Big Data Management
SAS | September 15, 2023
SAS has launched SAS Health, an end-to-end enterprise solution focused on healthcare analytics and data automation.
SAS Health is powered by a common health data model with predefined mappings to industry standards.
SAS' introduction of SAS Health is part of its $1 billion commitment to invest in AI-powered industry solutions over the next three years.
SAS, a globally renowned leader in AI and analytics, has recently unveiled SAS Health, an innovative end-to-end enterprise solution designed for analytics and data automation in the healthcare sector. This innovative platform streamlines health data management, enhances data governance and expedites the generation of valuable patient insights.
Within the healthcare industry, the cumbersome process of consolidating data from various systems and formats has been a significant impediment in the development and deployment of scalable healthcare analytic solutions that can benefit both individuals and communities. The patient insights generated through these analytics, ranging from the proactive identification of gaps in clinical staffing to the visualization of screening center distribution relative to the patient population, enable healthcare systems to gauge the quality of each patient interaction and make positive contributions to the care of individuals with complex chronic conditions.
In pursuit of a solution to the challenge of providing healthcare providers and payers with centralized, secure, and analytics-optimized data, SAS Health is powered by a common health data model with predefined mappings to widely recognized industry standards. With just a few secure connection details entered, customers can rapidly embark on addressing the most critical aspects of enhancing patient care.
Leveraging the capabilities of the analytics and AI platform SAS Viya, SAS Health facilitates the swift extraction of actionable insights, all while ensuring adherence to industry standards and regulations.
Gail Stephens, VP of Health Care and Life Sciences at SAS, commented, "Having one consistent, common data model built on a powerful advanced analytics platform is pivotal for hospital systems and the future of health care delivery. SAS Health offers an extraordinary opportunity to advance patient care and treatment through improved efficiencies in data and analytics frameworks, which ultimately will allow health care payers and providers to deliver better outcomes, more quickly."
[Source: Cision PR Newswire]
SAS Health's common health data model on SingleStore will serve as a central hub for integrating diverse health data with financial, clinical, and operational information, offering an efficient and adaptable approach that reduces costs and simplifies data accessibility. The cloud-native solution will streamline the ingestion of data from multiple industry standards, commencing with the Fast Healthcare Interoperability Resources (FHIR), all in a no-code/low-code format.
The global adoption of the FHIR industry data standard, which delineates how healthcare information can be exchanged among various computer systems, continues to grow. Prominent electronic health record (EHR) companies are swiftly embracing FHIR, and in the United States, the Centers for Medicare & Medicaid Services (CMS) have mandated its use.
The introduction of SAS Health is one of the outcomes of SAS' recent commitment to invest $1 billion in AI-powered industry solutions over the next three years. This investment, announced in May 2023, builds upon SAS' decades-long dedication to providing tailored solutions for various industries, including government, banking, insurance, retail, manufacturing, healthcare, energy, telecommunications, media, and more, to address their unique challenges effectively.
Salesforce | September 14, 2023
Salesforce introduces the groundbreaking Einstein 1 Platform, built on a robust metadata framework.
The Einstein 1 Data Cloud supports large-scale data and high-speed automation, unifying customer data, enterprise content, and more.
The latest iteration of Einstein includes Einstein Copilot and Einstein Copilot Studio.
On September 12, 2023, Salesforce unveiled the Einstein 1 Platform, introducing significant enhancements to the Salesforce Data Cloud and Einstein AI capabilities. The platform is built on Salesforce's underlying metadata framework. Einstein 1 is a reliable AI platform for customer-centric companies that empowers organizations to securely connect diverse datasets, enabling the creation of AI-driven applications using low-code development and the delivery of entirely novel CRM experiences.
Salesforce's original metadata framework plays a crucial role in helping companies organize and comprehend data across various Salesforce applications. This is like establishing a common language to facilitate communication among different applications built on the core platform. It then maps data from disparate systems to the Salesforce metadata framework, thus creating a unified view of enterprise data. This approach allows organizations to tailor user experiences and leverage data for various purposes using low-code platform services, including Einstein for AI predictions and content generation, Flow for automation, and Lightning for user interfaces. Importantly, these customizations are readily accessible to other core applications within the organization, eliminating the need for costly and fragile integration code.
In today's business landscape, customer data is exceedingly fragmented. On average, companies employ a staggering 1,061 different applications, yet only 29% of them are integrated. The complexity of enterprise data systems has increased, and previous computing revolutions, such as cloud computing, social media, and mobile technologies, have generated isolated pockets of customer data.
Furthermore, Salesforce ensures automatic upgrades three times a year, with the metadata framework safeguarding integrations, customizations, and security models from disruptions. This enables organizations to seamlessly incorporate, expand, and evolve their use of Salesforce as the platform evolves.
The Einstein 1 Data Cloud, which supports large-scale data and high-speed automation, paves the way for a new era of data-driven AI applications. This real-time hyperscale data engine combines and harmonizes customer data, enterprise content, telemetry data, Slack conversations, and other structured and unstructured data, culminating in a unified customer view. Currently, the platform is already processing a staggering 30 trillion transactions per month and connecting and unifying 100 billion records daily. The Data Cloud is now natively integrated with the Einstein 1 Platform, and this integration unlocks previously isolated data sources, enabling the creation of comprehensive customer profiles and the delivery of entirely fresh CRM experiences.
The Einstein 1 Platform has been expanded to support thousands of metadata-enabled objects per customer, each able to manage trillions of rows. Furthermore, Marketing Cloud and Commerce Cloud, which joined Salesforce's Customer 360 portfolio through acquisitions, have been reengineered onto the Einstein 1 Platform.
Now, massive volumes of data from external systems can be seamlessly integrated into the platform and transformed into actionable Salesforce objects. Automation at scale is achieved by triggering flows in response to changes in any object, even events from IoT devices or AI predictions, at a rate of up to 20,000 events per second. These flows can interact with any enterprise system, including legacy systems, through MuleSoft.
Analytics also benefit from this scalability, as Salesforce provides a range of insights and analytics solutions, including reports and dashboards, Tableau, CRM analytics, and Marketing Cloud reports. With the Einstein 1 Platform's common metadata schema and access model, these solutions can operate on the same data at scale, delivering valuable insights for various use cases.
Salesforce has additionally made Data Cloud accessible at no cost to every customer with Enterprise Edition or higher. This allows customers to commence data ingestion, harmonization, and exploration, leveraging Data Cloud and Tableau to extend the influence of their data across all business segments and kickstart their AI journey.
Salesforce's latest iteration of Einstein introduces a conversational AI assistant to every CRM application and customer experience. This includes:
Einstein Copilot: This is an out-of-the-box conversational AI assistant integrated into every Salesforce application's user experience. Einstein Copilot enhances productivity by assisting users within their workflow, enabling natural language inquiries, and providing pertinent, trustworthy responses grounded in proprietary company data from the Data Cloud. Furthermore, Einstein Copilot proactively takes action and offers additional options beyond the user's query.
Einstein Copilot Studio: This feature enables companies to create a new generation of AI-powered apps with custom prompts, skills, and AI models. This can help accelerate sales processes, streamline customer service, auto-generate websites based on personalized browsing history, or transform natural language prompts into code. Einstein Copilot Studio offers configurability to make Einstein Copilot available across consumer-facing channels such as websites and messaging platforms like Slack, WhatsApp, or SMS.
Both Einstein Copilot and Einstein Copilot Studio operate within the secure Einstein Trust Layer, an AI architecture seamlessly integrated into the Einstein 1 Platform. This architecture ensures that teams can leverage generative AI while maintaining stringent data privacy and security standards.
The metadata framework within the Einstein 1 Platform expedites AI adoption by providing a flexible, dynamic, and context-rich environment for machine learning algorithms. Metadata describes the structure, relationships, and behaviors of data within the system, allowing AI models to better grasp the context of customer interactions, business processes, and interaction outcomes. This understanding enables fine-tuning of large language models over time, delivering continually improved results.
Dremio | September 15, 2023
Dremio, a renowned easy and open data lakehouse solution provider, has recently introduced its next-gen Reflections technology, marking a transformative milestone in SQL query acceleration. Dremio Reflections facilitate sub-second analytics performance across an organization's entire data ecosystem, irrespective of data location. This groundbreaking technology is redefining data access and analysis, ensuring that valuable insights are derived efficiently and swiftly, all while reducing costs to merely one-third of a typical cloud data warehouse.
Reflections represent Dremio's innovative SQL query acceleration technology. Queries that leverage Reflections exhibit performance gains ranging from 10 to 100 times faster than their non-accelerated counterparts. This latest release introduces the Dremio Reflection Recommender, a pioneering feature that empowers users to accelerate Business Intelligence workloads in a matter of seconds. The Reflection Recommender automatically evaluates an organization's SQL queries and generates recommended Reflections to accelerate them.
Tomer Shiran, founder of Dremio, commented,
Dremio Reflections accelerate SQL queries by orders of magnitude, eliminating the need for BI extracts/imports and enabling companies to run their most mission-critical BI workloads directly on a lakehouse. With automatic recommendations and next-generation incremental updates, we've made it even easier for organizations to take advantage of this innovative technology.
[Source: Business Wire]
Reflection Recommender eliminates the need for labor-intensive manual data and workload analysis, making the process of obtaining the fastest and most intelligent query results effortless, requiring only a few simple actions. The user-friendly nature of the Reflection Recommender puts advanced query acceleration capabilities within the reach of all users, significantly saving both time and expenses.
Dremio has also refined the process of refreshing Reflections to further bolster query performance and drive cost efficiencies. It now intelligently refreshes Reflections on Apache Iceberg tables, promptly capturing incremental data changes. This innovative approach obviates the requirement for complete data refreshes, resulting in speedier updates and reduced compute expenses.
Dremio Reflections eliminates the need for data teams to export data from the data lakehouse into BI extracts or imports for analytical reasons and overcomes performance bottlenecks for BI dashboards and reports. In addition, Reflections negate the necessity of creating precomputed tables within the data lake or data warehouse to achieve sub-second performance for BI workloads, reducing the workload and complexity for data teams.
Dremio is a leading, easy and open data lakehouse solution provider, offering organizations the versatility of self-service analytics coupled with the functionality of a data warehouse and the flexibility of a data lake. Dremio's platform empowers users to harness the lightning-fast SQL query service alongside various processing engines, all on the same dataset. The company distinguishes itself through a pioneering data-as-code methodology akin to Git, which facilitates data experimentation, version control, and governance. This innovative approach enhances agility and empowers organizations to explore and manage their data resources with unprecedented efficiency. Furthermore, Dremio offers a fully managed service that expedites organizations' entry into analytics, allowing them to commence their data-driven journey within minutes.