- 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
With Software-as-a-Service (SaaS) applications growing in popularity, learn about the SaaS data integration challenges companies should be aware of ... Continue Reading
Find out if real-time data integration applications has more data quality issues than other approaches. Also, see if real-time or near-real-time ... Continue Reading
Learn the difference between change data capture (CDC) and data federation. Find out how companies can use both data integration technologies to ... Continue Reading
Have a question for an expert?
Please add a title for your question
Get answers from a TechTarget expert on whatever's puzzling you.