It has a complete new, more fancy, look. And besides the look, it has a few nice additions as well, first of all a blog (http://www.knime.com/blog)!
The first post on the blog ("Welcome to the new KNIME") is, and could only be, from the KNIME CEO Michael Berthold, explaining the changes in the web site.
The part that I find most interesting in this first post is the one about the "five pillars
upon which an open analytics platform stands on". Basically, he advocates the need for openness in data analytics, to make data analysts work more productively and more powerfully. (Check the new logo: it carries "open for innovation" on it).
In this post, openness is described as: integrative, transparent and trustworthy, flexible and agile, collaborative, and therefore more powerful (http://www.knime.com/open-for-innovation). Those are all features I can definitely subscribe for. Below I report my point of view as the average data analyst.
Integration and Collaboration
As a data analyst, I usually have two problems: no single tool can cover all of my needs and I do not work alone anymore. Being in a group, where everybody somehow specializes, calls for the need to integrate, complement, and share each other's work inside the same platform.
I know what you are thinking. The case described above is not your case. You are an expert in a given data analytics tool, you work alone, and this is enough for all your work requirements. Well, this might be true now, but you never know how things will change down the road in a few years or even months. Data capacity and IT infrastructure is changing quickly, challenging your analytical tool every day. Since you can not be sure of what is coming, you need to be always in control of your data strategy to be able to steer it at any moment. The analytics tool has to allow you to retain this control.
Flexibility and Agility
Data analytics has changed from an academic exercise to an enterprise productive activity. You have tons of data and a deadline. Work becomes a compromise between the analytics results and the approaching deadline. The more agile the analysis is, the faster the productionizing process is, the more time you have to improve your analytical models.
I find this discussion about openness a very interesting one.