Predictive modeling and advanced analytics

Discussion of technologies and vendors in the overlapping areas of predictive analytics, predictive modeling, data mining, machine learning, Monte Carlo analysis, and other “advanced” analytics.

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

August 19, 2013

Why privacy laws should be based on data use, not data possession

For years I’ve argued three points about privacy intrusions and surveillance:

Since that last point is still very much a minority viewpoint,** I’ll argue it one more time below.  Read more

August 14, 2013

The two sides of BI

As is the case for most important categories of technology, discussions of BI can get confused. I’ve remarked in the past that there are numerous kinds of BI, and that the very origin of the term “business intelligence” can’t even be pinned down to the nearest century. But the most fundamental confusion of all is that business intelligence technology really is two different things, which in simplest terms may be categorized as user interface (UI) and platform* technology. And so:

*I wanted to say “server” or “server-side” instead of “platform”, as I dislike the latter word. But it’s too inaccurate, for example in the case of the original Cognos PowerPlay, and also in various thin-client scenarios.

Key aspects of BI platform technology can include:

Read more

July 31, 2013

“Disruption” in the software industry

I lampoon the word “disruptive” for being badly overused. On the other hand, I often refer to the concept myself. Perhaps I should clarify. :)

You probably know that the modern concept of disruption comes from Clayton Christensen, specifically in The Innovator’s Dilemma and its sequel, The Innovator’s Solution. The basic ideas are:

In response (this is the Innovator’s Solution part):

But not all cleverness is “disruption”.

Here are some of the examples that make me think of the whole subject. 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

July 2, 2013

Notes and comments, July 2, 2013

I’m not having a productive week, part of the reason being a hard drive crash that took out early drafts of what were to be last weekend’s blog posts. Now I’m operating from a laptop, rather than my preferred dual-monitor set-up. So please pardon me if I’m concise even by comparison to my usual standards.

*Basic and unavoidable ETL (Extract/Transform/Load) of course excepted.

**I could call that ABC (Always Be Comparing) or ABT (Always Be Testing), but they each sound like – well, like The Glove and the Lions.

June 13, 2013

How is the surveillance data used?

Over the past week, discussion has exploded about US government surveillance. After summarizing, as best I could, what data the government appears to collect, now I ‘d like to consider what they actually do with it. More precisely, I’d like to focus on the data’s use(s) in combating US-soil terrorism. In a nutshell:

Consider the example of Tamerlan Tsarnaev:

In response to this 2011 request, the FBI checked U.S. government databases and other information to look for such things as derogatory telephone communications, possible use of online sites associated with the promotion of radical activity, associations with other persons of interest, travel history and plans, and education history.

While that response was unsuccessful in preventing a dramatic act of terrorism, at least they tried.

As for actual success stories — well, that’s a bit tough. In general, there are few known examples of terrorist plots being disrupted by law enforcement in the United States, except for fake plots engineered to draw terrorist-leaning individuals into committing actual crimes. One of those examples, that of Najibullah Zazi, was indeed based on an intercepted email — but the email address itself was uncovered through more ordinary anti-terrorism efforts.

As for machine learning/data mining/predictive modeling, I’ve never seen much of a hint of it being used in anti-terrorism efforts, whether in the news or in my own discussions inside the tech industry. And I think there’s a great reason for that — what would they use for a training set? Here’s what I mean.  Read more

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