James Fisher & Sons had hearty ambitions to build predictive maintenance capabilities for its customers' subsea cables -- but lacked the right data to do so. In a creative pivot, the IBM Data Science and AI Elite team delivered more than what the heritage engineering company bartered for --
Haruto Sakamoto, the Chief Information Officer at a Japanese multinational imaging company, had a few challenges to contend with. His business units had a presence in 180 countries worldwide with geographically-dispersed data warehouses and business intelligence applications in various locations.
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
Learn how marine solution provider James Fisher and Sons plc and the IBM Data Science and AI Elite team surge towards a renewable tomorrow with the electrifying power of data visualization built on IBM Watson Studio.
In this blog post, we’ll share real-world stories of how decision optimization technology delivers prescriptive analytics capabilities and opens the door to operational efficiency. We will also introduce you to the IBM data science and AI platform solutions that can deliver operational efficiency
Segmentation, targeting, positioning – how does an organization optimize these strategic approaches in the context of retention? Which factors should the segmentation take into consideration? This is where good information management and analytics come into play. Explore IBM’s solutions today:
A new generation is among us. They were born after 2010 into a world where technology is ubiquitous...We are witnessing the birth of a new intelligent species...While all under 10 years old, Siri, Watson and Alexa have already made an impact on the world and we can imagine the potential they all
Making the case for AI, or any nascent technology for that matter, can be a struggle for companies today. While large enterprises know they need to be fast, agile and innovation-obsessed to survive disruption, their age-old policies, antiquated systems, disconnected data and entrenched corporate
From reading the news headlines of yet another retail chain closing its stores, one can easily be left with the impression that we’re in a retail apocalypse. But in reality, the overall retail industry is very strong and healthy—especially online.
What we’re witnessing however, is a transformation
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
The conversation around data preparation has been evolving. What started as a push for self-service access for specific use cases has now expanded to operationalizing a data pipeline across the enterprise. The goal is to create efficiencies and eliminate workflow silos to propel data strategy
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