Is It Time to Revamp Your Data Translation Strategy?

After a few brutal years in a stagnant economy, things are starting to look up for many supply chain companies. Consumers and companies are beginning to buy more, factories are ramping up and the business outlook is improving in certain segments of the market. This uptick is prompting companies to seriously evaluate whether their data translation strategy is up for the growth ahead.

If you're doing data translation on-premise using point-to-point data mapping, you know that it works perfectly well when you have a small number of trading partners. It only starts to break down and becomes hard to maintain as you add more trading partners, document types and back-end applications-all byproducts of growth. In other words, point-to-point mapping simply is not scalable in a practical or cost-effective way.

If this is striking a chord with you, it may be time to consider a data translation strategy revamp: replacing your on-premise point-to-point mapping strategy with a canonical-based mapping strategy-and, perhaps, even going a step further to adopt a cloud integration strategy that brings with it several key, additional benefits.

The Problem with Point-to-Point Mapping

As the number of systems to be integrated grows, the number of messages and interfaces multiplies in a linear fashion. But the number of potential interface points-individual data connections and transformations-grows exponentially.

Even if you have no problem with the cost of the initial integration project, the maintenance costs of traditional, manually driven point-to-point integration can become overwhelming. Every time any one application changes or a trading partner changes its specification interface, all of its external connections must be reviewed for potential impact. An upgrade cycle for a large ERP system may spawn dozens, hundreds, or even thousands of integration projects ...