The global market for Augmented Analytics was estimated at US$21.9 Billion in 2023 and is projected to reach US$87.4 Billion by 2030, growing at a CAGR of 21.9% from 2023 to 2030. This comprehensive report provides an in-depth analysis of market trends, drivers, and forecasts, helping you make informed business decisions.
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.
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.
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.
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.
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 the Study
The report analyzes the Augmented Analytics market, presented in terms of market value (US$ Million). The analysis covers the key segments and geographic regions outlined below.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.Key Insights:
- Market Growth: Understand the significant growth trajectory of the Software Component segment, which is expected to reach US$63.8 Billion by 2030 with a CAGR of a 21.1%. The Services Component segment is also set to grow at 24.2% CAGR over the analysis period.
- Regional Analysis: Gain insights into the U.S. market, estimated at $6.2 Billion in 2023, and China, forecasted to grow at an impressive 21.2% CAGR to reach $13.4 Billion by 2030. Discover growth trends in other key regions, including Japan, Canada, Germany, and the Asia-Pacific.
Why You Should Buy This Report:
- Detailed Market Analysis: Access a thorough analysis of the Global Augmented Analytics Market, covering all major geographic regions and market segments.
- Competitive Insights: Get an overview of the competitive landscape, including the market presence of major players across different geographies.
- Future Trends and Drivers: Understand the key trends and drivers shaping the future of the Global Augmented Analytics Market.
- Actionable Insights: Benefit from actionable insights that can help you identify new revenue opportunities and make strategic business decisions.
Key Questions Answered:
- How is the Global Augmented Analytics Market expected to evolve by 2030?
- What are the main drivers and restraints affecting the market?
- Which market segments will grow the most over the forecast period?
- How will market shares for different regions and segments change by 2030?
- Who are the leading players in the market, and what are their prospects?
Report Features:
- Comprehensive Market Data: Independent analysis of annual sales and market forecasts in US$ Million from 2023 to 2030.
- In-Depth Regional Analysis: Detailed insights into key markets, including the U.S., China, Japan, Canada, Europe, Asia-Pacific, Latin America, Middle East, and Africa.
- Company Profiles: Coverage of major players in the Global Augmented Analytics Market such as Domo, IBM, Information Builders, Microsoft, Microstrategy and more.
- Complimentary Updates: Receive free report updates for one year to keep you informed of the latest market developments.
Some of the 19 companies featured in this Global Augmented Analytics Market report include:
- Domo
- IBM
- Information Builders
- Microsoft
- Microstrategy
- Oracle
- Qlik
- Salesforce
- Sap
- SAS
Table of Contents
I. METHODOLOGYII. EXECUTIVE SUMMARY2. FOCUS ON SELECT PLAYERSIII. MARKET ANALYSISCANADAITALYREST OF EUROPEREST OF WORLDIV. COMPETITION
1. MARKET OVERVIEW
3. MARKET TRENDS & DRIVERS
4. GLOBAL MARKET PERSPECTIVE
UNITED STATES
JAPAN
CHINA
EUROPE
FRANCE
GERMANY
UNITED KINGDOM
ASIA-PACIFIC
Companies Mentioned
- Domo
- IBM
- Information Builders
- Microsoft
- Microstrategy
- Oracle
- Qlik
- Salesforce
- Sap
- SAS
Table Information
| Report Attribute | Details |
|---|---|
| No. of Pages | 82 |
| Published | December 2024 |
| Forecast Period | 2023 - 2030 |
| Estimated Market Value ( USD | $ 21.9 Billion |
| Forecasted Market Value ( USD | $ 87.4 Billion |
| Compound Annual Growth Rate | 21.9% |
| Regions Covered | Global |
| No. of Companies Mentioned | 10 |
