Mergers & Acquisitions will only increase the complexity of system environments. The convergence of multiple ERP platforms, for example, supports the likelihood of a potential mish-mash of varying system protocols, standards, multiple sets of training requirements and divergent user experiences. But M&A activity also creates a fertile breeding ground for highly disparate data models.

Primary ERP systems of the last ten years such as SAP, Oracle, Lawson, JD Edwards and PeopleSoft, might offer similar applications, but each one guarantees a proprietary approach to the physical and structural creation of data. SAP, for example, sees its internal Material Master as a key, competitive differentiator. But when these disparate systems with their multiple data models attempt to coexist within the same enterprise landscape, odds are there is little to no organic basis for standardized data creation.

Hence, a single-view of customer is impossible. Below is a simple example of how redundancy occurs because of variances in data model attributes and different standards or requirements (or the lack thereof), for data entry:


(ERP) First_Name Last_Name Middle Initial Addres_1 Addres_2 City State Zip_code
System1 Ron Smith M. 123 Main St Apt 2 Dumont NJ 07628
System2 Ronald Smith (Null in data model) 123 Main Street, Apt 2 Dumont NJ 07628-2392
System3 R. Smithe (Null in data model) 123 Main St. Fl. 2 Bergenfield NJ 07628-2392
System4 Ron Smith 123 Main Dumont NJ 07628-2392


For all automated intents and purposes (and common sense notwithstanding), the above example represents 4 different customers. It also represents a very shaky foundation for good data management, ultimately resulting in poor analytics and degraded operational processes.

A Master Data Management Solution

At Software AG, we’ve seen a tendency for acquiring companies to reduce and consolidate their ERPs systems, maintaining the dominant ERP solution as the system of record. But while this approach sets-in-motion a very necessary phase1, it does not guarantee a single-view of master data or a single-version of good and consistent data. (Oddly enough, even within only one, vendor ERP instance, data can still be duplicated and inaccuracies propagated).

webMethods OneData is currently used by large-to-very large, digital enterprises to support complex M&A scenarios requiring a flexible MDM solution for consolidation of master data across multiple platforms, including ERP. OneData’s versatility and multi-vector hub-deployment architecture enables numerous ways of implementing connectivity between the MDM repository and the evolving enterprise landscape.

In addition to integrating mission critical sourcing and subscribing systems, OneData also supports a company’s M&A requirement for canonical modeling. Canonical models, in a manner of speaking, represent another version-of-truth, providing a way of reconciling different domain modeling formats into one centralized data model. Since canonical models are oft-times developed through 3rd party data modeling tools (e.g. CA ERwin®) they can be easily imported, directly into OneData’s backend - automatically creating MDM’s UI.

Admittedly, considering all the business activities swirling around mergers & acquisitions, data management may not be foremost in an organization’s planning. Which, is not to say it shouldn’t be.



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