Tag Archives: Integration Pattern Repository

e-Business Integration Mapping Approaches and When to Use Them

When implementing an e-business integration solution, whether it is cloud, data, application-to-application (A2A) or business-to-business (B2B), there are always common activities that have to be performed.  One of those activities is creating the data transformation maps.   This is usually the most complicated and time consuming part of the integration process.  In an attempt to reduce time and effort required to create these maps, different approaches, both manual and automated, have been tried and tested.  Let’s take a look at some of these methods and which circumstances to use them:

Manual Mapping

One available approach is the process of identifying the source and target data formats, then manually mapping the rules for the transformation.  Unlike the old conventional method of coding, current integration middleware solutions usually employ some form of visual drag-and-drop functionality.  This is the most common, but also the most time-consuming approach.  Manual mapping can be used for building new transformations, especially when no similar map is available to use as a starting point. This approach can also be used when implementing a brand new integration middleware solution, or when there aren’t any other available options.  Examples of this are when new applications and trading partners are integrated into the solution or, when brand new document formats from external integrations are introduced.   Continue reading

Is Data Mapping a Solved Problem?

Many IT professionals believe that data transformation and mapping are “solved problems”.  After all, mapping tools have been around for over 20 years, and thousands of IT organizations use them in integration projects every day.  “If it ain’t broke, don’t fix it”, right?

What belies that attitude are the missed integration project deadlines, runtime exceptions, customer chargebacks, vendor scorecard deductions and other business problems that can be traced to data transformation mapping practices.  Mapping is also the single most costly integration activity, accounting for up to 75% of some integration project costs.  Yet few project teams focus attention on ways to improve mapping efficiency and accuracy. Continue reading