Advanced Analytics Market Growth Drivers, Strategic Insights, Opportunities, Key Players Analysis and Outlook 2026-2031

Advanced Analytics Market by Offering (Agentic (Copilots, Assistants, Autonomous Analytics Agents), Augmented (Insight Discovery, Automation, Orchestration), Predictive, Prescriptive), Delivery Mode (Cloud & Lakehouse, Edge) - Global Forecast to 2031.
The Advanced Analytics Market [https://www.marketsandmarkets.com/Market-Reports/advanced-analytics-market-58104148.html?utm_campaign=advancedanalyticsmarket&utm_source=abnewswire.com&utm_medium=referral] is expected to grow at a compound annual growth rate (CAGR) of 14.7% from its anticipated USD 97.17 billion in 2026 to USD 193.23 billion by 2031. Instead of just experimental innovation, the market is being driven by an expanding range of real-world company needs. The complexity of regulatory and compliance requirements is one of the main drivers, particularly in industries like government, healthcare, energy, and BFSI where businesses need to make auditable choices with data to support them fast and reliably. The lack of qualified analytics and data science personnel is another factor driving businesses to use platforms that automate modeling, insight production, and decision-making processes in order to lessen their reliance on specialist resources.

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By delivery mode, cloud-native & lakehouse-based delivery segment to lead market in 2026

By delivery mode, the cloud-native and lakehouse-based delivery segment is estimated to lead the advanced analytics market in 2026 due to its ability to unify data storage, processing, and analytics on a single scalable platform. Enterprises prefer this model for handling large and diverse datasets while reducing data movement and infrastructure complexity. Strong adoption of cloud services, support for real-time and AI-driven analytics, and lower total cost of ownership are key supporting factors. This delivery mode also enables faster deployment, elastic scaling, and seamless integration with machine learning and AI workloads, making it the preferred choice for enterprise analytics.

By application, product, digital, and IT operations analytics segment to register second-highest CAGR during forecast period

By application, the product, digital, and IT operations analytics segment is expected to record the second-highest growth rate during the forecast period as organizations focus on improving digital performance and system reliability. Growing dependence on digital platforms, cloud applications, and IT infrastructure is driving demand for analytics that monitor usage, performance, and user experience. Enterprises are also using these analytics to reduce downtime, optimize costs, and improve service quality. Increased adoption of DevOps, observability tools, and automated IT operations further supports strong growth in this application segment.

By region, North America to lead market during forecast period

North America is estimated to dominate the advanced analytics market in 2026 due to its early adoption of data-driven technologies and strong enterprise IT spending. The region benefits from the presence of major analytics vendors, cloud service providers, and a mature data ecosystem. Enterprises across the BFSI, healthcare, retail, and technology sectors have deeply embedded advanced analytics into operations. Strong regulatory requirements, availability of skilled talent, and continued investment in AI and cloud infrastructure further support North America's leading market position.

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Unique Features in the Advanced Analytics Market

AutoML removes heavy dependency on data science experts. Tools now automate model selection, feature engineering, and hyperparameter tuning. That speeds experimentation and cuts time from data to decision. It drives broader adoption beyond specialist teams.

Analytics is moving into live operations. Platforms ingest streaming data and deliver insights on the fly. That matters in fraud detection, predictive maintenance, and dynamic pricing. The shift reduces latency between insight and action.

Modern analytics supports plain-language queries and conversational interfaces. Business users can ask questions in English instead of learning SQL or Python. This lowers skill barriers and speeds self-service.

Regulation and trust issues are pushing explainability into analytics stacks. Systems now surface how models reach results. That reduces black-box risks, supports audits, and aligns models with compliance.

Major Highlights of the Advanced Analytics Market

Advanced analytics continues to expand as enterprises move from descriptive reporting to predictive and prescriptive decision-making. Investment is driven by measurable ROI. Boards now expect data-backed strategy, not intuition.

Machine learning, deep learning, and generative AI are embedded in most modern platforms. The market is shifting from dashboard-centric tools to model-driven intelligence. Automation in model building and optimization is accelerating adoption.

Batch reporting is no longer enough. Enterprises demand real-time analytics for fraud detection, supply chain visibility, pricing optimization, and operational monitoring. Streaming architectures are becoming standard in data stacks.

Most new deployments are cloud-based due to scalability and cost flexibility. However, hybrid and multi-cloud environments remain common because of data residency, compliance, and legacy systems. Vendors now compete on integration flexibility.

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Top Companies in the Advanced Analytics Market

The major players in the advanced analytics market include IBM (US), Microsoft (US), Google (US), Palantir (US), Oracle (US), SAS Institute (US), SAP (Germany), Adobe (US), Zoho (India), Sisense (US), Workday (US), Infor (US), Epic Systems (US), Savant Labs (US), AWS (US), Mu Sigma (US), and other vendors.

Moody's Analytics

Moody's Analytics is a leading provider of advanced analytics solutions focused on risk management, financial intelligence, and decision support for regulated and data-intensive industries. Its portfolio includes platforms such as the Moody's Analytics Risk Platform, CreditLens, and Intelligent Risk Platform, which support credit risk assessment, stress testing, forecasting, and regulatory compliance. Over the past few years, Moody's Analytics has increasingly embedded machine learning and AI into its solutions to enhance predictive accuracy, automate risk modeling, and improve scenario analysis. The company has also expanded its cloud-based delivery, enabling clients to scale analytics securely while meeting data governance and regulatory requirements. Strategically, Moody's Analytics continues to strengthen its market position through targeted acquisitions, partnerships with cloud and data providers, and continuous enhancements to its analytics platforms. Its focus on explainable analytics, model transparency, and regulatory alignment helps differentiate it in the advanced analytics market, particularly among banks, insurers, asset managers, and corporates seeking trusted, decision-ready insights.

Salesforce

Salesforce has emerged as a strong player in the advanced analytics market by tightly integrating analytics with its broader CRM and enterprise platform. Its analytics offerings, including Tableau, Tableau Pulse, and Einstein Analytics, enable organizations to analyze customer, sales, marketing, and operational data within business workflows. In recent years, Salesforce has accelerated innovation by embedding AI and generative capabilities through Einstein and Einstein Copilot, allowing users to generate insights, forecasts, and recommendations using natural language. The company continues to invest in product enhancements that improve embedded analytics, real-time insights, and decision automation across its ecosystem. Salesforce has also pursued strategic partnerships with cloud providers, data platforms, and system integrators to expand reach and integration depth. Its strategy focuses on making advanced analytics accessible to business users, driving adoption through ease of use, and positioning analytics as a core layer within customer-centric and enterprise decision processes.

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