Ich bin bereits zum dritten Mal in San Francisco und wieder mal sehr beeindruckt von der großartigen Architektur der Golden Gate Bridge. Diesmal lasse ich mich mit dem Boot nach Sausalito, auf die andere Seite der Bucht, übersetzen. Das dortige Orts-Museum erzählt dem Besucher von der bewegten Geschichte des Ortes
Tag: Analytics Lifecycle
Analytics platforms have a lot to live up to. The scope may be fairly straightforward, but expectations can be high, and there is a wide range of users and customers, all of whom have slightly different needs. This post explores what IT decision makers can expect from an analytics platform.
The business landscape changes daily and with that comes new “buzzwords.” You know those ones that really bug you – those where you kind of know what they mean, but they can mean lots of things and different things to different people. Well let me share one of mine with
The quote above is from Jason Handley, Director of Smart Grid Technology and Operations at Duke Energy. It says it all. Changing demands from customers and regulators requires utilities to think differently about every aspect of business – from what they offer to how they price and deliver it. Utilities
Over the summer, I had the pleasure of being involved in a major SAS study on enterprise readiness for artificial intelligence (AI). The study involved in-depth interviews between SAS consultants and 100 C-level senior executives from organisations across the EMEA region. It was designed to explore their understanding of AI,
Creating an architecture to support AI is about creating a modern platform for advanced analytics, and means being able to support all steps of the analytics lifecycle. Requirements An architecture for advanced analytics/artificial intelligence need to cover three main domains, data, discovery and deployment. Data Data is the foundation of
I'm sure I'm not the only one who has read and contributed to threads on the internet about all the different languages used for data mining. But one aspect that's been left out of most of these comparisons is that SAS is more than a 4th generation programming language (4GL).
There aren’t many things that keep me awake at night but let me share a recent example with you. I’ve been grappling with how to help a local SAS team respond to a customer’s request for a “generic enterprise analytics architecture.” As background, this customer organization had recently embarked on
One of the most frequent questions I’m asked by my students is which business analytics books to read to support their professional self-development. It is always hard to pick out the best books, especially because I like to mix classics and domain-specific references. I particularly like those that influence business
In the digital era data analysis is not even a necessity, but an everyday task of any company. The effectiveness and efficiency of decision-making processes has a key influence on whether the organization is successful or fails. The use of advanced analytics in order to obtain the best possible recommendations