Global Augmented Analytics Market Surges at 27.2% CAGR as Digital Transformation Accelerates Worldwide
The global augmented analytics market is estimated at USD 9.2 billion in 2024, and is projected to skyrocket to USD 39.2 billion by 2030, registering a robust CAGR of 27.2% over 2025–2030. This growth is being fuelled by the rising urgency among organizations to automate data collection and data preparation, enabling them to convert raw data into actionable insights — ultimately driving revenue growth. As enterprises across sectors demand greater efficiency, cost-effectiveness, and productivity, conventional analytics tools are increasingly being augmented with artificial intelligence (AI) and machine learning (ML) for enhanced accuracy and predictive power — pushing companies to adopt advanced analytics at scale.
Similarly, the accelerating wave of global digitization —
from widespread smartphone and internet adoption among consumers to government
and enterprise digital transformations — is dramatically increasing the volume
of usable data. As more processes digitize and industrial automation spreads,
companies are deriving deeper, real-time insights from this data. This dual
push — of data explosion plus demand for smarter decision-making — is setting
the stage for augmented analytics to become central in enterprise strategies
worldwide.
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Key Insights
- The component-wise
segmentation reveals that software accounted for the largest
share in 2024, making up around 65% of the market. Meanwhile, services —
including consulting, deployment, operations, and managed support — are
identified as the fastest-growing segment over the forecast
period. This reflects increasing demand for end-to-end implementation and
ongoing support solutions as organizations adopt complex
augmented-analytics platforms.
- In
terms of organization size, large enterprises currently dominate,
thanks to their financial and technical capacity to procure and deploy
advanced analytics solutions. Their large customer bases and extensive
data generation make analytics especially valuable for optimizing
operations and improving customer experiences. At the same
time, small and medium-sized enterprises (SMEs) are projected to
grow fastest (around 29% CAGR), driven by broader cloud adoption,
cost-effective deployment, and increasing availability of AI-powered tools
tailored for smaller firms.
- Across industry
verticals, Banking, Financial Services & Insurance (BFSI) leads
in 2024 with about 25% revenue share — attributable to growing
demand for data-driven transaction management, customer analytics, fraud
detection, and personalized financial services. Meanwhile, healthcare is
expected to witness the fastest growth as hospitals,
life-sciences firms, and health insurers adopt AI-powered analytics to
manage patient data, optimize operations, and support decision-making —
often leveraging “no-code” platforms to minimize dependence on technical
experts. Other industries covered include retail, telecom & IT,
manufacturing, government, and logistics.
- On
the deployment mode front, cloud-based solutions represent
the larger share in 2024 and are forecast to maintain the higher CAGR
(≈ 28.0%) through 2030. The appeal lies in cost-effectiveness,
scalable storage and computing, automatic updates, and flexibility —
especially beneficial for SMEs and geographically distributed teams.
- From
a business-function perspective, sales and marketing commands
the largest slice (≈ 35% share in 2024) and also shows the fastest growth
trajectory. This highlights how companies increasingly rely on
augmented analytics to understand customer behavior, optimize pricing and
promotions, tailor digital marketing strategies, and derive insights from
consumer data streams.
- Regional
trends indicate that North America remains the largest regional
market in 2024, benefiting from strong R&D activity and early
adoption of advanced analytics tools by major industry players. At
the same time, Asia-Pacific (APAC) is forecast to be the fastest-growing
region, with rapid adoption of AI-based business intelligence in countries
like India, China, and Japan — driven by rising demand to handle
unstructured data and real-time streaming data analytics. Emerging
investments from local firms — for example, the launch of platforms such
as HyperSense — underscore growing regional momentum.
- On market
structure and competition, the landscape remains moderately
consolidated: a handful of major players hold significant market share,
while numerous smaller vendors offer customized solutions for niche
requirements. Larger IT firms benefit from financial strength and
broad enterprise clients; many are actively acquiring promising startups
to expand their analytics portfolios. This consolidation underscores
ongoing competitive dynamics but also suggests opportunities for
emerging players — especially those specializing in domain-specific
analytics or offering agile, cost-effective solutions to SMEs or emerging
markets.
- The technological
evolution — including integration of AI, ML, and natural language
processing (NLP) — is increasingly enabling augmented-analytics software
to generate sophisticated, actionable insight, support forecasting, trend
analysis, and decision support. As per analysis, ML-based analytics
may power over 300 enterprise use cases, potentially delivering
an average 15% return on investment for businesses leveraging
these tools.
- The global wave of digital transformation and industrial automation — from consumer online services to industrial IoT and healthcare diagnostics — is creating massive volumes of data from devices, applications, and services. Augmented analytics is increasingly critical in turning this data into insight — from recommending e-commerce products to healthcare diagnosis, infrastructure energy-use optimization, or predictive maintenance in manufacturing — thus boosting operational efficiency and sustainability.
- However, the market faces headwinds, notably around data privacy, security risks and regulatory compliance. Use of sensitive personal, financial, or medical data heightens risk of breaches — which could lead to severe financial losses, reputational damage, and compliance costs. Also, the high cost and technical complexity of augmented-analytics solutions — often requiring legacy system upgrades, specialized expertise, and ongoing maintenance — can deter small and medium enterprises or startups from adoption.






