Related Topics:
			Technology Research: Business Intelligence, Information Management, Master Data Management Imperative Research: Compliance Management, Performance Improvement Vendor Research: Hyperion, IBM, Initiate Systems, Kalido, Oracle, Purisma, SAP, Siperian, Stratature
			Printer friendly version Email this article Send feedback to editor

Summary
Customers are the life blood of companies, yet recent research by Ventana Research shows that for the majority of companies, their customer data doesn’t help them serve customers effectively. That data is scattered across different systems that are often managed in different business units and even in geographically dispersed locations. With so many sources, inconsistency in data is bound to crop up, and the data’s quality can vary widely. Without a reliable or “golden” source of data, groups will have different views about customers and may end up making conflicting decisions. DataFlux is one vendor that provides a system to improve the quality of data and to ensure it is managed consistently across the enterprise.

Assessment
One of today’s biggest business and IT challenges is for companies to know how many active, discrete customers they have and to manage data about them effectively. Various business and technology developments have produced situations in which companies have customer data distributed all over the enterprise and managed by an array of different systems. Years of mergers and acquisitions have worsened the problem, and many companies struggle to integrate disparate systems into a rationalized architecture. For example, in a recent study, Ventana Research discovered that 8 percent of companies have more than 50 instances of their enterprise resource planning (ERP) system and that only 14 percent totally trust the quality of their master data.

DataFlux is one of a small group of companies that provide solutions to this issue. The process begins with addressing the quality of existing data, which DataFlux breaks down into five steps: profiling, quality assessment, enforcement, documenting and monitoring. Through its prebuilt Accelerator for Customer Data, companies can extract customer data from a variety of sources and begin to profile it. A Web-based tool generates reports showing the quality of customer data and uses weighting criteria to “score” its quality. At the same time, the system identifies business rules to address common data issues such as duplicate entries or nonstandard items. Prebuilt workflows and Web services handle standard customer data quality improvement tasks such as standardizing addresses, matching and clustering records and verifying phone numbers. The next step is to match, link and integrate data from the different data sources to create a consolidated view of a customer’s information. This can be enriched by linking with internal and external data sources that add demographic data, postal address enhancement, personal preferences and other information. Once this first pass is completed, the monitoring tools keep watch as the data is modified over time, providing a picture of how the quality is being maintained, issuing alerts if the changes take the data outside predefined limits and reducing costs associated with divergent data quality.

Once the quality of the customer data has been established, the system supports the creation of a master customer reference database. Through an integration server that is driven by maintainable business rules, the cleaned data can be replicated back to all the data sources. Using DataFlux’s dfPower Studio, companies can build these business rules once and then through batch processes or a service-oriented architecture (SOA) replicate them across all applications and systems, thereby ensuring the ongoing consistency and quality of customer data. The DataFlux Standard Integration Server can call discrete DataFlux data quality algorithms from native programming interfaces including C, COM, Java and Perl; it can be deployed on Windows, UNIX and Linux platforms with client/server support. The outcome is a single, high-quality source of customer data, which can be used to produce a complete view of the customer.

Market Impact
The market for master data management (MDM) is still amorphous, with MDM meaning different things to different people. MDM may be implementing a data warehouse; using a type of enterprise application such as ERP, customer relationship management (CRM) or supply chain management (SCM) or deploying extraction, transformation and loading (ETL) tools; or establishing a full customer information hub. In any case, at the core is the issue of data quality. DataFlux is established in addressing all aspects of data quality. Its current competition includes Business Objects (Firstlogic), IBM and Trillium, but the group is being joined by relative newcomers such as Acuate and Data Discoveries. On the customer data integration (CDI) side, the strongest competition comes from the enterprise application vendors and Siperian. With all this competition, DataFlux will have to work hard to make sales, but we expect the overall market to grow strongly, and the backing of its parent company – SAS – should help DataFlux maintain a leading position.

Recommendation
Our research found many reasons why companies are being driven to sort out their customer data issues, not the least of which are the high costs of maintaining enormous amounts of data, difficulties in providing accurate analysis of business situations and the need to comply with regulations such as the Sarbanes-Oxley Act. The same research showed that the most companies (42 percent of those surveyed) expect the main business benefit of data quality initiatives to be more accurate reporting and business analysis. As companies go in search of a comprehensive view of customers and a single source of data about them, we recommend they look at what DataFlux can offer. As they do this, they should also pay more attention to putting in place a data governance process to ensure better control of data going forward.