Data fusion is the integration of data from various unrelated data sources in different formats to produce consistent, accurate, and more useful information which is not possible with individual data source. Data fusion solutions help in seamlessly detecting and incorporating the data to extract improved information for better decision-making processes. Within an organization, data fusion helps in delivering a comprehensive view of customer and business data collected from different big data sources. Data fusion helps in extending the data model by allowing fusion of any unrelated data dimensions.
Technological advances in sensors (Internet of Things) and data analytics technologies have enabled disparate integration in everyday objects. Every connected device and machine has sensors attached that play a significant role in gathering data. Collected data can then be used to derive meaningful insights via analytics and fusion. To understand the multidimensional information provided by these sensors, data fusion solutions are employed to provide a knowledgeable representation of the sensor output. This is also known as sensor fusion. Sensor fusion is stimulation of data from multiple sensors to induce intelligence and control a phenomenon with increased reliability and precision. Large scale usage of sensor systems are anticipated to propel demand for data fusion solutions over the forecast period.
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Organizations are increasingly developing their monitoring tools to gain more visibility into their data. Using data fusion tools, early detection of problems could be accomplished. Data fusion allows big data to integrate into relevant insights for quick problem resolution. It enables organizations to abstract insights from the different data types which could help them to proactively achieve a faster meantime. In the geospatial domain, there is a significant need to combine varied data sets into a unified data set which includes all of the data points and time steps from the input data sets. Increased use of data fusion solutions for geospatial applications is also expected to be a major factor driving the data fusion solution market over the forecast period. The data fusion solutions market has grown quickly owing to transition from traditional data management techniques such as data warehouse, to innovative data management techniques along with the rising adoption of data-driven decision-making processes across various industry verticals. However, fluctuations in regulatory policies and high-investment costs are expected to limit the market growth.
The global data fusion solutions market can be segmented based on components, business function, enterprise size, end-use, and region. Based on components, the market can be segmented into software and services (professional and managed). On the basis of business function, the global market can be categorized into information technology, sales & marketing, finance, operations, and others. Based on enterprise size, the market is categorized into Small & Medium Enterprises (SMEs) and large enterprises. In terms of end-use, the market has been bifurcated into BFSI, government, energy & utilities, healthcare, manufacturing, retail & consumer goods, telecom & IT, education, and others.
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Geographically, North America is anticipated to contribute major share in the data fusion solutions market during the forecast period. The region has a considerable presence of prominent technology players. Organizations in this region are early adopters of these solutions due to increasing technological advancements. Technological advancements such as improvements in data analytics are working in favor of the market in North America. Europe is also expected to show significant growth in data fusion market over the forecast period. Middle East & Africa and South America are also expected to contribute toward growth of the market with lucrative opportunities in the long run.
Some of the major players active in the development of data fusion solutions include Thomson Reuters, AGT International, ESRI, RELX Group (LexisNexis), Palantir Technologies, Cogint Inc., TDK Corporation (InvenSense), Clarivate Analytics, Merrick & Company, and INRIX.
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