The future of Oracle in pioneering the change in analytics with Oracle Fusion.

The future of Oracle Fusion Data Intelligence Platform in pioneering the change in analytics. (Source – Shutterstock)

Oracle Fusion Data Intelligence pioneering the change in analytics

  • The Oracle Fusion Data Intelligence Platform addresses modern analytical challenges, ensuring easy data use and accessibility.
  • The platform’s advanced features make analytics seamless and accessible across industries.

In the fast-paced world of technology, the last decade has marked significant transformations in analytics. Tracing this evolution, two key trends stand out, fundamentally altering the analytics landscape and setting a path for the future.

The inception of self-service analytics is the first significant transformation to the spotlight. Historically, analytics was predominantly an IT-centric task. IT professionals would compile data from various systems, formulate specialized solutions, and disseminate reports and dashboards to users. This conventional process was a linear and restrictive approach to analytics, limiting the scope and adaptability of data usage and exploration.

The inception of self-service analytics

However, the recent decade has heralded the rise of self-service analytics tools, a shift that has democratized data access and manipulation. Tools like those offered by Oracle Analytics have empowered business users, allowing them a more direct and hands-on interaction with data. This shift has empowered individuals across various sectors to consume, prepare, and explore data, offering direct insights and a deeper understanding of data narratives.

T.K. Anand, executive vice president at Oracle Analytics, speaks on the next generation of analytics - Oracle Fusion.

T.K. Anand, executive vice president at Oracle Analytics, speaks on the next generation of analytics.

As described by T.K. Anand, executive vice president at Oracle Analytics, in an interview with Tech Wire Asia, this transition has provided more than ease of access. It has broadened the horizon, enabling individuals to utilize data for everyday decision-making, and bolstering various business sectors, including HR, finance, and sales.

The second pivotal transformation is the sweeping move to cloud-based analytics solutions. Traditional on-premises software has given way to SaaS (Software as a Service) solutions, marking a significant transition adopted widely across the industry. This shift to the cloud has made managing large volumes of data more feasible by leveraging the benefits of low-cost data storage and elastic computing capabilities for processing large datasets.

Despite these advancements, challenges persist. The burgeoning volume of data, although more manageably stored and processed in the cloud, still presents a substantial burden for individuals seeking to explore and extract valuable insights. The task remains a complex and overwhelming endeavor, highlighting a pressing need for further innovation.

Meeting the challenges with the new Oracle Fusion Data Intelligence Platform

This necessity for advancement segues into the emerging trend that Anand highlights – the increasing role of machine learning and AI in analytics. In this next wave, the objective is to harness the power of AI and machine learning to automate data analysis, making it a continuous and integrated process. The goal is for these advanced technologies to work tirelessly behind the scenes to analyze data, uncover insights, offer recommendations, and guide decisions, even in your absence.

This innovative approach aims to reduce the human burden of data exploration, making insights more accessible and understandable, and ensuring that data-driven decision-making becomes integral to business operations.

Addressing these challenges, Oracle introduced the Fusion Data Intelligence Platform, a reflection of Oracle’s dual strategy as both a cloud infrastructure and applications provider. This platform emphasizes the importance of quality data, mitigating the “garbage in, garbage out” issue and allowing for a comprehensive view of customers, products, and employees.

“We provide rich, out-of-the-box analytics and machine learning models for each subject area and applications that use these analytics to offer tailored experiences,” Anand detailed, emphasizing the platform’s robust capabilities. Designed specifically for a fusion suite of applications, including ERP and HCM, the platform is built to complement the Oracle Analytics Cloud, part of OCI.

Anand highlighted the platform’s goal to enhance user experiences and ensure everyday data use for average users without requiring extensive data analyst expertise. This commitment guarantees that individuals in various roles can seamlessly integrate data insights into their workflow, reflecting Oracle’s comprehensive approach to making analytics more accessible.

“The objective is to simplify the adoption of analytics for Fusion customers, whether in ERP, financials, HR, or supply chain, and offer user experiences that enable effortless daily data use,” Anand added. Despite the advancements in self-service analytics and tool accessibility, the necessity for expertise remains a notable challenge, which the Oracle Fusion Data Intelligence Platform aims to alleviate.

Oracle Fusion Platform multifaceted impact: From healthcare to financial services

The Oracle Fusion Data Intelligence Platform stands out with its exceptional ability to efficiently integrate data from diverse industries such as health, financial services, and utilities. The platform does not merely compile data in isolated sets but establishes meaningful connections between them, enabling seamless integration between disparate systems, such as CRM and accounting, within its pre-integrated suite.

Oracle aims to create a unified and comprehensive data system in healthcare to enhance patient care and optimize healthcare operations. “We aim to collect data from multiple healthcare systems within a Health Network, particularly in the U.S., where the healthcare system is notably complex,” explains Anand. Known as Patient 360 in the Health Data Intelligence Platform, this initiative focuses on consolidating all patient data, including visits to primary doctors, specialist referrals, test locations, and pharmacies.

Oracle plans to extend this integration even further. Consider a healthcare provider network that utilizes various electronic health record (EHR) systems like Cerner and Epic alongside Fusion for HR and accounting. Anand outlines the ambitious goal: to combine all this data from non-Oracle EHRs like Epic and provide a comprehensive view of their patient population. This integration enables Oracle’s CRM system to initiate targeted health campaigns, enhancing general health trends for specific populations like the elderly, thereby ensuring efficient and effective outreach.

Moving beyond healthcare, Oracle’s approach to data integration resonates across various industries. Anand emphasized that building a “data lakehouse,” a unified and integrated data system, is undoubtedly complex, but Oracle is prepared to meet this challenge head-on.

The platform is committed to ensuring seamless data integration across industries beyond merely consolidating data. “We aim to provide out-of-the-box analytics and machine learning in AI,” Anand notes. With its extensive involvement in numerous business and industry-specific applications, Oracle is uniquely positioned to resolve diverse data challenges and ensure smooth integration, analytics, and insights across the board.

Oracle is taking healthcare to the next level with generative AI - Oracle Fusion.

Oracle is taking healthcare to the next level with generative AI. (Source – X)

In discussing the potential drawbacks or criticisms of Oracle’s Fusion Data Intelligence Platform, Anand acknowledges its early deployment stage. He expresses Oracle’s readiness to face and learn from the inevitable mistakes on this journey.

“In healthcare, the technology foundation from the Cerner side was not as robust because they were not on a modern technology stack like OCI,” Anand shares. This insight highlights an area where Oracle anticipates technological challenges and strives to transition the Health Intelligence Platform into a seamless, clean SaaS offering on OCI within the coming year. The ongoing work since the Cerner acquisition stands as a testament to Oracle’s commitment to overcoming these technological hurdles.

The fragmented nature of the U.S. healthcare system presents another significant obstacle. In contrast to the Fusion side, where data acquisition is more straightforward, healthcare data integration involves intricate connections to various diverse systems. “Cerner has already started developing connectors to different systems, including connections to Epic, a main competitor. This is something Oracle needs to continue to improve to effectively navigate the U.S. healthcare landscape,” Anand explains.

A global perspective: Insights from Singapore and Australia

Looking globally, Anand highlights the advances in countries like Australia and Singapore, noting their success in healthcare data integration. “Singapore represents a dream state that we aspire to achieve,” he adds, highlighting the nation as a model of efficient healthcare data management. Oracle’s prior experience developing a health data platform for Australia’s public health system also offers valuable insights and lessons for addressing the U.S. healthcare system’s unique challenges.

While acknowledging the distinct challenges, particularly in healthcare data integration and technology upgrade, Oracle is proactively working on solutions. The active development of connectors to diverse systems and the relentless pursuit of technological advancement demonstrate the company’s commitment to ensuring the Fusion Data Intelligence Platform’s success.

Generative AI: Crafting personalized and effective solutions

In AI’s diverse and dynamic landscape, Oracle significantly emphasizes leveraging generative AI to enhance platform and application dimensions. In healthcare, Oracle utilizes generative AI to bolster the connection between doctors and patients. As Anand outlines, generative AI paves the way for highly personalized, context-rich messages from healthcare providers to patients. This nuanced approach facilitates targeted outreach, reminding patients of upcoming check-ups, follow-up appointments, or necessary screenings, crafted based on their individual medical history.

“Imagine if your doctor is sending you a message, inviting you to come in and do your annual checkup…and also noting, ‘Hey, last time when we met, we had these follow-ups, and you might be due for this colon screening and whatnot.’ So, we can use generative AI to craft messages that provide a personalized experience,” Anand explains, highlighting the potential for enhanced patient care and engagement.

Expanding beyond healthcare, Oracle’s embrace of generative AI echoes in its Oracle Analytics Cloud (OAC). Within OAC, generative AI is an ‘analytics assistant,’ envisioned as a copilot for users navigating data exploration and visualization. This assistant allows users to engage in seamless, natural language interactions, ask questions, and receive comprehensible responses, enriching the data visualization experience.

When delving into the management and optimization of generative AI, Anand emphasizes the criticality of model tuning. He draws parallels with OpenAI, highlighting a collaborative approach with Cohere, aiming to augment foundational generative AI models for specific industry needs.

“In collaboration with Cohere, we’re working with various generative AI models, including the foundational model which you can adapt for patient care by feeding in extensive data about diseases, X-rays, and medical imaging. This process augments the dataset not available in the public domain, enhancing the intelligence of the model,” Anand notes.

Despite the revolutionary potential of generative AI, Anand acknowledges the potential pitfalls, emphasizing the dedicated, comprehensive training required to hone the models for reliable, accurate outputs. This extensive investment in training ensures the generative AI models’ robustness, ensuring they effectively serve their intended roles across diverse applications.

“Before we feel confident releasing them, substantial effort and computational capacity are invested in training these models to ensure their reliability and accuracy,” Anand concludes, underscoring Oracle’s commitment to realizing the full potential of generative AI within its ecosystem.