IBM Hybrid Cloud Marketing VP Scott Hebner speaks with Big Data and Analytics Hub about the bets he’s placing on the offering to evolve into the company’s first AI platform and emulate WebSphere’s success.
Data can be an organization’s most valued asset, providing insights that help strengthen business. Knowing what works and what does not can help you invest more resources in what would work in the future. Learn more about the Watson Knowledge Catalog.
Rob Thomas, general manager of IBM Analytics, discusses the principles that guide the IBM Analytics business and have led to its latest major offering: IBM Cloud Private for Data, a platform for high-performance analytics that powers cloud-based applications so companies can be ready for AI.
Data science and machine learning provide the basis for business growth, cost and risk reduction and even new business model creation -- but implementing predictive analytics does present some challenges. IT Central Station members have shared tips that help organizations overcome the challenges in
How can we always be sure the stories we’re reading are accurate? Is there an agenda to distort facts to change opinions? Does a story assert falsehoods, misquote its subjects or rely on hyperbole? In short: is the news we’re consuming the truth? Or is it “fake news?” Meet Mike Tamir, an analytics
Natural disasters seem inescapable, leaving us feeling vulnerable in the hands of nature. How is this possible, given all the data and technology that surrounds us today? Can’t experts get better at prediction, and even try to stave off more natural calamities or more effectively reduce the loss of
In this Q&A, IBM financial services solution architect Irina Saburova discusses an insurance use case with IBM Data Science Marketing Lead Rosie Pongracz. In this scenario common to the insurance industry, an organization needs to adjust its operations based on upcoming weather event and
Running a machine learning pilot project is a great early step on the road to full adoption. To get started, you’ll need to build a cross-functional team of business analysts, engineers, data scientists and key stakeholders. From there, the process looks a lot like the scientific method taught in