Sam Wong brings answers through analytics during a global pandemic
This story is part of Analytics Heroes, a series of profiles on leaders transforming the future of business analytics.
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This story is part of Analytics Heroes, a series of profiles on leaders transforming the future of business analytics.
For years, IBM’s Netezza proved to be an excellent option for companies to run advanced analytics on a single data warehouse appliance without needing to set up and configure a traditional data warehouse. When IBM decided to replace Netezza with …
This story is part of Analytics Heroes, a series of profiles on leaders transforming the future of business analytics.
In early 2018, I had a conversation with some of my leadership team about what we could build to address our client’s most pressing needs. We knew data was growing at an exponential rate, and analytics were critical. Our decision …
IBM Cloud Pak for Data: Two years of modernizing your data for AI Read more »
Modern Data and AI application deployments are expanding through open source containers and hybrid multi-cloud support, but how can you achieve the benefits of infrastructure optimization and unified operationalization without vendor lock-in?
Around the world, companies are pivoting to adjust to an entirely new reality. Business-as-usual means something far different today than it did just a few months ago. For most enterprises, digital transformation isn’t just a distant business objective—now it’s an …
India’s current patient to physician ratio prevents thousands from receiving individualized care needed. iKure has developed a network of facilities with an integrated EMR system that brings care to rural communities in India, Vietnam, and Africa at an affordable and …
Using data to navigate the COVID-19 crisis From electronic healthcare records to mapping the human genome, data remains critical to quality healthcare.
The data science market is evolving rapidly. Businesses need to respond to a volatile climate and be able to scale cost-efficiently by automating AI lifecycle management. A key phase in the AI lifecycle is model selection, training, and deployment. Many …
The integrity and trustworthiness of data or any other master entity is enforced via data quality rules. Customers no longer want to rely on hand crafted rules that can number in the thousands, which in turn also need a lot of …
Machine learning capabilities abound with IBM Product Master Read more »