Today’s healthcare industry is bursting with all kinds of data about patients, conditions, treatments, drug protocols, outcomes, etc. The problem is that this type of data is unstructured, making it difficult to capture and analyze by traditional means.
In this podcast, Peter ffoulkes from 451 Research discusses the recent launch of the new Intel Xeon processor E7 v2 chip. “What Intel did at this launch event was set their sights firmly on the RISC Unix vendors and basically introduced this new processor family as a new architecture to change a lot of the things that are going on.”
The analytics software category is 30 years old and about $15 billion in size. So why is it that information technology research and advisory firm Gartner has not published a standalone Magic Quadrant on the sector until this year? The answer: Gartner has meshed business intelligence with analytics for the past decade, and viewed them […]
“A Big Workflow approach to big data not only delivers business intelligence more rapidly, accurately and cost effectively, but also provides a distinct competitive advantage. We are confident that Big Workflow will enable enterprises across all industries to leverage big data that inspires game-changing, data-driven decisions.”
I’ve been monitoring a curious and lively discussion over on LinkedIn – “Is it necessary to have a Masters Degree to become a data scientist?” The comments I’ve seen have exhibited a number of points of view on the matter that I think are reflective of the questions on many people’s minds – both those wanting to become a data scientist and those wanting to hire a data scientist.
I am convinced we’re at an important inflection point in the timeline of the discipline of computer science. When compared to other disciplines like mathematics, physics and biology, computer science is a very young field, starting around 1964. But something is happening now, in 2014, that is propelling the field into a new evolutionary period.