Couple days ago The Economist published an article titled "Linux and AWS: Cloud chronicles". The article stressed the undeniable importance of Linux and Amazon Web Services (AWS) in the upsurge of cloud computing and went on to explain how network effects have helped both grow in popularity. Despite similar paths, article predicted that their future will diverge and Linux will "happily plod along" whereas "AWS could end up dominating the IT industry just as IBM’s System/360, a family of mainframe computers, did until the 1980s".
Cloud analytics have changed the world big time. BIGDATAPUMP is an independent and objective solution provider using always the best fit technology for each purpose. Personally I have not written earlier about any specific analytics technology but this time I decided to make an exception. Microsoft and Azure deserve a special focus.
tl;dr: Gartner's Magic Quadrant for advanced analytics doesn't do a good job in helping to decide which analytics tool(s) to acquire. It also misses the point that open source projects are the real leaders in advanced analytics. Vendors that position themselves to complement open source projects rather than competing against them will be ones to watch.
Online advertisement is sold more and more through ad exchanges and auctions in particular. Ad auctions are an efficient way of buying and selling ad space and they will get more popular simply because they are better for everyone. In theory, auctions are "strategy proof" and hence you can't game them.
In BIGDATAPUMP, we have had two main BI visualization tool families in most active use in our customer projects: Qlik products and Tableau. However, also Microsoft’s Power BI started to look really promising, and in February it ranked really well in Gartner’s Magic Quadrant for Business Intelligence and Analytics Platforms.
Many people have a biased view of what econometrics really is and how it can be applied to business problems. In the following posts I will introduce some methods from applied econometrics, structural modelling and panel data/time series tools and show how they can be used to solve problems where standard statistical and machine learning tools do not work.
A solution implementation architect’s workflow nowadays might look like magic for an outsider. Not only can you ramp up components of your architecture with few clicks of a mouse but you can conjure whole multi-machine-solution infrastructures in a neatly organized, modular, portable and repeatable way.
Machine learning and “data science” have been popular topics in the tech circles for years now but their usage hasn’t followed the buzz in the more traditional industries. Companies have largely recognized the potential of data driven decision making, yet the wide spread adoption is still on the way.
In our earlier blog we discussed the use of personal data in regards of privacy in the European Union area, and continue now to look at the changes brought by the planned EU Data Protection reform.