Data Fabric Market driven by growth of unstructured and structured business data across various industry, Data fabric market is segmented by component into the software and services categories
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Data fabric is a diversified data management platform that integrates real-time and historical data in an efficient manner. It is a software-enabled approach that helps a business connect distinct storage resources (such as cloud and on-premises storage) with data management and thereby restructure the latter.
The exponential growth of unstructured and structured business data across various industry verticals in the recent past is likely to be a key driver of the data fabric market over the forecast period, besides the rapid progression from conventional techniques of data management (including the transfer of data from a data warehouse) toward new ones. Implementation of data fabric solutions in sectors such as retail & e-commerce, health care, telecommunication & banking, and financial services & insurances and the growing need for real-time running analytics are driving factors for the data fabric market.
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Big Data Fabrics is expected to ease and automate the process of analysis, thereby stemming valuable insights out of the wide volume of data.
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The data fabric market is segmented by component into the software and services categories. In terms of usage, the market is segregated into the following groups: business process management, customer experience management, fraud detection & security management, sales & marketing management, governance, risk & compliance management, and others.
Data fabric is extensively utilized for fraud detection & security management as a result of which this segment is likely to occupy a significant share in the market at the end of the forecast period. The rising acceptance of data fabric implemented for business process management coupled with the need to resolve business-related problems in real time and modernized operations are likely to drive this segment over the forecast period.
Based on organizational size, the data fabric market is segmented into the following divisions: large enterprises and small & medium enterprises (SMEs). Small & medium scale enterprises are estimated to register a rapid growth rate owing to the spur in the demand for cloud-based data and on-demand fabric solutions due to their time-efficient and cost-effective properties.
On the basis of end-use, the data fabric market is divided into the following segments: telecommunication & IT, health care, banking, financial, services & insurance (BFSI), media & entertainment, manufacturing, energy, and others. BFSI held the dominant market share in 2016 and is predicted to retain its position over the forecast period, propelled by the increasing dependence on data generated from IT systems for financial transactions coupled with the need for data security.
Geographically, the data fabric market is distributed over North America, Europe, Asia Pacific, Middle East & Africa, and Latin America. Asia Pacific is anticipated to reflect a significant growth rate by the end of forecast period due to the rising adoption of Internet of Things (IoT) and smart technologies.
Government initiatives to develop smart cities in emerging countries such as China and India are estimated to motivate the data fabrics market in this region. North America constituted a prominent market share in 2016 and is likely to remain dominant over the forecast period due to the heavy investment in R&D of virtualization technologies in this region.
Significant presence of global vendors in this region is expected to be one of the crucial driving factors for the growth of data fabrics market. U.S. is expected to retain its dominance for the data fabrics market at the end of forecast period.
Key players in the data fabrics market include international Business Machines Corporation, Hewlett Packard Enterprise, Oracle Corporation, Teradata, NetApp Inc., Software AG, Splunk Corporation, VMware, Inc., Informatica, Denodo Technologies, and Informatica Corporation, among others.
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