To be effective, EDQM as a process must also meet a number of technical challenges. The process must work across multiple platforms and information architectures, must be adaptable and capture knowledge from an organization, and not scare away users by being difficult. Today's enterprises not only contain a variety of computing technologies, from PCs to workstations to servers and mainframes, but also a number of database management systems, data architectures and applications with which EDQM must interface. Hardware flexibility is a critical component in choosing a data-reengineering tool. Tools must be portable from one application to another, scalable for large as well as small applications, and have the ability to be re-used throughout an organization, building on prior knowledge and operational rules.
Functional flexibility is another critical component in choosing a data-reengineering tool.
Ease of Use is critical in the successful implementation of EDQM within an organization. A successful data-cleansing application must be easy to implement, integrate with existing applications and business processes, and provide for monitoring and tuning of the system to ensure that knowledge and rules are easily maintained. Flexibility in use is another critical component in choosing a data-reengineering tool. A data-cleansing tool with these three technical capabilities will facilitate deployment and consistent utilization of EDQM techniques throughout an organization. Initially, data reengineering consisted of manually written code interposed between the data extraction and the data loading phases of the Data Warehousing implementation.