Oracle Rolls Out a Broad Suite of Cloud Analytics Stack Products
Oracle’s CloudWorld push centers on a more capable analytics stack that tightens the link between data, applications, and observable business outcomes. At the event, the Oracle database and applications powerhouse rolled out a broad set of enhancements to its cloud business intelligence and analytics lineup. The focus ranges from reinforcing the core Oracle Analytics Cloud platform with new base features to extending analytics coverage through Oracle Fusion Analytics, which brings pre-built models, data pipelines, and dashboards into Oracle’s Fusion Applications. In a conversation with VentureBeat, Joey Fitts, Vice President of Product Strategy for Oracle Analytics, outlined why Oracle believes its analytics approach is uniquely positioned to serve business users at the application layer. He emphasized the advantage of understanding job roles, job data, workflows, decision triggers, and measurable outcomes all within the company’s own workflow, which he said provides a distinct edge for analytics deployment and action.
Business users sit at the heart of Oracle’s renewed analytics strategy. The marketing and product materials present a compelling view of Oracle’s marketecture, which combines Oracle Analytics Cloud, Oracle Analytics Server, and Fusion Analytics into a unified framework designed to empower decision makers across the enterprise. The most notable strategic move is the expansion of Fusion Analytics for customer experience, or CX, as part of the Fusion Analytics family. This new CX analytics capability is designed to reach a broader set of personas who influence revenue cycles, including chief revenue officers, campaign managers, demand generation leads, and renewal managers. The offering aggregates more than 50 key performance indicators in the revenue intelligence domain and supplies dashboards and pipelines that facilitate the transfer of data into the Oracle Autonomous Data Warehouse for timely analysis. The intent is to provide a ready-to-use analytics environment that can rapidly translate customer interactions and revenue signals into insights.
In addition to the CX expansion, Oracle is bolstering its Fusion Analytics product line with additional use cases that broaden the platform’s applicability across enterprise functions. Project Management Analytics is being added to Fusion Analytics for ERP, enabling deeper insights into project execution, cost control, resource utilization, and timeline adherence. For HCM, Oracle is introducing Diversity, Payroll and Learning Analytics, including employee skills matching, to help organizations manage workforce quality, development, and inclusion. In SCM, new Cost Accounting and Intra/Inter Organizational Transfer analytics are introduced to optimize cost and inventory management by providing more granular visibility into internal transfers and cost allocation. These additions collectively expand Oracle’s persona coverage to financial managers, accountants, P&L heads, audit managers, and a broader set of people managers, including heads of diversity, compensation managers, and talent acquisition leads.
A recurring theme in Oracle’s narrative is the goal of moving analytics deeper into business workflows, rather than placing them on the sidelines. This is reflected in explicit emphasis on the business application layer as a driver of analytics value. The argument posits that when analytics are closely tied to the workflow, the data, the decision triggers, and the outcomes are visible at the moment of action, the opportunities for optimization become more immediate and tangible. The vision presents analytics not as a separate discipline but as an integrated layer that informs and accelerates day-to-day business decisions within the ERP and CRM ecosystems. Taken together, the Fusion Analytics updates—coupled with the CX expansion and the new use cases—signal Oracle’s intent to deliver more comprehensive, end-to-end analytics coverage across enterprise functions, from front-office revenue generation to back-end operations.
AI scaling has become a focal point of enterprise conversations as organizations weigh performance, cost, and risk. Oracle’s announcements touch on the broader industry issue of scaling AI in production environments. The market context includes concerns about power consumption, token costs, and inference latency, all of which can dampen the perceived value of AI initiatives if not managed carefully. Enterprises are increasingly seeking strategies that optimize throughput and maintain quality of service while controlling total cost of ownership. The discourse around AI scaling is shifting from pure capability—more models, more data—to sustainable architectures that balance throughput with reliability and cost efficiency. The Oracle narrative suggests customers can pursue architecture choices that deliver predictable performance, even as workloads scale, and that align with established governance and security requirements.
Platform enhancements are complemented by a set of improvements designed to help customers realize value without abandoning existing investments. Oracle’s renewed emphasis on the analytics platform side includes a new Semantic Modeler, a tool designed to enable the construction of rich business models that translate raw data into consumable measures and attributes. Semantic modeling reframes data presentation from flat tables to dimensions and measures that better reflect business concepts, enabling faster comprehension and more intuitive dashboards. The tool uses a semantic modeling approach, along with a JSON-based encoding called Semantic Model Markup Language, or SMML, which supports both visual construction and code-based manipulation for developers who prefer programmatic control. Compatibility with Git-based source control systems enhances collaboration and versioning, reinforcing the platform’s appeal to development teams seeking enterprise-grade workflows.
In practice, Semantic Modeler works alongside existing visualizations by enabling more meaningful combinations of metrics and visual representations. Users can pair metrics with specific visuals to convey context more clearly without overwhelming dashboards with additional tiles or cards. Auto Insights is another notable capability, providing recommended visualizations based on the analytics performed by Oracle Analytics Cloud. These recommendations are designed to reduce guesswork in dashboard design and to help analysts surface the most informative representations for their datasets. The broader platform integration includes in-panel access to Oracle Cloud Infrastructure cognitive services, starting with OCI Vision, integrated in a way that keeps users within OAC. This approach eliminates the friction of provisioning external cognitive services separately and helps maintain a seamless user experience.
A focus on vertical analytics integration complements the platform-level innovations. Oracle positions itself as one of the few vendors capable of delivering analytics inside the context of both data and applications, a capability that is particularly meaningful for organizations pursuing end-to-end digital transformation. Oracle argues that the combination of cloud, data, analytics, and applications creates a turnkey solution that can outpace rivals that rely on disjointed stacks. While the article acknowledges other players in the market and their strengths, it underscores Oracle’s capacity to deliver rapid, scalable analytics within the core business processes that organizations rely on every day. The emphasis on a unified stack—cloud infrastructure, data warehouses, analytics, and enterprise apps—illustrates Oracle’s belief that the most significant value comes from reducing silos and enabling faster, more accurate decision-making across the enterprise.
In parallel with these platform-level developments, Oracle is intensifying its commitments to vertical analytics integration. The broader market context highlights Oracle’s assertion that it has the fastest-growing cloud relative to its peers and that the company’s analytics strategy aligns with a broader cloud-first, data-first approach. Oracle acknowledges that some well-known competitors may dominate specific segments, such as Tableau in data visualization or Power BI in enterprise BI, but the firm argues that the real value proposition lies in the ability to deliver a cohesive, turnkey environment that combines data, analytics, and applications in a single platform. The math behind this argument centers on reduced data movement, streamlined governance, simplified security models, and faster time to value for business users who rely on real-time insights to guide decisions.
Against this backdrop, Oracle sponsors a narrative of practical, enterprise-ready analytics. The platform’s new capabilities aim to translate complex data strategies into actionable intelligence that business users can act on within the tools and workflows they already use. Oracle emphasizes that Fusion Analytics for CX, along with the addition of multiple use cases across ERP, HCM, and SCM, reduces the friction that often accompanies analytics adoption. By aligning analytics more closely with business processes and by offering robust, ready-made analytics kits tailored to specific domains, Oracle seeks to shorten deployment cycles and improve the likelihood that analytics initiatives translate into measurable business outcomes.
As with any major product announcement, the practical implications for organizations depend on their particular data environments, governance structures, and existing investments. Enterprises considering Oracle Analytics will want to assess data model complexity, lineage, and the fit of pre-built Fusion Analytics content with their unique processes. At the same time, Oracle’s new features provide opportunities to accelerate the adoption curve for analytics by removing some of the heavy lifting involved in setting up models, data pipelines, and dashboards. The result is a more agile analytics capability that can scale with business needs and adapt to evolving priorities, whether in revenue operations, product management, supply chain optimization, or workforce analytics.
AI scaling, in particular, remains a topic of ongoing discussion for enterprises seeking to balance capability with cost and reliability. While Oracle highlights the potential for faster, more accurate insights through its integrated AI capabilities, the broader market continues to push for governance frameworks, observable metrics, and practical benchmarks that demonstrate tangible ROI. Oracle’s approach—anchoring AI capabilities within the analytics platform and aligning with business workflows—aims to deliver a more manageable, end-to-end experience for organizations wrestling with AI scale challenges. The focus is not merely on “more AI features” but on delivering a cohesive, cost-aware, and governance-friendly environment in which analytics can yield consistent value across departments and use cases.
Conclusion
At Oracle CloudWorld, Oracle’s analytics announcements reflect a deliberate effort to integrate data, analytics, and applications in a way that aligns with how businesses operate. The Fusion Analytics expansion, new use cases across ERP, HCM, and SCM, CX-focused analytics, and the Semantic Modeler with SMML all contribute to a more capable, developer-friendly, and business-oriented analytics stack. The platform’s emphasis on in-workflow analytics, coupled with in-platform cognitive services and vertical integration, positions Oracle as a compelling option for organizations seeking a turnkey cloud analytics solution tied to Fusion Applications. The evolving AI narrative emphasizes sustainable scaling and governance, signaling a broader move toward analytics that are not only powerful but practical and controllable within enterprise constraints. As Oracle continues to invest in core platform enhancements and domain-specific analytics content, enterprises will be watching how these capabilities translate into real-world outcomes, cost efficiencies, and accelerated decision-making across the enterprise.
