This year will a significant shift in the data mining software sector, with fewer companies producing specific tools for one aspect of data mining and more firms striving to produce a top-to-bottom software ‘platform’. Already this month, SPSS Inc has bought UK-based Integral Solutions Inc for $7.1m, in order to get ISL’s Clementine data-modeling engine. SPSS CEO, Jack Noonan says that the acquisition broadens the scope of the firm’s products, which can now deal with data collection, transformation, cleaning, analysis and electronic report distribution. Noonan says that the only area of software that SPSS is not targeting is the warehousing business. According to Mike Norman, senior analyst at the Data Warehouse Tools Bulletin, this push towards offering a wide breadth of data mining software capabilities is the only game in town. He predicts that the result of this drive towards a platform will be a drastic increase in merger and acquisition activity in the sector, with companies buying up other firms to fill ‘holes’ in their software capabilities. Norman says that the SPSS purchase of ISL was a necessary move for the firm if it wants to compete with rivals such as SAS Institute Inc and Ardent. He claims that the losers in the data mining field will be the firms that continue to concentrate on individual applications targeted at a specific niche in the data mining field. Norman thinks that the take-up of data mining systems at the departmental level will increase quite dramatically this year and sees the Clementine engine as well placed to pick up on this mid-range custom. Jack Noonan concurs with this, describing data mining systems as more critical than they’ve been in the past. This is despite drains on IT department’s spending budgets such as the Y2K problem and European currency conversion. Noonan expects that IT departments will put application activity on hold in the second half of this year to concentrate on Year 2000 issues. However, he cites data mining software as one of the areas won’t be as badly hit because company’s can use data mining – looking for patterns in business data – as a problem solving tool that should save them money.
