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      SAS Institute

      Analysis of data mining powerhouse SAS, and the especially the relationship between SAS’s data mining products and various database management systems. Related subjects include:

      May 13, 2015

      Notes on analytic technology, May 13, 2015

      1. There are multiple ways in which analytics is inherently modular. For example:

      Also, analytics is inherently iterative.

      If I’m right that analytics is or at least should be modular and iterative, it’s easy to see why people hate multi-year data warehouse creation projects. Perhaps it’s also easy to see why I like the idea of schema-on-need.

      2. In 2011, I wrote, in the context of agile predictive analytics, that

      … the “business analyst” role should be expanded beyond BI and planning to include lightweight predictive analytics as well.

      I gather that a similar point is at the heart of Gartner’s new term citizen data scientist. I am told that the term resonates with at least some enterprises.? Read more

      October 6, 2013

      What matters in investigative analytics?

      In a general pontification on positioning, I wrote:

      every product in a category is positioned along the same set of attributes,

      and went on to suggest that summary attributes were more important than picky detailed ones. So how does that play out for investigative analytics?

      First, summary attributes that matter for almost any kind of enterprise software include:

      *I picked up that phrase when — abbreviated as RAS — it was used to characterize the emphasis for Oracle 8. I like it better than a general and ambiguous concept of “enterprise-ready”.

      The reason I’m writing this post, however, is to call out two summary attributes of special importance in investigative analytics — which regrettably which often conflict with each other — namely:

      Much of what I work on boils down to those two subjects. For example: Read more

      September 20, 2013

      Trends in predictive modeling

      I talked with Teradata about a bunch of stuff yesterday, including this week’s announcements in in-database predictive modeling. The specific news was about partnerships with Fuzzy Logix and Revolution Analytics. But what I found more interesting was the surrounding discussion. In a nutshell:

      This is the strongest statement of perceived demand for in-database modeling I’ve heard. (Compare Point #3 of my July predictive modeling post.) And fits with what I’ve been hearing about R.

      Read more

      September 11, 2013

      SAP is buying KXEN

      First, some quick history.

      However, I don’t want to give the impression that KXEN is the second coming of Crystal Reports. Most of what I heard about KXEN’s partnership chops, after Roman’s original heads-up, came from Teradata. Even KXEN itself didn’t seem to see that as a major part of their strategy.

      And by the way, KXEN is yet another example of my observation that fancy math rarely drives great enterprise software success.

      KXEN’s most recent strategies are perhaps best described by contrasting it to the vastly larger SAS.? Read more

      August 25, 2013

      Cloudera Hadoop strategy and usage notes

      When we scheduled a call to talk about Sentry, Cloudera’s Charles Zedlewski and I found time to discuss other stuff as well. One interesting part of our discussion was around the processing “frameworks” Cloudera sees as most important.

      HBase was artificially omitted from this “frameworks” discussion because Cloudera sees it as a little bit more of a “storage” system than a processing one.

      Another good subject was offloading work to Hadoop, in a couple different senses of “offload”: Read more

      July 12, 2013

      More notes on predictive modeling

      My July 2 comments on predictive modeling were far from my best work. Let’s try again.

      1. Predictive analytics has two very different aspects.

      Developing models, aka “modeling”:

      More precisely, some modeling algorithms are straightforward to parallelize and/or integrate into RDBMS, but many are not.

      Using models, most commonly:

      2. Some people think that all a modeler needs are a few basic algorithms. (That’s why, for example, analytic RDBMS vendors are proud of integrating a few specific modeling routines.) Other people think that’s ridiculous. Depending on use case, either group can be right.

      3. If adoption of DBMS-integrated modeling is high, I haven’t noticed.

      Read more

      April 15, 2013

      Notes on Teradata systems

      Teradata is announcing its new high-end systems, the Teradata 6700 series. Notes on that include:

      Teradata is also talking about data integration and best-of-breed systems, with buzzwords such as:

      Read more

      February 21, 2012

      The 2011/2012 Gartner Magic Quadrant for Business Intelligence Platforms — company-by-company comments

      This is one of a series of posts on business intelligence and related analytic technology subjects, keying off the 2011/2012 version of the Gartner Magic Quadrant for Business Intelligence Platforms. The four posts in the series cover:

      The heart of Gartner Group’s 2011/2012 Magic Quadrant for Business Intelligence Platforms was the company comments. I shall expound upon some, roughly in declining order of Gartner’s “Completeness of Vision” scores, dubious though those rankings may be.? Read more

      February 11, 2012

      Applications of an analytic kind

      The most straightforward approach to the applications business is:

      However, this strategy is not as successful in analytics as in the transactional world, for two main reasons:

      I first realized all this about a decade ago, after Henry Morris coined the term analytic applications and business intelligence companies thought it was their future. In particular, when Dave Kellogg ran marketing for Business Objects, he rattled off an argument to the effect that Business Objects had generated more analytic app revenue over the lifetime of the company than Cognos had. I retorted, with only mild hyperbole, that the lifetime numbers he was citing amounted to “a bad week for SAP”. Somewhat hoist by his own petard, Dave quickly conceded that he agreed with my skepticism, and we changed the subject accordingly.

      Reasons that analytic applications are commonly less complete than the transactional kind include: Read more

      February 8, 2012

      Comments on SAS

      A reporter interviewed me via IM about how CIOs should view SAS Institute and its products. Naturally, I have edited my comments (lightly) into a blog post. They turned out to be clustered into three groups, as follows:

      Next Page →

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