Any financial services firm using AI must revisit its approach to model risk management. The reason is that AI models are evolving faster than the rules-based models that were standard previously. If AI models perform inadequately, major operational losses can grow quickly. Watson OpenScale helps
To enable companies to get the most out of their machine learning, Cloud Pak for Data, IBM’s leading data and AI platform, partners with Datameer to build an end-to-end pipeline that collects, organizes, and analyzes data and helps infuse AI throughout the business.
This story is part of Analytics Heroes, a series of profiles on leaders transforming the future of business analytics.
Carving out her educational and career paths have led Frances Fiorello to where she is today. She manages to seamlessly blend aspects of both her personal and professional life: “I
Business intelligence (BI) tools are undergoing massive disruption. The powerful integration of artificial intelligence (AI) frameworks like natural language processing and automated predictive insights are transforming what BI can do for businesses.
Thankfully, making sense of this new world of AI
The IBM Data Science and AI Elite team showed that PostNord can predict non-deliveries of traceable items depending on address, weather condition, sizes and time of delivery. By leveraging AI, it’s possible to reduce non-deliveries by 50 percent annually, beneficial for both customers and PostNord
With the amount of choices surrounding big data analytics, data lakes and AI, it can sometimes be difficult to tell fact from fiction. With more than 40% of organizations expecting AI to be a “game changer,” it’s important to have a complete picture of the capabilities and opportunities available.
Imagine a day in the life of Sarah, a hypothetical Chief Data Officer at a major bank in South Africa. There are many expectations on her shoulders. She struggles to deliver business-ready data to fuel her organization and support the decision makers within the bank. It is her job to put in place a
DataOps is the orchestration of people, process, and technology to accelerate the quick delivery of high-quality data to data citizens. When done right, DataOps creates business value because users know what data they have, can trust the quality and its meaning, and use it without violating
High-quality data is the core requirement for any successful, business-critical analytics project. It is the key to unlock and generate business value and deliver insights in a timely fashion. However, stakeholders across the board are responsible for data delivery, quickly evolving requirements,