Poor Man’s BI: Getting Started with Open Source Tools for Analyt. Article published in DMReview.com June 1, 2006, By Steve Miller source: In his award-winning book The World is Flat, Thomas L. Friedman argues convincingly that open sourcing is one of 10 significant “flatteners” changing the world. Though much of that disruption is just now starting to play out in the market, it’s safe to say that the open source software movement is no longer at the fringes of the technology.
While open source communities are thriving, businesses building on the open source model are just starting to hit their stride, struggling with strategies of how to make money surrounding “free” products. Consider a minimal architecture for BI, an architecture that, while not complete, can certainly deliver valuable startup intelligence to an enterprise.
The Python/PostgreSQL/OpenOffice/R BI platform presents a maturity that will be comforting to users of poor man’s BI. Python Open source is now established as an IT force. Picalo: Data Analysis and Fraud Detection Toolkit. Orange. Picalo: Data Analysis and Fraud Detection Toolkit. SciCraft. Data Mining Resources. Data Mining Resources “So what’s getting ubiquitous and cheap? Data. And what is complementary to data? Analysis. So my recommendation is to take lots of courses about how to manipulate and analyze data: databases, machine learning, econometrics, statistics, visualization, and so on.” Professor Hal Varian, Chief Economist at Google, speaking to the New York Times in February 2009. Togaware Resources OnePageR provides a growing collection of material to teach yourself R. Books on my Bookshelf Include: A literate, agile, approach to data mining projects means the data miner's toolbox will include R and LaTeX (for using Sweave).
Other Resources The Data Mining Software Repository makes available a collection of free (as in libre) open source software tools for data mining The Data Mining Catalogue lists many of the free and commercial data mining tools that are available on the market. Using R for Data Mining.