- Using a data profiling tool to analyze and measure data quality of both source or target environment data prior to developing the integration logic.
- Establishing data acceptance criteria for an individual application system. Perfect data isn't practical; the focus should be "good enough." And the business stakeholders involved in an integration project should be able to identify usage scenarios that reflect what "good enough" looks like.
- Including a data quality/accuracy review step as part of the design review step in an overall development project.
- Establishing standard load and export interfaces for systems that provide data and take it on board on a regular basis. Most integration development follows a one-off approach even when it's common for some systems to on-board data from new systems on a regular basis.
Dig Deeper on Enterprise application integration software
Related Q&A from Evan Levy
Learn the difference between change data capture (CDC) and data federation. Find out how companies can use both data integration technologies to ... Continue Reading
Learn about SOA risks and challenges common when transitioning to a service-oriented architecture (SOA). Take steps to reduce risks with these SOA ... Continue Reading