Tag Archives: IBM SPSS

ML/AI Vendor Roundup: August 2018

Product enhancements, customer references, partnerships, acquisitions, financials, or other significant news for ML/AI vendors in August 2018. Alteryx Announces general availability for Release 2018.3. Key new bits: interactive visualization; enhanced Spark integration; a Jupyter notebook for Python scripting. Andrew Brust opines. Releases Q2 financials, revealing 54% YOY topline growth. Amazon Web Services Adds support for custom attributes to SageMaker model

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IBM Adds Spark Support to Analytics Server

With its customary PR blitz, IBM announces that it has added Spark integration to several products, including SPSS.   IBM gets a small pat on the head for adding Spark support to its Analytics Server software, under the premise that something is better than nothing. There is a very narrow pool of SPSS users who will benefit from this enhancement.  Spark integration is

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Dell Buys Statsoft

Dell announced this morning that it has acquired Statsoft, a privately held company that distributes Statistica, a suite of software for statistics and data mining.   Terms of sale were not announced. Founded by academics in 1984, Statsoft has developed a loyal following at the low end of the analytics market, where it offers a reasonably priced alternative to SAS

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Latest Forrester Analytics “Wave”

Forrester’s latest assessment of predictive analytics vendors is available here;  news reports summarizing the report are here, here and here. A few comments: (1) While the “Wave” analysis purports to be an assessment of “Predictive Analytics Solutions for Big Data”, it is actually an assessment of vendor scale.  You can save yourself $2,495 by rank-ordering vendors by revenue and get similar results. (2) The

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Leverage the In-Database Capabilities of Analytic Software

Many analysts have a strong preference for commercial analytic workbenches such as SAS or SPSS.  Both packages are widely used, respected by analysts, and each has strong advocates.  The purpose of this article is to point out that analytic users can benefit from the performance and simplicity of IBM Netezza in-database analytics without abandoning their preferred interface. Let’s start with

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