September 8, 2006

Where does data mining succeed, and why?

As previously noted, I have a Computerworld column coming out next week on data mining. The heart of the column is an enumeration of markets where data mining applications were having genuine success. Before I sat down to actually write the column, my list went something like this:

By the time I submitted the column, the list had morphed into:

For lots of examples and explanation of the categories, please see the column when available. (Theoretically that should be on the inauspicious date of September 11. In practice, it could be any time next week. I’ll post a link here when I know of one that works.)

While the latter version of the list may be slicker and more precise, which is why I went with it in the column, I think the former is more useful for a discussion of why those particular apps are the ones that get adopted. Simply put, data mining apps are concentrated at two extremes:

  1. Seeking “gold nuggets” of insight.
  2. Continuous process improvement.

What’s more, if I had to pick just one of those categories, I’d pick #2. The annals of BI are replete with examples of insights that just leapt out of reports and danced straight to the bottom line. But those stories are generally about reports and OLAP analyses, not full-blown statistical workups. Don’t get me wrong — I’m sure there are plenty of cases of data mining producing hugely valuable sudden insights. But, uh, I can’t think of any right now, at least not in the mainstream statistical analyses we usually think of when we hear “data mining.” (Perhaps some kindly product vendors will help me out with examples. If nothing else, there should be examples in the life sciences, forensics, product quality, etc. – i.e., in applications where there only ever was one single answer to discover in the first place. )

Where data mining does succeed all the time is in areas such as marketing efficiency improvement – mailing smarter, better targeting customer offers, and of course avoiding “bad guy” customers such as fraud or default risks in the first place. Text mining is something of an exception to that rule – but then, despite its name, it’s not clear that all of text mining should be classified as data mining anyway. Some of it is just “knowledge/fact/information extraction”, which generally is used to inform analytic technologies of some sort or other. But those can be regular BI or text search or whatever, with data mining just being one of the candidates on the fact-consumer-technology candidate list.


8 Responses to “Where does data mining succeed, and why?”

  1. The Monash Report»Blog Archive » My actual column on data mining on September 11th, 2006 11:59 pm

    […] In a couple of recent posts about data mining, I referenced a Computerworld column due to run September 11. Wonder of wonders, they got it posted on the very first day. Here’s a link. • • • […]

  2. Curt Monash on September 12th, 2006 12:05 am

    James Taylor attempted to post this comment but for some reason failed. So I’m doing it for him.

    I’m doing this from vacation on Grand Cayman, after a hard day of snorkeling (revelation of the day — “beautiful squid” is NOT an oxymoron), so please forgive my lack of effort to fix word wrap and any other formatting issues. (But at least I fixed the typo in one of the URLs …) CAM


    Think you are completely correct on this one – the ongoing management
    and improvement of decisions using data mining is where the money is.
    One of the challenges this raises is how to “operationalize” the insight
    that comes from data mining. One way is to mine the data for business
    rules and operationalize those and another is to mine the data so as to
    produce executable predictive analytic models.

    I have written about a one-time immediate improvement
    ( ) but more
    have the kind of ongoing success you discuss. There’s a lot of confusion
    around data mining, analytics, predictive analytics and so on so it
    comes up a lot on my blog at

    One last thing – this poll at KD Nuggets was fun

  3. DBMS2 — DataBase Management System Services»Blog Archive » Data warehouse and mart uses – a tentative taxonomy on September 24th, 2006 1:29 am

    […] Finally, there is hardcore data crunching. Data mining fits that bill, but so does heavy SQL-only data exploration (aka “The Query That Ate Pittsburgh”). This is where a small number of expert users extract value from massive data stores. Scheduled reporting can also fit into this category at aggressive enterprises. Here is where the high-end data warehouse vendors – e.g., Teradata, IBM (mainframe DB2), and the data warehouse appliance startups – really shine. At smaller enterprises, other kinds of data stores also suffice. I have a careful list (two versions of the same list, actually) of data mining app categories over on the Monash Report. It’s a good start on a list of apps for this whole category. […]

  4. The Monash Report»Blog Archive » The problem with dashboards, and business intelligence segmented on October 5th, 2006 9:02 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. The Monash Report»Blog Archive » Three ways to market analytics-related technology on March 19th, 2007 4:57 am

    […] Data mining and predictive analytics are mainly information access plays. Yes, the information being accessed is calculated rather than raw. Yes, I believe that the heart of the data mining market is continuous process improvement. Even so, what users buy from the vendors is usually little more than information toolkits. […]

  6. The three principal kinds of analytic business benefit | DBMS 2 : DataBase Management System Services on March 10th, 2011 2:26 am

    […] 2006 I rattled off a long list of early-warning uses for text analytics. The same year I discussed application areas for data mining and came up with a list much like the one in this post — lots of early-warning or other […]

  7. Historical notes on the departmental adoption of analytics | Software Memories on January 17th, 2012 3:05 am

    […] that one might think of as having to do with artificial intelligence – e.g. expert systems, predictive analytics* and text analytics — have wound up with applications being concentrated in the same few […]

  8. Analytic application themes | DBMS 2 : DataBase Management System Services on April 25th, 2013 3:42 am

    […] wrote up a different list of analytic use cases back in […]

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.