Climbing the MDM Ladder

Customer data is not just a component of good service and a successful business--it's the foundation. Any enterprise that hopes to excel will need to adopt master data management (MDM) and customer data integration (CDI) solutions, and will need to progress up the data management ladder, according to Jill Dyche, a partner and cofounder of Baseline Consulting. The model, as outlined at the CDI Executive Summit in New York in June 2006, has four stages. At stage 1--which is called unaware--an organization has few defined rules or processes in place regarding data management. Duplicate data may exist in multiple databases, each serving different departments. The company has little visibility into its data management costs or performance, and IT often takes the lead at this stage. The result is an enterprisewide failure to understand why problems exist or what impact they may have. The associated business risks are high (lost customers and/or improper business procedures) while the rewards are low. Approximately 35 percent of companies are at stage 1. Stage 2 is when organizations begin to understand the importance of data management, but deal with data quality issues only as they occur. At best, the business hopes to react to problems to mitigate the severity of outcomes. Tactical data management solutions, such as data profiling or data quality solutions, have been implemented, but the organization still lacks an integrated data management solution. Risks are still high due to this lack of integration, while rewards are limited and mostly anecdotal. Forty-five percent of companies fall into the reactive stage. Only 15 percent of companies are at stage 3. Such companies have the ability to avoid risk and reduce uncertainty when it comes to customer data. Data management starts to play a critical role within the business and it receives more tangible value from consistent, accurate, and reliable data. Companies begin to look beyond the horizon to understand the impact of data problems on mission-critical information. Data management initiatives begin to resemble CRM-type implementations, with executive- and management-level support and a data governance team guiding receiving end-user feedback. EAI (enterprise application integration), EII (enterprise information integration), and CDI tools are used to abstract and cleanse data automatically. "Data quality becomes an everyday part of life," says Tony Fisher, president and CEO of DataFlux. Fewer than 15 percent of organizations currently operate at stage 3. At stage 4, companies reach the nirvana of data management. In the predictive stage, data quality is ingrained throughout the company. Processes are automated and all business applications are connected to a central MDM/CDI repository that formats, cleanses, and redistributes data back to all the applications in real time. Fisher says making the jump from stage 2 to stage 3 is the biggest hurdle that companies will face. "This is the step where departments that have been happy operating independently of one another have to come together. It's an enterprisewide shift in approach as to how customer data is handled." Five percent of companies currently reside at stage 4, according to Dyche. "MDM and CDI are still in the early development stages, so as the technology improves that will be one of the factors, but only one of them. The real drive will come from the customers as they realize its value."

Related Articles

MDM in the Real World

Putting master data management into practice.