-
Data science and machine learning provides the basis for business growth, cost and risk reduction and even new business model creation. Implementing predictive analytics does present some challenges, however. The process can be complex, and it can be difcult to find data scientists and analysts with a mix of the right skillsets. A drag and drop, visual data science tool, exemplified by IBM SPSS Modeler, enables rapid creation of machine learning models while making it easy to collaborate with data science and analytics teams as a whole. In this paper, members of IT Central Station who use IBM SPSS Modeler share their experiences and ofer insights and recommended best practices for data science and machine learning.
-
Learn why Forrester named IBM Watson Discovery as a Leader in AI search in the 2019 Forrester Wave™: Cognitive Search report. You'll understand how AI search is unlike traditional search tools, including how it can empower your experts to do more meaningful work in less time.
-
Watson Discovery, IBM's AI powered search capability, doesn't just extract information; it crawls complex business documents and serves up answers with context, both on-demand and proactively via a simple natural language search, giving businesses insights impossible to derive otherwise. Clients across a variety of industries have seen positive results leveraging Watson Discovery; Australian energy company, Woodside, has experienced a 75% decrease in time employees spend searching for expert knowledge.
-
Written by IBM's conversational AI experts, this report demonstrates the market opportunity for conversational AI and outlines the use cases that will enhance your customer service and drive value across your organization.
-
Gaining full visibility into network traffic in complex environments requires integrating network packet analysis and system events. Learn more!
-
Read this Digital Dialogue to learn about new evaluation tools that promote employee retention by utilizing Self Development Objectives, and focusing on the employee as a person rather than their role in the firm.
-
The maturity of AI and machine learning (ML) use has reached a point where businesses of all sizes are using the technology as key strategic technology enablers. As an analytic and data science professional, you’re in control. You can empower people in centers of excellence (COE) and line of business (LOB) to C-level executives with scalable insights with trust and transparency. Data science competency is critical for your business to increase predictability, optimize operations and govern use of AI.
-
ESG recently completed testing of IBM Watson Studio and Watson Machine Learning, which are designed to enable organizations to accelerate the value they can extract from AI more easily. Testing examined how IBM Watson Studio and Watson Machine Learning collect data, organize an analytics foundation, and analyze insights at scale—with a focus on the ease of operationalizing AI and data science to improve trust, simplify compliance, and speed monetization.
-
Onboarding quality customer data across all sources is essential to improve the customer experience and meet compliance while cutting costs. Melissa’s AI-enabled active data quality solutions ensure your rising data volumes comply with business rules and are analytics ready.
-
The ability to integrate APIs from multiple sources is critical to success. New approaches to enterprise integration, backed by flexible, cloud-ready technologies, are necessary. Agile integration, an architectural approach, combines agile methods and practices with technologies for the purpose of rapidly integrating applications and data.
-
For the majority of organizations, digital business means pivoting to a culture of organizational agility, where the rapid pace of demand can only be satisfied by faster and more flexible development and delivery models. As most organizations do not have the luxury of completely rebuilding their technology foundation or immediately adopting new practices and mindsets, they are embracing gradual yet fundamental shifts in culture, processes, and technology to support greater velocity and agility. Learn 8 steps to guide your journey to cloud-native application development, including Red Hat® customer success stories.
-
Mayo Clinic started working with IBM Watson for Clinical Trial Matching in 2014. Over time, a team of Mayo experts determined optimal workflows and enhanced patient awareness about clinical trials. The system was implemented with a team of clinical research coordinators in the ambulatory practice for breast cancer.
Read this article to learn how Watson for Clinical Trial Matching has enabled Mayo to improve both enrollment metrics for breast cancer clinical trials as well as operational efficiency in its practice.