This post is part of a series on how the 9 Laws of Data Mining from Tom Khabaza can be applied to analytics. You can find previous posts here. Law #9: “Law of Change”: All patterns are subject to change All patterns change — not only because the data changes, but because our understanding of […]
This post is part of a series on how the 9 Laws of Data Mining from Tom Khabaza can be applied to analytics. You can find previous posts here. Law #8: “Value Law” – The value of data mining results [and analytics] is not determined by the accuracy or stability of predictive models In analytics,
Mega-retailer Target recently announced that it would be closing the doors of eleven locations by February 1, 2015. The company’s press release stated, “The decision to close a Target store is only made after careful consideration of the long-term financial performance of a particular location.”[i] Target’s team of site selection experts have certainly been doing
Did Target Miss the Mark? Recent Closings Explained With Analytics Read More »
This post is part of a series on how the 9 Laws of Data Mining from Tom Khabaza can be applied to analytics. You can find previous posts here. The statement that “a piano makes music” is a clear misunderstanding. We all know that the pianist or any other musician is the source of the music and that
This post is part of a series on how the 9 Laws of Data Mining from Tom Khabaza can be applied to analytics. You can find previous posts on the 4C Blog. I grew up in the Amazon. (How and why is a topic for another forum.) Today, I am an analyst. Although there are few similarities between
Sculpting Your Clay: Analytics Lessons from the Amazon Read More »
We utilize software from Esri, which allows us to view and analyze data from a geographic perspective.
4CGeoWorks uses maps and other information management tools to show relationships, patterns and trends to help companies make smart decisions about the growth or downsizing of their business.
