Feeds

Digging into the future of data mining

Crystal ball gazing

Build a business case: developing custom apps

Comment The first thing to appreciate about data mining is that it should be thought of as R&D. That is, you do a bunch of research, some of which (but by no means all) is then deployable in the business. Moreover, some of it becomes so well established that it becomes a mass market product. For example, market basket analysis (which products have relationships to others) was once regarded as being as esoteric as anything else in data mining but is now so mainstream that it is embedded in all sorts of other environments. This is a trend that will continue, with techniques moving out of data mining R&D and into conventional deployment.

Historically, this move out of data mining has been to call centres, CRM, fraud and other standard applications. However, as complex event processing (CEP) engines take greater market share then we are likely to see increasing synergy with data mining. After all, CEP is essentially about identifying patterns and then detecting anomalies, which is exactly what data mining does.

There is a lot of hype about predictive analytics as opposed to data mining. If we take the case of market basket analysis, this is essentially saying that once we have identified that the sale of nappies is associated with beer sales (even if that is an urban myth) then we can make predictions about one based on the other. Useful, and certainly an increasing focus, but not really significantly different from what data mining has always been about.

Of course, there is also a trend to make data mining easier (and less costly) to do, but that is hardly surprising: it is common across the whole IT sector.

In my view, perhaps the most important trend is towards the integration of text mining and data mining. As yet, this is a relatively immature market but the fact is that most information held within business today is in unstructured format. While most of the discussion has been about Search that is simply about finding things related to a particular topic, while text mining is about finding patterns of information within text which, in the right context, is much more valuable. Moreover, with the advent of DB2 Viper we are likely to see the increased use of applications that employ both relational and XML-based information, in which case a combination of data and text mining makes sense.

While SPSS is one of the two major players in the data mining market it is the clear leader in the text mining space, not least because it is the dominant provider of market research software, and doing text mining on the back of the results of market research makes obvious sense. However, it is probably also the leading provider of combined text and data mining outside of this environment as well, so if I am right about the future of data mining, and its increased use with text capabilities, then SPSS is very well-placed.

SPSS is also in a good position because IBM has withdrawn the client component of its Intelligent Miner product and users thereof will be looking for a replacement offering, and SPSS has a much closer relationship with IBM than its major competitors, which it is looking to capitalise upon by picking up these users.

Copyright © 2006, IT-Analysis.com

Gartner critical capabilities for enterprise endpoint backup

More from The Register

next story
'Stop dissing Google or quit': OK, I quit, says Code Club co-founder
And now a message from our sponsors: 'STFU or else'
Why has the web gone to hell? Market chaos and HUMAN NATURE
Tim Berners-Lee isn't happy, but we should be
Microsoft boots 1,500 dodgy apps from the Windows Store
DEVELOPERS! DEVELOPERS! DEVELOPERS! Naughty, misleading developers!
Mozilla's 'Tiles' ads debut in new Firefox nightlies
You can try turning them off and on again
Apple promises to lift Curse of the Drained iPhone 5 Battery
Have you tried turning it off and...? Never mind, here's a replacement
Uber, Lyft and cutting corners: The true face of the Sharing Economy
Casual labour and tired ideas = not really web-tastic
Linux turns 23 and Linus Torvalds celebrates as only he can
No, not with swearing, but by controlling the release cycle
prev story

Whitepapers

Top 10 endpoint backup mistakes
Avoid the ten endpoint backup mistakes to ensure that your critical corporate data is protected and end user productivity is improved.
Implementing global e-invoicing with guaranteed legal certainty
Explaining the role local tax compliance plays in successful supply chain management and e-business and how leading global brands are addressing this.
Backing up distributed data
Eliminating the redundant use of bandwidth and storage capacity and application consolidation in the modern data center.
The essential guide to IT transformation
ServiceNow discusses three IT transformations that can help CIOs automate IT services to transform IT and the enterprise
Next gen security for virtualised datacentres
Legacy security solutions are inefficient due to the architectural differences between physical and virtual environments.