According to a recent report by Reports and Data, the embedded analytics market is projected to reach USD 173.30 billion by 2030. Embedded analytics is a developing method of data analysis that allows users to explore data across various business applications and workflows to make informed decisions. Many companies are adopting embedded analytics due to its combination of business application and business intelligence tools in a single suite. Conventional business intelligence tools provide insights from data analysis and require support for real-time decision-making. In contrast, embedded analytics provides insights and actions in the same context by integrating analytics within business applications to establish real-time decisions.
These analytics techniques facilitate the use of analytical and diagnostic techniques in value-based enterprise applications. These techniques can operate outside the application but must be easily accessible within the application without requiring users to switch between systems. The integration of business intelligence platforms with application engineering allows users to select where in the business process the analysis should be established.
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During the forecast period, the Asia-Pacific (APAC) region is projected to have the highest compound annual growth rate (CAGR) of 14.7%. The embedded analytics market in the APAC region is an emerging market that focuses on several domains, such as telecommunications, IT, and BFSI verticals. More organizations in the APAC region are adopting a cloud-enabled approach, making cloud-based technology a preferred option for IT modernization.
Driving factors of
The increasing demand for data-driven decision-making processes among organizations.
The need for real-time insights and actions in business processes, which embedded analytics can provide.
The integration of business intelligence tools within business applications, leading to the adoption of embedded analytics.
The ability of embedded analytics to improve operational efficiency and reduce costs.
The rising demand for cloud-based solutions, which offer scalability, flexibility, and cost-effectiveness.
The increasing adoption of IoT devices, leading to a massive amount of data generation and the need for real-time analysis.
The growing popularity of mobile devices, which are used for data analysis, increasing the need for embedded analytics.
The increasing demand for customized solutions that meet specific business needs.
The emergence of artificial intelligence and machine learning, which are integrated into embedded analytics for more accurate predictions and insights.
The increasing adoption of embedded analytics in various industries such as healthcare, BFSI, retail, and manufacturing, among others.
Some of the notable innovations in the
Natural Language Processing (NLP): NLP is a technique that enables users to interact with data using natural language. It helps users to query data and obtain insights quickly and easily, without the need for technical expertise.
Predictive Analytics: Predictive analytics is an advanced technique that uses machine learning algorithms to analyze historical data and make predictions about future outcomes. It helps organizations to identify trends, patterns, and anomalies in data and make informed decisions.
Self-Service Analytics: Self-service analytics is a feature that allows users to perform data analysis without the need for IT support. It provides users with a user-friendly interface that enables them to explore data and obtain insights quickly and easily.
Augmented Analytics: Augmented analytics is a combination of machine learning, NLP, and other advanced techniques that help users to analyze data and obtain insights quickly and easily. It provides users with intelligent suggestions and recommendations based on their data analysis.
Cloud-Based Analytics: Cloud-based analytics is a solution that allows users to perform data analysis in the cloud. It provides users with scalability, flexibility, and cost-effectiveness, as they can access data from anywhere and at any time.
Mobile Analytics: Mobile analytics is a feature that enables users to access data and perform analysis using mobile devices. It provides users with flexibility and convenience, as they can perform analysis on the go.
Embedded AI: Embedded AI is a feature that integrates artificial intelligence into embedded analytics solutions. It provides users with more accurate predictions and insights, leading to better decision-making.
Major players in the market include
Microsoft Corporation, IBM Corporation, Sisense, Inc., TIBCO Software, Inc., SAS Institute, Inc., Microstrategy, Inc., SAP SE, Qlik Technologies, Inc., Birst, Inc., Opentext Corporation, Logi Analytics, Inc., and Information Builders, Inc.
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