Big Data Management
WebWire | September 29, 2023
SAS, a leader in AI and analytics, is helping to revolutionize the use of clinical trial data so new medicines can be delivered to patients faster than before. After a thorough evaluation, SAS has been chosen by global biopharmaceutical company AstraZeneca to help increase efficiency and drive automation in the delivery of statistical analyses for clinical and post-approval submissions to regulatory authorities, via SAS’s cloud-based software and technologies.
SAS will support the redesign of clinical and patient data flow by delivering industry-leading analytics and AI, manage changing trial designs in a fast-evolving regulatory environment, enable data re-use, and help accelerate reporting and submission timelines. It will also deliver increased capacity, automation, interoperability, and flexibility to bring in and analyze diverse and novel patient data sources – such as those coming from wearables, sensors and precision medicine – as part of the submissions process.
This will be achieved by supporting the analysis and reporting phases with SAS® Life Science Analytics Framework and SAS® Viya®, a scalable and powerful cloud-based industry platform enabling swift decision-making regardless of data volumes or complexity using modern cloud technologies. This has the potential to provide significant productivity gains by driving faster time to market and reduced IT costs. The SAS and AstraZeneca partnership will enable teams across the organization to collaborate and increase clinical research innovation.
Christopher J Miller, VP Biometrics at AstraZeneca, said, This partnership with SAS supports the transformation of how we use clinical data to support our patient-centric approach and focus on getting medicines to patients faster than ever before. It will also allow us to introduce new ways of working and embrace new technologies and trial models to accelerate our portfolio.
Bryan Harris, SAS Executive Vice President and Chief Technology Officer, said, “I’m delighted that SAS is building on the strong relationship it has had with AstraZeneca over many years by being part of this transformation program. The work they do positively impacts the lives of millions of people around the world.
“This is exciting because we have solidified a great foundation between our companies, but we also recognize we are just scratching the surface. We pay attention to technology and the advancements in AI, and we thrive on thinking through how our technology blended with AstraZeneca’s expertise and insight can create new medical solutions for their customers.”
About SAS
SAS is a global leader in AI and analytics software, including industry-specific solutions. SAS helps organizations transform data into trusted decisions faster by providing knowledge in the moments that matter. SAS gives you THE POWER TO KNOW®.
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Business Intelligence
Oracle | September 25, 2023
Oracle introduces a data, analytics, and AI platform for Fusion Cloud Applications to enhance business outcomes.
The platform offers 360-degree Data Models, Prescriptive AI/ML Models, Rich Interactive Analytics, and Intelligent Applications.
Oracle plans to extend the platform to NetSuite and other industry applications, enriching analytics offerings.
Oracle has recently unveiled the Fusion Data Intelligence Platform, a cutting-edge data, analytics, and AI solution designed to empower Oracle Fusion Cloud Applications users to enhance their business outcomes through the fusion of data-driven insights and intelligent decision-making. This groundbreaking platform, which builds upon the foundations of the Oracle Fusion Analytics Warehouse product, offers business data-as-a-service with automated data pipelines, comprehensive 360-degree data models for critical business entities, interactive analytics, AI/ML models, and intelligent applications. These ready-to-use capabilities run on top of the Oracle Cloud Infrastructure (OCI) Data Lakehouse services, including Oracle Autonomous Database and Oracle Analytics Cloud, thereby facilitating complete extensibility across data, analytics, AI/ML, and application layers.
The Oracle Fusion Data Intelligence Platform presents the following suite of pre-built capabilities that are designed to empower Oracle Fusion Cloud Applications users to unlock the full potential of their data:
360-Degree Data Models: This will equip business users with a cohesive and comprehensible representation of their organizational data, allowing them to discern the intricate relationships between data and business processes. By providing a range of conformed data models based on Oracle Fusion Cloud Applications data and other data sources, this platform offers a 360-degree view of various facets of a business, including customers, accounts, products, suppliers, and employees.
Prescriptive AI/ML Models: Leveraging pre-configured AI/ML models, such as workforce skills assessment and customer payment forecasting, organizations can solve specific business problems by automating labor-intensive tasks, freeing up resources for strategic endeavors. Furthermore, it empowers organizations to rapidly analyze substantial datasets, uncovering invaluable insights and patterns that can drive business growth and efficiency.
Rich Interactive Analytics: Business users can seamlessly explore and visualize their data using pre-built dashboards, reports, and key performance indicators (KPIs). Additionally, Analytics Cloud features like natural language query, auto insights, and mobile applications allow quick access to data and insights.
Intelligent Applications: These applications go beyond providing insights offering intelligent recommendations based on pre-existing data models, AI/ML models, and analytics content. They enable organizations to make informed decisions swiftly, ultimately improving business outcomes.
The Fusion Data Intelligence Platform is a pivotal step in a long-term vision to transition from data and analytics to actionable decisions that drive business success. Importantly, this platform will extend its reach beyond Oracle Fusion Cloud Applications, with plans to offer the same foundational platform for NetSuite and across various Oracle industry applications, such as healthcare, financial services, and utilities, to facilitate cross-domain insights.
The Fusion Data Intelligence Platform includes an extensive portfolio of ready-to-use analytics for Oracle Fusion Cloud Enterprise Resource Planning (ERP), Oracle Fusion Cloud Human Capital Management (HCM), Oracle Fusion Cloud Supply Chain & Manufacturing (SCM), and Oracle Fusion Cloud Customer Experience (CX). These analytics offerings have been further enriched with the following additions:
Oracle Fusion ERP Analytics: The introduction of Accounting Hub analytics empowers finance teams to create a system of insights for accounting data sourced from Oracle Accounting Hub sub-ledger applications.
Oracle Fusion SCM Analytics: New Manufacturing analytics provide manufacturers with timely insights into work order performance, enhancing shop floor efficiency by rapidly identifying anomalies and continually optimizing plan-to-produce processes by connecting insights across supply chain data.
Oracle Fusion HCM Analytics: The addition of Inferred Skills, Payroll Costing, and Continuous Listening analytics equips organizational leaders with integrated workforce insights, covering employee skills, payroll trends and anomalies, and the efficacy of a continuous listening strategy at any given point in time.
Oracle Fusion CX Analytics: The new Quote-to-Cash analytics extend the analysis beyond the lead-to-opportunity pipeline by offering insights into how the price, contract, and quote process influences the overall customer experience.
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Big Data Management
Microsoft | September 22, 2023
AI models rely heavily on vast data volumes for their functionality, thus increasing risks associated with mishandling data in AI projects.
Microsoft's AI research team accidentally exposed 38 terabytes of private data on GitHub.
Many companies feel compelled to adopt generative AI but lack the expertise to do so effectively.
Artificial intelligence (AI) models are renowned for their enormous appetite for data, making them among the most data-intensive computing platforms in existence. While AI holds the potential to revolutionize the world, it is utterly dependent on the availability and ingestion of vast volumes of data.
An alarming incident involving Microsoft's AI research team recently highlighted the immense data exposure risks inherent in this technology. The team inadvertently exposed a staggering 38 terabytes of private data when publishing open-source AI training data on the cloud-based code hosting platform GitHub. This exposed data included a complete backup of two Microsoft employees' workstations, containing highly sensitive personal information such as private keys, passwords to internal Microsoft services, and over 30,000 messages from 359 Microsoft employees. The exposure was a result of an accidental configuration, which granted "full control" access instead of "read-only" permissions. This oversight meant that potential attackers could not only view the exposed files but also manipulate, overwrite, or delete them.
Although a crisis was narrowly averted in this instance, it serves as a glaring example of the new risks organizations face as they integrate AI more extensively into their operations. With staff engineers increasingly handling vast amounts of specialized and sensitive data to train AI models, it is imperative for companies to establish robust governance policies and educational safeguards to mitigate security risks.
Training specialized AI models necessitates specialized data. As organizations of all sizes embrace the advantages AI offers in their day-to-day workflows, IT, data, and security teams must grasp the inherent exposure risks associated with each stage of the AI development process. Open data sharing plays a critical role in AI training, with researchers gathering and disseminating extensive amounts of both external and internal data to build the necessary training datasets for their AI models. However, the more data that is shared, the greater the risk if it is not handled correctly, as evidenced by the Microsoft incident. AI, in many ways, challenges an organization's internal corporate policies like no other technology has done before. To harness AI tools effectively and securely, businesses must first establish a robust data infrastructure to avoid the fundamental pitfalls of AI.
Securing the future of AI requires a nuanced approach. Despite concerns about AI's potential risks, organizations should be more concerned about the quality of AI software than the technology turning rogue.
PYMNTS Intelligence's research indicates that many companies are uncertain about their readiness for generative AI but still feel compelled to adopt it. A substantial 62% of surveyed executives believe their companies lack the expertise to harness the technology effectively, according to 'Understanding the Future of Generative AI,' a collaboration between PYMNTS and AI-ID.
The rapid advancement of computing power and cloud storage infrastructure has reshaped the business landscape, setting the stage for data-driven innovations like AI to revolutionize business processes. While tech giants or well-funded startups primarily produce today's AI models, computing power costs are continually decreasing. In a few years, AI models may become so advanced that everyday consumers can run them on personal devices at home, akin to today's cutting-edge platforms. This juncture signifies a tipping point, where the ever-increasing zettabytes of proprietary data produced each year must be addressed promptly. If not, the risks associated with future innovations will scale up in sync with their capabilities.
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