Dubai UAE - November 26 2025 - Kings Research has released its newest publication titled “Global Predictive Analytics Market Size Share Trends and Forecast 2025-2032”. The report presents a comprehensive overview of the technology landscape including segmentation by component deployment end use industry and region. It also offers valuable insight for software vendors analytics developers enterprise transformation leaders and investors who are evaluating the evolving predictive analytics market.
According to Kings Research the global predictive analytics market was valued at USD 18.45 billion in 2024. It is projected to reach USD 22.54 billion in 2025 and further grow to USD 109.90 billion by 2032 with a compound annual growth rate of 25.40 percent over the forecast period.
This robust growth is being driven by accelerating adoption of artificial intelligence (AI) and machine learning (ML) technologies across industries, as well as enterprises shifting from on-premise to cloud-native analytics architectures to access elastic compute power and deliver real-time decision-making.
Predictive analytics refers to the application of statistical modeling, machine learning and pattern recognition to forecast future outcomes based on current and historical data. It is widely used in demand planning, risk management, customer retention, supply chain management, financial forecasting, and asset management. As enterprises accelerate data driven operations, predictive analytics enables faster actionable decision intelligence for performance improvement across multiple sectors.
Kings Research has underscored the key trends reshaping the predictive analytics market. Among them are:
- Expansion of cloud-based predictive analytics platforms
Cloud infrastructure enables companies to scale analytics workloads quickly and to access elastic compute power when models require heavy processing. Cloud platforms also simplify data ingestion model deployment and collaboration across global teams which helps organizations move from pilot projects to production grade analytics.
The Organisation for Economic Cooperation and Development digital economy outlook highlights how cloud adoption underpins digital transformation and broader analytics diffusion across firms (Source: oecd.org).
- Rise of low code and no code model development environments
Low code and no code tooling allow business users and domain experts to build and validate models without deep programming experience. This shift reduces time to insight and expands the volume of workflows that can be automated inside a single enterprise.
Harvard Business Review highlights increasing corporate adoption of citizen data science and platform-driven analytics that empower teams to operationalize predictive modeling without writing code. As more organizations embed predictive analytics into daily decision cycles, accessible model-building environments are becoming a strategic priority for cycle speed, model iteration, and enterprise-wide automation.
- Integration with advanced machine learning frameworks
Enterprises embed industry grade machine learning libraries and frameworks into analytics pipelines to increase model sophistication and to process complex data types such as text images and time series. This integration improves detection of subtle patterns and delivers more reliable short term and medium term forecasts for operations and risk management.
A comprehensive survey from Northeastern University outlines modern machine learning approaches and practical pipeline steps for time series forecasting (Source:northeastern.edu). A hands on MIT thesis on demand forecasting describes practical methods model evaluation and maintenance approaches that support enterprise deployments (Source:mit.edu). With these frameworks in production firms can operationalize forecasting models at scale and integrate predictions directly into business workflows.
- Automation and real time analytics adoption
Automation of data pipelines model retraining and scoring enables analytics systems to deliver updated forecasts as new events occur in supply chains production lines and customer channels. Real time analytics reduces the lag between signal detection and action which supports faster operational response and lower disruption impact. The National Institute of Standards and Technology documents measurable gains in manufacturing reliability and maintenance outcomes when firms deploy predictive maintenance workflows and automated analytics for planning and scheduling (Source:nist.gov).
The predictive analytics market offers clear business value as firms invest in data driven decision making. It provides:
- More efficient operations through earlier detection of failure modes and improved scheduling
- Better customer engagement driven by predictive churn models and personalized offers
- Stronger financial planning with scenario based forecasts and rolling budgets
- Improved supply chain balance with timely demand signals and reduced waste
- Smarter asset management with condition based maintenance and longer equipment life
Regional Outlook
- North America: North America is expected to lead the market as enterprises continue investing in cloud infrastructure and AI to support forecasting and decision
- Asia Pacific: The region is anticipated to expand rapidly with accelerated digital transformation in e-commerce, fintech, and smart manufacturing.
Competitive Landscape
Vendors in the predictive analytics market focus on product reliability ease of deployment and integration with enterprise data stacks. IBM, SAP SE, Microsoft, SAS Institute Inc., Oracle, H2O.ai., Cloud Software Group, Inc., FICO, Alteryx, Salesforce, Inc., Verisk Analytics, Inc., Palantir Technologies Inc., Dataiku, LexisNexis, and Altair Engineering Inc. are major participants in this market.
These companies are adopting strategies such as delivering cloud-native analytics solutions, offering low-code model development environments to lower adoption barriers, and integrating end-to-end data pipelines. This promotes the development of predictive analytics platforms that can be deployed with minimal infrastructure investment and faster time-to-value.
To request a sample or access the full Global Predictive Analytics Market Size Share Trends and Forecast 2025 2032 report visit
https://www.kingsresearch.com/report/predictive-analytics-market-2985
About Kings Research
Kings Research is a global research and consulting firm that supports organizations in navigating emerging markets assessing investment potential and making evidence based strategic decisions.
All market data are sourced from Kings Research proprietary analysis, validated against credible government publications and peer-reviewed research papers. Examples cited include: Organisation for Economic Co-operation and Development (OECD), Harvard Business Review (HBR), Northeastern University (NEU), Massachusetts Institute of Technology (MIT), and National Institute of Standards and Technology (NIST).