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Companies across many industries face business challenges that affect their master data—the high-value, business-critical information about customers, suppliers, products and accounts—and the ability of IT to deliver on the requirements of a dynamic business. This critical business information is replicated and fragmented across business units, geographic branches and applications. Enterprises now recognize that these symptoms indicate a lack of effective and complete management of master data. Since companies began shifting from a mainframe-based architecture to a more flexible distributed architecture, IT departments have attempted to gain control over this master data using a variety of methods. But few have demonstrated true success due to their reliance on existing, but repurposed, systems and applications. Traditional approaches to master data management The enterprise application Traditional approaches to master data include the use of existing enterprise applications, data warehouses and even middleware. Some organizations approach the master data issue by leveraging dominant and seemingly domain-centric applications, such as a customer relationship management (CRM) application for the customer domain or an enterprise resource planning (ERP) application for the product domain. However, CRM and ERP, among other enterprise applications, have been designed and implemented to automate specific business processes such as customer on-boarding, procure-to-pay and order-to-cash—not to manage data across these processes. The result is that a specific data domain, such as customer or product, may actually reside within multiple processes, and therefore multiple applications.
In this scenario using application masters, it is difficult to determine which iteration of customer, product or account—if any—is complete and correct (see Figure 1). Additional complexity occurs as organizations attempt to maintain the correct copy of the data, and identify and understand all of the systems that can update a particular domain, those that consume portions of the updates, and the frequency rate at which this consumption occurs. It quickly becomes apparent to organizations that have undergone such a project that the process-automating application cannot also manage data across the enterprise.
Alternately, some enterprise initiatives have attempted to repurpose new or existing data warehouses to serve as a master data repository. As data warehouses aggregate enterprise information, the warehouse is often viewed as a starting point for companies attempting to master their data. However, data warehouses have inherent design characteristics to optimize reporting and analysis, and to drive sophisticated insight to the business. This design, while effective for its primary use, cannot scale well within an operational environment—even in the case of dynamic warehousing—when measured against the needs of most businesses today. Based on its fundamental design, the data warehouse also lacks data management capabilities. Essential functionality such as operational business services, collaborative workflows and real-time analytics that are critical to success in these types of master data implementations require large amounts of custom coding. Similarly, data management capabilities—data changes that trigger events and intelligent understanding of unique views required by consuming systems—are also absent from a data warehouse.
Integration middleware Enterprise information integration (EII) or enterprise application integration (EAI) technologies used to federate and synchronize systems and data have also been presented as substitutes for data management products. Although these solutions can tie together disparate pieces of architecture either at the data tier (EII) or at the application tier (EAI), they do not provide either a physical or virtual repository to manage these key data elements. And much like warehouses, they lack data functionality. The management of data processes poses yet another challenge. Choosing to build functionality within this middleware technology can affect performance in its core competency: the integration of applications and data. Without a true master data solution to complement it, the implementation of EII and EAI technology can actually add to the architectural complexity of the business and perpetuate master data problems with point-to-point integration.
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