because of it that the growth of the Data Fabric market is being propelled by the need for real-time streaming analytics, corporate agility and accessibility of data, among other factors.
Key Findings
It could provide an opportunity to weave multiple sets of data, which otherwise may go unnoticed. The growth of digital and smart technologies from HR to finance to operations and supply chain across different business functions would boost the market size. The increasing quantity of data being generated from social media and weblogs, as well as other sources, such as sensor data, geo-location data, and machine data, is growing at a tremendous rate. Many leading brands and organizations are embracing data fabric solutions to arrive at real-time insights based on such rising data. Data fabric allows data from many sources, enabling firms to store vast amounts of data and analyse those data intuitively somewhere. It provides several benefits, which include derivation of value out of any type of data; the storing of all structured as well as unstructured types of data; and an all-around access to data in one unified view of data of organizations.
Key Findings
The biggest deterrent to the spread of data fabric solutions all over the world is the administrative philosophy, which involves the use of outdated methods in a bid to store and assess business data, particularly through the tools of data warehouses and data marts. Therefore, the most important task today is to make businesses aware of real-time storage and analysing critical data across different business events to derive a sustainable impact from the adoption of data fabric. Hence, it's pretty crucial to integrate such legacy data management systems with existing popular systems. The legacy systems do not have well-defined interfaces and IT teams lack the requisite skills. Such data management technologies like data fabric will require an amount of awareness across the leading organizations in the world.
Data fabric can create a positive ROI for organizations in numerous ways. Data fabric systems, at the core, improve operational efficiency. It becomes possible to eliminate paper forms and reduce delays until handoff with them. Analytical systems emphasize the effectiveness of business operations. It helps target the best prospects and price risk more accurately than ever. Better use of available resources is also achieved. Generally, the goodness of data fabric translates into a positive ROI in one of the several ways. It offers substantial throughput, which happens to be lower in latency than what the traditional low-latency IP-based middleware can deliver. Data fabric is known to achieve breakthrough performance at lower CPU overhead that enables business applications to do even more critical work faster. Most costly resources can be allocated with high precision where they would add most value, thereby enriching the solution.
Key Findings
Such amount of efforts and cost it demands to the company to adopt new technology or changes in the existing ones put it on a critical decision table. The adoption of data fabric, Meta data management, data catalo, etc. among leading brands are strictly dependent on several factors such as business values of the technology, the compatibility of the technology with the existing infrastructure, complexity of the technology to be adopted, budgetary restrictions, administration policies, and techniques. The set-up costs of the new technology involved ownership at set-up arising from IT spending and the need for infrastructure plus training and recruiting, and maintenance and support. Some of the other factors influencing the uptake of new data management technologies include the nature of the stakeholders and their acceptance of the change. In addition, traditional applications have sophisticated user interfaces which may lead to incompatibility issues with third-party software that introduce errors. Data integration from sources that are originated from multiple channels could be a challenging task for the firms which will weaken the overall performance within the organizations. Hence, the above-mentioned factors do not induce the respective organizations to accept data fabric solutions and services.
Legacy systems are often the core feature of most large enterprises' business environments because they have been in operation for decades. Even if they work, many of them are not pliable enough to adapt to the latest data management requirements. When introducing new data fabric solutions to legacy infrastructure, things can become very complicated and time-consuming, especially concerning aspects such as compatibility and the flow of data across different platforms. It can also be costly and involves much time, which requires reconfiguration or updating of existing systems. Plus, legacy systems host critical business data that organizations simply don't want to touch to avoid disinterruption during integration. Such reluctance to replace or upgrade established systems can also delay the adoption of data fabric technologies, which the organizations might measure against immediate cost and difficulties against the long-term benefits of seamless data integration and enhanced analytics. Integration complexity may be a major challenge to enterprising companies with very low financial resources and average technical competency in implementing data fabric solutions.
The market scope is segmented because of By Component, By Deployment, By Type, By Enterprise Size, By Business Applications, By Industry, By Region.
By Component Insights
Based on the Component Insights of the market is segmented into Software, Services.
In 2021, solution accounted for nearly 80% of the revenue share in the data fabric market. Throughout the forecast period, the segment is expected to hold the largest market share. Data fabric platforms have gained recognition due to optimization in the management of business data. The architecture also helps businesses with agility by eliminating the hindrance caused by data silos and facilitates real-time streaming analytics that benefits the business.
Services is likely to experience an exponential rise in the forecast period. The market players are providing services to their customers by offering professional solutions. Because of real-time connectivity, self-service, automation, and universal changes in business, traditional data integration cannot meet the demands of modern businesses. However, various organizations can effectively extract data from numerous sources. They struggle with combining, processing, curating, and transforming it. Therefore, services in the data fabric market are expected to increase.
By Deployment Insights
Based on the Deployment Insights of the market is segmented into On-Premise, Cloud.
The on-premises segment accounted for the highest share with a revenue percentage of over 61% in 2021. On-premises deployment seems to capture lower market share as compared to the cloud at the end of the forecast period as this includes major upfront capital costs and support and operating costs. Furthermore, it takes more time for the implementation as the software has to be installed on individual servers and computers. For example, In December 2020, SAP introduced SAP Data Intelligence 3.1. The version contains delivery and deployment, integration and connectivity, governance and metadata pipeline modelling, and intelligent processing. It is the on-premise version of the system of SAP Data Intelligence.
Cloud is expected to have the highest CAGR of the forecast period. Cloud offers flexibility, efficiency, and choice that businesses require. Companies are scaling their advanced analytics and artificial intelligence projects on the cloud, enabling them to make better data-driven decisions in a dynamically competitive industry. In October 2021 - NetApp Inc. launched the ONTAP data management platform with an objective of creating a multi-cloud hybrid data management tool. It offers powered integration and high-performance storage on a public cloud.
Regional Snapshots
By region, Insights into the markets in North America, Europe, Asia-Pacific, Latin America and MEA are provided by the study. The data fabric market in North America is leading primarily due to the presence of major technology companies and early adoption of advanced data management solutions. Also some of the key drivers for this market, especially in the sectors such as BFSI, healthcare, and retail, include the rising trend for big data, adoption of cloud, and growing need for data governance and security. In the U.S., specifically, there is strong investment in innovation, digital transformation, and AI capabilities.
The second major area for growth is Europe, and growth is primarily based on the fact that the way organisations have to keep their data private according to regulations like the GDPR. This kind of regulation, of course, makes companies use safe, complaint frameworks for managing data, and therefore the need for a data fabric. Its adoption has been far more prominent in Germany, the UK, and France. Here, there is further emphasis on digitalization in the industries of finance, healthcare, and more.
The Asia-Pacific region is the high-growth market, especially through rapid digitalization, cloud adoption, and big data analytics across industries in this market. In the markets of China, India, and Southeast Asia, growing investments in technology and the incessant demand for real-time data integration continue to fuel growth. Growth is very high in the expansion of e-commerce, telecommunication, and financial services markets.
In the case of Latin America and MEA, the growth has been constant due to the fact that organizations in all industries are shifting towards the digital process. The governments as well as businesses in these regions have been investing a lot in developing infrastructures regarding data management, especially in oil and gas, banking, and telecom industries, which increases the demand for data fabric solutions for optimized operations and better decision-making. These regional trends identify the rising global demand for data fabric solutions, with each region bringing in its unique drivers into play based on regulatory, technological, and industrial factors.
The report will cover the qualitative and quantitative data on the Global Data Fabric Market. The qualitative data includes latest trends, market players analysis, market drivers, market opportunity, and many others. Also, the report quantitative data includes market size for every region, country, and segments according to your requirements. We can also provide customize report in every industry vertical.
Study Period | 2024-32 |
Base Year | 2023 |
Estimated Forecast Year | 2024-32 |
Growth Rate | CAGR of 20.8% from 2024 to 2032 |
Segmentation | By Component, By Deployment, By Type, By Enterprise Size, By Business Applications, By Industry, By Region |
Unit | USD Billion |
By Component |
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By Deployment |
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By End-User |
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By Region |
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North America accounted for the highest Data Fabric% market share in terms of revenue in the Data Fabric market and is expected to expand at a CAGR of Data Fabric% during the forecast period. This growth can be attributed to the growing adoption of Data Fabric. The market in APAC is expected to witness significant growth and is expected to register a CAGR of Data Fabric% over upcoming years, because of the presence of key Data Fabric companies in economies such as Japan and China.
The objective of the report is to present comprehensive analysis of Global Data Fabric Market including all the stakeholders of the industry. The past and current status of the industry with forecasted market size and trends are presented in the report with the analysis of complicated data in simple language.
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Data Fabric Market Trends: Market key trends which include Increased Competition and Continuous Innovations Trends:
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