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IBM Fleshes Out Data Intelligence, Information Managment, Data Warehouse Portfolios

The revamped DWE 9.1 does little to address Big Blue’s MOLAP value proposition, which has been MIA since last summer.

• By Stephen Swoyer
• March 22, 2006

It was another busy week for IBM Corp. on the business intelligence (BI) and data warehousing (DW) fronts. First, Big Blue snapped up partner and data quality specialist Language Analysis Systems (LAS). Then, IBM updated its DB2 Warehouse Edition with a new version 9.1 release, which now includes an Eclipse-based design tool, flow-based models for SQL transformations and data mining, and a new Web-based admin console based on Rational Data Architect.

In retrospect, IBM telegraphed its intent to acquire LAS in December, when it announced a partnership with that company. LAS is a specialty provider of multi-cultural name identification, profiling, and cleansing software. It’s been in the business for 20 years and has a host of customers in the government sector.

In the post-9/11 climate, LAS expanded its marketing efforts to the commercial sector, too. In a certain sense, it was a no-brainer acquisition for Big Blue, which can tap LAS’ technology to further flesh out its best-of-breed data integration and information management stack, as well as enhance its technology and services portfolio in a few of LAS’ bread and butter market verticals (government and finance, for example).

In a December interview, LAS CEO Jack Hermansen claimed that his company has the bulk of the multi-cultural name identification and cleansing market to itself. “You’ve got to have a good search algorithm; you’ve got to know how smart the user is; but the third component is the name database [of names and cultural variations of names],” Hermansen said.

LAS markets a number of different products (most of which are highly complementary), and recently released a new name profiling tool, dubbed Name Inspector, that buttresses its other offerings. The company’s other products include NameParser, NameClassifier (which can identify names on the basis of ethnicity, nationality), NameHunter (a name search optimization tool), MetaMatch, and others. Most LAS products evolved over time in response to the specific needs of customers. NameInspector, on the other hand, is a product of the post-9/11 age. It helps identify parsing issues and erroneous names, highlights gender and culture distribution by name, and can pinpoint other name-based anomalies or insights, Hermansen explained.

“Names of people, places, and businesses—there are no dictionaries for them, there’s no way to look up [a name] and say this is wrong. We run into very, very intractable problems [in transliteration from] other writing systems, so if somebody’s looking for a name coming from the Korean culture, or the Cyrillic—for example, Tchaikovsky with a ‘T’ in front of it, because that’s the French transcription of his name—we have to be able to match that.”

One irony, of course, is that some folks—this reporter, for one—have cited LAS and the specialty niche that it occupies as an example of why the data quality market could resist commoditization. (http://www.tdwi.org/News/display.aspx?id=7815) Since that article appeared, however, both Firstlogic and LAS have been acquired. (Business Objects SA acquired the former company last month.) In fact, Hermansen himself argued that the amount of upkeep that goes into maintaining top-flight name matching and name recognition technology might dissuade potential buyers. “We have almost a billion names now [in LAS’ name database] from every country in the world, that we’ve been using as our statistical cauldron, that we’re able to profile and determine name characteristics,” he pointed out.

Big Blue Expands DB2 Warehouse Edition

IBM’s DB2 Warehouse Edition (DWE) got a shot in the arm last week, with a new Eclipse-based design tool, flow-based models for SQL transformations and data mining, and a new Web-based admin console based on Rational Data Architect.

Nevertheless, DWE 9.1 does little to address at least one long-simmering question, concerning Big Blue’s multidimensional OLAP (MOLAP) strategy.

Last summer, IBM severed a long-standing OEM and development relationship with Hyperion Solutions Corp. (http://www.tdwi.org/News/display.aspx?id=7621) Big Blue enhanced and resold Hyperion’s Essbase OLAP engine as its DB2 OLAP Server for almost a decade, and the termination of its relationship with Hyperion (which meant, in effect, the end-of-life of DB2 OLAP Server) left it without a MOLAP option to provide immediate responses and multidimensional data types to model complex calculations.

This severed relationship also placed IBM at a disadvantage vis-à-vis two of its most prominent competitors, analysts said. “By contrast,” wrote Gartner analysts Howard Dresner, Kurt Schlegel, and Frank Buyendijk, “competitors Microsoft and Oracle both offer full-function MOLAP offerings, [which puts] IBM at something of a competitive disadvantage.”

Like its predecessor, the revamped DWE ships with DB2 Alphablox (the fruit of IBM’s acquisition, almost two years ago, of the former Alphablox Corp.), which Big Blue positions as a tool for developing custom applications with embedded analytics. (Alphablox runs on WebSphere Application Server and provides an application framework and development tools for assembling analytic applications.) It also includes the DB2 CubeViews technology that IBM announced almost three years ago. Together, CubeViews and Alphablox give customers a means to develop basic analytical applications, but IBM stops short of positioning the combination as an alternative to Essbase or other OLAP offerings.

With DWE 9.1, for the most part, IBM’s MOLAP value proposition is the same as it has been. Elsewhere, DWE 9.1 ships with a new Eclipse-based Design Studio, along with a new data discovery function that lets customers profile data, sample and view table contents, and visualize correlated statistics to determine where data mining can most fruitfully be applied. The Design Studio features a data flow editor that enables users to visually design data mining flows.