Augmented Analytics

Augmented Analytics

Global Augmented Analytics Market to Reach US$87.4 Billion by 2030

The global market for Augmented Analytics estimated at US$21.9 Billion in the year 2023, is expected to reach US$87.4 Billion by 2030, growing at a CAGR of 21.9% over the analysis period 2023-2030. Software Component, one of the segments analyzed in the report, is expected to record a 21.1% CAGR and reach US$63.8 Billion by the end of the analysis period. Growth in the Services Component segment is estimated at 24.2% CAGR over the analysis period.

The U.S. Market is Estimated at US$6.2 Billion While China is Forecast to Grow at 21.2% CAGR

The Augmented Analytics market in the U.S. is estimated at US$6.2 Billion in the year 2023. China, the world`s second largest economy, is forecast to reach a projected market size of US$13.4 Billion by the year 2030 trailing a CAGR of 21.2% over the analysis period 2023-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 19.2% and 18.2% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 14.7% CAGR.

Global Augmented Analytics Market - Key Trends and Drivers Summarized

What Is Augmented Analytics and How Does It Work?

Augmented Analytics is an advanced approach to data analysis that leverages artificial intelligence (AI), machine learning, and natural language processing to automate and enhance data preparation, insight generation, and even the explanation of insights. Traditionally, data analysis has been a time-intensive process requiring skilled analysts to manually sift through data, apply statistical techniques, and interpret results. Augmented analytics disrupts this by automating these steps, using AI algorithms to cleanse, process, and analyze data faster and more accurately than human analysts alone. This process begins with data preparation, where machine learning algorithms help identify patterns, detect anomalies, and perform data cleansing to ensure accuracy and consistency. During analysis, AI-powered algorithms identify significant trends, correlations, and anomalies within vast datasets, producing insights that are not only accurate but often reveal previously overlooked patterns. Natural language generation (NLG) capabilities can then transform these findings into easy-to-understand narratives, summarizing complex analytics into accessible explanations. This empowers users across organizations—from analysts to non-technical professionals—to make data-driven decisions more confidently and efficiently. The potential for augmented analytics to democratize access to insights is vast, making it an invaluable tool in today’s data-rich environments.

What Sets Augmented Analytics Apart from Traditional Data Analysis?

Augmented analytics differs from traditional data analysis in its speed, scalability, and ability to deliver deeper insights across larger datasets with minimal manual intervention. Unlike traditional analysis, where analysts manually prepare data, apply statistical models, and generate insights, augmented analytics streamlines these processes through automation. This automation reduces the time and cost of data analysis, making insights available much faster. Additionally, augmented analytics uses advanced machine learning algorithms that can adapt and improve over time, continuously refining the models used for data processing and analysis. Another key differentiator is the use of natural language processing and generation, which allows augmented analytics tools to interpret complex data in real-time and present insights in a user-friendly format. This makes analytics accessible to non-experts, allowing professionals in marketing, operations, finance, and other departments to query data and understand results without needing a deep technical background. Furthermore, augmented analytics supports predictive and prescriptive insights, which go beyond historical data to anticipate future trends and recommend actions based on data-driven projections. This shift from descriptive to predictive analytics is transformative, as it provides businesses with the ability to proactively respond to changing market conditions rather than merely reacting to past events.

How Is Augmented Analytics Transforming Industries?

Augmented analytics has become a transformative force across industries, improving decision-making and operational efficiency in fields as diverse as healthcare, finance, retail, and manufacturing. In healthcare, augmented analytics enables providers to analyze patient data at scale, helping them to predict patient outcomes, identify at-risk populations, and personalize treatment plans based on historical patterns and AI-driven insights. In finance, augmented analytics streamlines fraud detection by continuously monitoring transactions for unusual patterns, allowing banks and financial institutions to reduce losses from fraudulent activities while improving compliance. In retail, augmented analytics plays a crucial role in demand forecasting, inventory management, and personalized marketing, enabling retailers to better anticipate customer preferences and ensure they have the right products available when and where they’re needed. Manufacturing companies use augmented analytics to optimize production processes by identifying inefficiencies and predicting equipment maintenance needs before issues arise, reducing downtime and operational costs. Furthermore, in customer service, augmented analytics helps businesses gain insights from customer feedback across multiple channels, improving service quality and enhancing the overall customer experience. Across these applications, augmented analytics empowers organizations to harness vast amounts of data in a way that is actionable and predictive, helping them achieve improved outcomes and competitive advantages in increasingly data-driven markets.

What’s Driving the Growth in the Augmented Analytics Market?

The growth in the augmented analytics market is driven by several factors, including advances in artificial intelligence and machine learning, the growing need for data-driven decision-making, and the rise in big data volumes across industries. AI and machine learning innovations have vastly improved the accuracy, speed, and usability of augmented analytics tools, allowing these systems to process complex data and produce insights in real-time. As organizations generate and collect more data from various digital channels, the need for tools that can efficiently analyze and extract valuable insights from this data has grown significantly. Augmented analytics meets this need by automating data analysis and allowing business users to interact with data through natural language interfaces, making insights accessible to a broader range of stakeholders. The trend toward democratizing data within organizations—empowering employees at all levels to access and leverage data insights—has further fueled the adoption of augmented analytics. In addition, the shift toward predictive and prescriptive analytics in industries such as finance, retail, healthcare, and manufacturing has increased demand for augmented analytics tools, which can provide forward-looking insights that inform strategic planning. With the continued expansion of data and the push for competitive advantage through better decision-making, the augmented analytics market is expected to experience sustained growth as organizations seek more efficient, accessible, and impactful ways to leverage their data assets.

SCOPE OF STUDY:

The report analyzes the Augmented Analytics market in terms of US$ Million by the following Component; Deployment; Vertical, and Geographic Regions/Countries:

Segments:
Component (Software, Services); Deployment (On-Premise, Cloud); Vertical (BFSI, IT & Telecom, Manufacturing, Government, Retail & eCommerce, Healthcare, Other Verticals)

Geographic Regions/Countries:
World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; and Rest of Europe); Asia-Pacific; Rest of World.

Select Competitors (Total 19 Featured) -

• Domo
• IBM
• Information Builders
• Microsoft
• Microstrategy
• Oracle
• Qlik
• Salesforce
• Sap
• SAS

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