October 4, 2006

KXEN and Verix try to disrupt the data mining market

Data mining is hugely important, but it does have issues with accessibility. The traditional model of data mining goes something like this:

  1. Data is assembled in a data warehouse from transactional information, with all the effort and expense that requires. Maybe more data is even deliberately gathered. Or maybe the data is in large part acquired, at moderate cost, from third-party providers like credit bureaus.
  2. The database experts fire up long-running, expensive data extraction processes to select data for analysis. Often, special data warehousing technology is used just for that purpose.
  3. The statistical experts pound away at the data in their dungeons, torturing it until it reveals its secrets.
  4. The results are made available to business operating units, both as reports and in the form of executable models.

Each in its own way, KXEN and Verix (the imminent new name of the company now called Business Events) want to change all that.

KXEN believes they have found a one-size-fits-all set of data mining models and algorithms. This is not an SVM (Support Vector Machine), which they actually don’t offer any more, but rather something else from the fertile brain of SVM co-inventor Vladimir Vapnik, called Structured Risk Minimization. While the details have been published, they asked me not to write about them anyway for some kind of security-through-obscurity competitive reason. So let’s just say that these are not just the linear models they previously were or seemed to be stuck with. (For a small company with limited footprint, there sure is a lot of false information out there about how the whole thing works.)

A limited set of models lets one design a fairly simple user interface, especially when the models are good at helping one zoom through what otherwise can be annoying steps (like variable reduction, in which you choose which 80-90%+ of the data columns to disregard). Based on that relative simplicity, KXEN wants to let business users data mine directly, without being dependent on statistical specialists and their machinery. They position this as providing better results, because it allows rapid-cycle-time data exploration.

They also have a pickier statistical point to make, which is that their model-building process is streamlined and automated enough that it’s realistic to build lots of parallel “local” models, e.g. for each store or region in a retail chain. By way of contrast, in traditional data mining one would normally have one model used for all localities, but perhaps with additional variables indicating which locality the model was currently referring to. KXEN confidently believes that its way is superior, but in a recent discussion didn’t actually provide me with much beyond hand-waving to back that claim up.

I don’t actually have a good feel for how well these pitches are being received by the market. KXEN’s biggest sales successes seem to be via partnerships with various other analytics players, and it’s tough to judge whether that’s due more to price or to embeddability or to the fundamental merits of their overall case.

Business Events, imminently to be renamed Verix, is a raw start-up with a story even more extreme than KXEN’s: Sophisticated analytic results just delivered on a SaaS basis, with no thinking required by the customer at all. Obviously, this can only make sense if the universe of possible results is rather limited, and indeed it is.

Verix’s approach assumes a classical star set-up: A single measure/fact table and a complexly hierarchical set of dimensions. Verix looks exhaustively at time series on the facts, pulls out all series that are showing anomalies in two or more dimensions at once, and isolates exactly the point in the dimension network where the anomaly is occurring. If sales of frobalizing widgets in Houston are off plan, it identifies whether this is really a Houston issue or a Texas one, and whether it’s a problem just for frobalizers or – gulp – for the entire widget category.

The company claims that some insights you get this way just wouldn’t have been found by conventional BI. E.g., if frobalizers are down in Houston but up in Dallas, and the analysis stopped at Texas, nobody (not even the Houston district manager?) would ever know of the great Houstonian frobalizer downturn.

The company sounds like they’re working on all the right things to generalize this model. Initial interest in what they have seems to be concentrated in the pharmaceutical and CPG (Consumer Packaged Goods) industry, although there are a couple of paying telecom customers as well. One thing pharma and CPG have in common is that a lot of your raw data comes from third parties, such as IMS, and so your sales data are visible to your competitors anyway. Given that, it’s easy to believe that the SaaS nature of the service isn’t causing a lot of customer discomfort.

And by the way, IT departments aren’t involved in the Verix buying process whatsoever.


7 Responses to “KXEN and Verix try to disrupt the data mining market”

  1. Text Technologies»Blog Archive » KXEN is getting into text mining on October 4th, 2006 6:25 am

    […] Data mining challenger KXEN is getting into text mining, and they’re writing all their own stuff. Not even any Inxight filters. Weird. It will be interesting to see if they stick with that plan. • • • […]

  2. The Monash Report»Blog Archive » Have analytics vendors rediscovered ease-of-deployment? on March 16th, 2007 9:12 pm

    […] KXEN and Verix offer “easy” data mining technology. […]

  3. Text Technologies»Blog Archive » Predictive analytics vendors’ text mining sophistication on September 18th, 2007 2:33 am

    […] Steve Gallant of KXEN contacted me over the summer to show me KXEN’s new text mining capability. It was pretty basic bag-of-words stuff, which is still a lot better than nothing, and actually fits pretty well with KXEN’s general simplicity-centric strategy. […]

  4. The Monash Report»Blog Archive » The problem with dashboards, and business intelligence segmented on November 29th, 2007 12:41 pm

    […] Nor does this change when the warnings are the product of text or data mining. For example, despite a very interesting approach to generating alerts, at this point in its development Verix delivers them in uninspired ways. […]

  5. DBMS2 — DataBase Management System Services » Blog Archive » Outsourced data marts on May 8th, 2008 1:14 am

    […] haven’t heard much about Verix in a while, but their niche was combining internal sales figures with external […]

  6. Algebraix | DBMS2 -- DataBase Management System Services on June 5th, 2010 4:55 am

    […] Examples include Infobright (rough set theory), CrossZ/QueryObject (fractals), and KXEN (support vector machines). Algebraix (extended set theory) shows every sign of being another such case. An extended […]

  7. SAP is buying KXEN | DBMS 2 : DataBase Management System Services on September 11th, 2013 11:30 am

    […] that was already old news back in 2006, and KXEN had pivoted to a simpler and more automated modeling approach. Presumably, this ease of […]

Leave a Reply

Feed including blog about enterprise technology strategy and public policy Subscribe to the Monash Research feed via RSS or email:


Search our blogs and white papers

Monash Research blogs

User consulting

Building a short list? Refining your strategic plan? We can help.

Vendor advisory

We tell vendors what's happening -- and, more important, what they should do about it.

Monash Research highlights

Learn about white papers, webcasts, and blog highlights, by RSS or email.