Many organizations attempt to achieve “data nirvana” by having 100% complete information for any given business decision. In the customer analytics space, this is sometimes referred to as a “360 degree view of the customer.” However, we really never know everything about our customers. What we call a 360 degree view is really just the
While reading Jared Diamond’s excellent book on the rise and subsequent global dominance of Eurasian societies Guns, Germs and Steel, I was stopped in my tracks by his chapter on the evolution of technology entitled Necessity’s Mother. Diamond...
Both IDC and The International Institute of Analytics (IIA) discussed their big data and analytics predictions for 2014 in separate webcasts last week. Here is my summary of their predictions plus a few nuggets from other sources. IDC predicts...
The Data Scientists need a large set of skills, including business know-how, modelling and mathematics, plus programming. They are as hard to find as unicorns, or superheroes. I know this talent shortage first hand. Is the solution...
R remains popular with the PhDs of data science, but as data moves mainstream, Python is taking over. While R has traditionally been the programming language of choice for data scientists, it is quickly ceding ground...
Companies have long been involved in the analysis of how a company performed over time. As the history of big data shows, already for many years we try to understand how the organisations or the world around...
As the “Year of Statistics” comes to a close, I write this blog in support of the many statisticians who carefully fulfil their analysis tasks day by day, and to defend what may appear to be demanding behavior when...
Asked to name a big data company, many of us would say Google or Facebook or eBay. But for old-school giants such as General Electric Co. and Macy’s Inc., big data is fast becoming as central to...
As I frequently travel in data science circles, I’m hearing more and more about a new kind of tech war: Python vs. R. I’ve lived through many tech wars in the past, e.g. Windows vs. Linux, iPhone...
Top 5 most used tools were R (used by 70% of data miners), IBM SPSS Statistics, Rapid Miner, SAS, and Weka, while STATISTICA, KNIME, SAS JMP, IBM SPSS Modeler, and RapidMiner had the the highest satisfaction. Big...
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