Staying at the forefront of digital transformation means embracing constant change. It’s about staying nimble to customer demands, tapping into the pulse of a shifting market, and taking actions on insights as they’re developed. All of this can be made possible through continuous intelligence (CI
Cloud Pak for Data, IBM’s leading data and AI platform, partners with Figure Eight to help companies address this growing sensitivity around data and make sure that security lies at the heart of any data-driven AI strategy.
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.
When planning for a day of business, how do you calculate the numerous factors that may affect your bottom-line revenue? For Serco, a company which operates a bike-sharing service throughout London, the answer was in their data.
The future of banking is transforming. From changing customer behavior and expectations, rapid innovation in digital technology, burgeoning regulatory requirements, and the macroeconomic environment, the very definition of financial services is changing. For banks to stay relevant, they need to
The number of business segments requiring data to drive contextual insights is increasing. Leaders are seeking new ways to manage the pressures of delivering high-quality data faster across their businesses. To date, many of these projects have focused solely on ingesting data into a data lake
In the latest release of IBM Cloud Pak for Data, v2.5 has three key themes: Red Hat integration, new key built-in capabilities like Watson tools and runtimes, and a heavy focus on open source .(https://www.ibmbigdatahub.com/blog/announcing-cloud-pak-for-data-2-5). Open source is widely adopted in
Proper use of time series and location data in prediction and optimization can considerably boost the yield of data science and AI initiatives. Using them properly in AI applications has been challenging, but spatiotemporal functions, implemented as part of Analytic Engines in Watson Studio, are
At IBM, we led the humans to the moon and coined the term machine learning 50 years ago. Now we are helping organizations scale the ladder to AI to reap rewards in growth, productivity and efficiency with IBM Watson. This journey to AI mirrors the history of travel. In this blog, I’ll describe how
This unified end-to-end platform, Cloud Pak for Data, delivers these data and AI capabilities as container-based microservices that help to power new and existing enterprise applications to run on cloud or on-premises. The platform makes it easy to implement data-driven processes and operations and
IBM is announcing the latest update to the IBM Cloud Pak for Data platform, Version 2.5. We are extremely excited for this release, as it brings to a head three key areas we’ve been building towards over the last year and a half: Red Hat integration, new key built-in capabilities and last but not
Let’s say you’re the Chief Technology Officer of a bank or retailer struggling to infuse AI that aims to improve customer experiences. You likely face three main challenges:
Data sprawl: Your customer data is currently on multiple clouds, including on-premises and a cloud data lake storage