The global augmented analytics market size value reached around USD 13.95 billion in 2024. The rapid growth of augmented analytics is driven by the increasing need for businesses to harness large volumes of data for better decision-making and insights. Augmented analytics, powered by artificial intelligence (AI) and machine learning (ML), enables users to analyze data more efficiently and uncover hidden trends that were previously difficult to identify. This has led to increased adoption across various industries, including retail, healthcare, and finance. Companies like IBM and Microsoft are integrating AI-based analytics tools into their offerings to improve business intelligence. As a result, the industry is expected to grow at a CAGR of 28.60% during the forecast period of 2025-2034 to attain a value of USD 172.58 billion by 2034. Additionally, the growing focus on data-driven decision-making and the shift towards self-service analytics are further propelling market expansion.
In this blog post, we will explore the key drivers and trends shaping the augmented analytics market, the various market segments, and the competitive landscape. We’ll also delve into the future prospects and potential challenges facing businesses looking to adopt these advanced tools for better insights and decision-making.
Market Overview
Augmented analytics is transforming the way organizations process, analyze, and derive value from data. Traditionally, businesses used descriptive analytics, which only told them what had happened. However, with augmented analytics, powered by AI and ML, businesses can predict future trends, uncover hidden patterns, and automate insights, making it easier to make informed, data-driven decisions.
This shift towards augmented analytics is mainly driven by the overwhelming volumes of data that organizations generate and the growing need for businesses to leverage this data for competitive advantage. The market is growing rapidly as more organizations, both large enterprises and small and medium-sized enterprises (SMEs), adopt AI-driven solutions to streamline their data processes.
Market Segmentation
The global augmented analytics market can be segmented across several dimensions, including components, organization size, deployment type, and industry verticals. Let’s take a closer look at these segments.
By Components
Software: Augmented analytics software tools use AI and ML algorithms to automate data preparation, insight generation, and reporting. These tools allow users to interact with data in real-time, uncover hidden patterns, and receive actionable insights with minimal manual intervention. Key players like IBM and Microsoft provide such software solutions to enable smarter business intelligence (BI) across industries.
Services: Augmented analytics services include consulting, integration, training, and support. As companies deploy augmented analytics solutions, they often require expert guidance for smooth integration and to maximize the software’s capabilities. Services are critical in enabling businesses to fully adopt and benefit from augmented analytics by ensuring proper implementation and effective usage.
By Organization Size
Large Enterprises: Large enterprises are adopting augmented analytics solutions to manage and process vast amounts of data more effectively. These organizations benefit from the scalability and advanced features of augmented analytics tools, which help them make real-time decisions, identify market opportunities, and improve overall efficiency. The adoption of AI and ML-driven analytics tools is particularly strong among industries like banking, finance, and healthcare, where large data sets are common.
Small and Medium-Sized Enterprises (SMEs): SMEs are increasingly adopting augmented analytics solutions as they become more affordable and accessible. AI-driven tools have become user-friendly and adaptable, which allows smaller businesses to integrate these technologies without requiring a dedicated data science team. Cloud-based solutions are especially beneficial for SMEs, as they don’t need to invest in costly infrastructure.
By Deployment Type
On-Premises: On-premises deployment of augmented analytics solutions requires significant infrastructure investment, including servers and IT resources. While on-premises solutions provide greater control and security, they can be costly and require constant maintenance. Larger enterprises often prefer on-premises deployments for sensitive data handling.
Cloud: Cloud-based augmented analytics solutions are growing in popularity due to their cost-effectiveness, scalability, and flexibility. Cloud deployments allow businesses of all sizes to access advanced analytics tools without the need for extensive infrastructure. The cloud also facilitates collaboration across geographically dispersed teams, which is particularly important as more organizations embrace remote work.
By Vertical
Banking, Financial Services, and Insurance (BFSI): The BFSI sector is one of the largest adopters of augmented analytics tools. Financial institutions use these solutions for risk management, fraud detection, customer segmentation, and predictive analytics. AI-powered tools enable real-time decision-making, which is essential for navigating the fast-paced financial market and regulatory landscape.
Others: Augmented analytics is being applied across various industries such as healthcare, retail, manufacturing, telecommunications, and supply chain management. In healthcare, for example, AI is used to analyze patient data and predict disease outbreaks. In retail, businesses are using augmented analytics to improve inventory management, customer experience, and sales forecasting.
Market Dynamics
SWOT Analysis
Strengths: The rapid adoption of AI and machine learning, the increasing availability of big data, and the need for faster, more accurate decision-making are key strengths of the augmented analytics market. By automating insights, businesses can make better decisions more quickly, improving operational efficiency.
Weaknesses: High costs of implementation, especially for small businesses, data privacy concerns, and the complexity of integrating AI-driven tools into existing systems are some of the challenges. Businesses also need skilled personnel to interpret and act on the insights generated by augmented analytics tools.
Opportunities: There is a significant opportunity to expand into emerging markets, where the adoption of augmented analytics tools is still in the early stages. Additionally, the growing shift toward self-service analytics provides businesses with the ability to empower non-technical users to gain insights from data without the need for data scientists.
Threats: The competitive landscape is rapidly evolving, with many companies offering similar solutions. Additionally, data security and privacy concerns are growing, particularly as more sensitive customer data is being analyzed using AI and ML.
Competitive Landscape
The augmented analytics market is highly competitive, with several key players dominating the space. Companies like IBM, Microsoft, SAS, Tableau, and Qlik have integrated AI-based analytics into their business intelligence tools. These companies are focusing on enhancing their offerings with machine learning capabilities to automate data analysis and provide deeper insights. Smaller, specialized vendors are also gaining traction by focusing on niche markets and offering tailored solutions.
The competition is further intensified by the emergence of innovative startups, offering more agile and cost-effective solutions. As businesses increasingly demand real-time insights, many players are focusing on improving the speed, scalability, and ease of use of their solutions.
Future Outlook (2025-2034)
The global augmented analytics market is poised for substantial growth over the next decade. With an estimated CAGR of 28.60% from 2025 to 2034, the market will experience widespread adoption across industries and geographies. As AI and machine learning technologies continue to evolve, businesses will gain even more powerful analytics tools capable of providing deeper insights with less manual intervention.
The future of the augmented analytics market is likely to be shaped by advancements in AI, as well as the growing need for real-time data processing and actionable insights. The cloud will remain the preferred deployment model, especially for SMEs, while larger enterprises will continue to invest in on-premises solutions for sensitive data. Additionally, the increasing adoption of self-service analytics tools will empower business users to make data-driven decisions without relying on IT teams or data scientists.
