Part 2 of a four-part series.
Click here for Part 1.
As noted in my previous column ("Mastering Customer Records," December 2007), today's CRM applications depend on services-oriented architecture (SOA) to ensure that customer data is truly ready for business.
But CRM without data-quality-infused SOA is inconceivable -- or just far too risky. High-quality data supports both low-level operations and high-level strategic planning. No organization can tolerate critical decisions based on garbage data.
To begin with, low-quality data undermines the trustworthiness of key performance indicators (KPIs) central not only to CRM, but to corporate performance management (CPM), business intelligence (BI), master data management (MDM), data integration, governance/risk/compliance, and other business applications. Without automated cleansing of data records prior to consolidation, users can't be sure they're really getting a "single version of the truth."
Data quality (DQ) capabilities -- to discover and profile source data; validate, de-duplicate, match, merge, and cleanse that data; and enhance, enrich, and augment it with additional data -- are key to avoiding the "garbage in, garbage out" trap, but not all SOA application and middleware vendors offer strong DQ tools. (Likewise, many CRM and other application vendors have strong DQ capabilities, but laggards -- such as Actuate, Infor, Information Builders, MicroStrategy, Sybase, and Teradata -- must partner with DQ vendors in order to offer such capabilities.)
In fact, much of the recent industry consolidation was driven by SOA vendors' need to incorporate strong DQ -- a trend set off in the early 2000s by SAS Institute's acquisition of DQ specialist DataFlux. Most of the leading pure-play DQ vendors have since been acquired by larger SOA, BI, or CPM vendors: Ascential by IBM, for example. The consolidation trend really picked up steam late in 2007, first with SAP's announced acquisition of Business Objects and then with IBM's move for Cognos.
SAP had offered some data-profiling and -cleansing features, but many of its customers preferred standalone DQ offerings from Business Objects and other best-of-breed vendors. After the acquisition, SAP will be able to sell Business Objects' SOA-enabled DQ offerings into its huge worldwide customer base.
In the case of IBM/Cognos, it's the acquired party that sorely needed strong DQ: Cognos had historically been at a competitive disadvantage against rivals -- such as Business Objects, Oracle, SAS, and SAP -- that had built-in DQ features. As part of IBM, Cognos will leverage the best-of-breed DQ tools that IBM acquired from Ascential, along with IBM's own DQ professional services. But the IBM deal complicates a two-month-old partnership between Cognos and Informatica, in which Cognos was to integrate into its deployments the Informatica DQ solutions.
Partnering with Informatica was never going to address Cognos' chief competitive disadvantage: relying on a strategic partner, rather than proprietary products, to address critical customer requirements involving DQ. Furthermore, the Informatica partnership overlapped with Cognos' existing DQ relationship with IBM, creating the risk of consulting, channel, and customer confusion regarding which solutions -- Informatica's or IBM's -- Cognos would recommend in which scenarios. That potential for confusion has grown even more acute with IBM's pending takeover of Cognos.
The remaining pure-play DQ vendors will continue to be attractive targets for SOA suite providers -- such as Microsoft, BEA Software, Progress Software, Software AG, and Tibco -- looking to improve their DQ capabilities. (Most of those suites provide only basic data cleansing.) The newly independent Teradata may even snatch up a pure-play DQ vendor to flesh out its MDM, data warehousing, and CPM offerings.
The importance of DQ in the SOA arena will continue to grow. The more data sloshing around the SOA universe, the more critically we'll all rely on middleware to ensure that we're not running our businesses on inaccurate, inconsistent, or out-of-date information.
Click here to read Part 3 of this series, and here for Part 4.
James Kobielus (jkobielus.blogspot.com) is a senior analyst at Forrester Research. You can email him at firstname.lastname@example.org.
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