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 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

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

May 28, 2012

Quick-turnaround predictive modeling

Last November, I wrote two posts on agile predictive analytics. It’s time to return to the subject. I’m used to KXEN talking about the ability to do predictive modeling, very quickly, perhaps without professional statisticians; that the core of what KXEN does. But I was surprised when Revolution Analytics told me a similar story, based on a different approach, because ordinarily that’s not how R is used at all.

Ultimately, there seem to be three reasons why you’d want quick turnaround on your predictive modeling: Read more

April 24, 2012

Three quick notes about derived data

I had one of “those” trips last week:

So please pardon me if things are a bit disjointed …

I’ve argued for a while that:

Here are a few notes on the derived data trend. Read more

March 31, 2012

Our clients, and where they are located

From time to time, I disclose our vendor client lists. Another iteration is below, the first since a little over a year ago. To be clear:

For reasons explained below, I’ll group the clients geographically. Obviously, companies often have multiple locations, but this is approximately how it works from the standpoint of their interactions with me. 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:

January 18, 2012

KXEN clarifies its story

I frequently badger my clients to tell their story in the form of a company blog, where they can say what needs saying without being restricted by the rules of other formats. KXEN actually listened, and put up a pair of CTO posts that make the company story a lot clearer.

Excerpts from the first post include (with minor edits for formatting, including added emphasis):

Back in 1995, Vladimir Vapnik … changed the machine learning game with his new ‘Statistical Learning Theory’: he provided the machine learning guys with a mathematical framework that allowed them finally to understand, at the core, why some techniques were working and some others were not. All of a sudden, a new realm of algorithms could be written that would use mathematical equations instead of engineering data science tricks (don’t get me wrong here: I am an engineer at heart and I know the value of “tricks,” but tricks cannot overcome the drawbacks of a bad mathematical framework). Here was a foundation for automated data mining techniques that would perform as well as the best data scientists deploying these tricks. Luck is not enough though; it was because we knew a lot about statistics and machine learning that we were able to decipher the nuggets of gold in Vladimir’s theory.

Read more

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