zAgile CEO: Why you should consider model-driven integration

We're only beginning to address the problem of cloud integration. CEO Sanjiva Nath describes a unique solution derived from the semantic Web

As more and more businesses turn to cloud applications and platforms, we risk reinventing the siloed organization, where different business units go around IT to adopt various disconnected solutions. Duplicate yet slightly different records about the same products and customers become scattered in isolated data stores, often serving a single app. For the organization as a whole, the chaotic result can be a step backward from carefully integrated legacy systems, which may have been rigid, but at least they talked to each other.

Enterprise applications integration, the traditional solution for breaking down silos, is not necessarily suited for cloud integration -- giving rise to a number of fresh solutions for the cloud era. In this week's New Tech Forum, we've invited Sanjiva Nath, CEO of zAgile, to explain the metamodel-driven technology behind his Wikidsmart platform, which offers data integration and can also tie application data with process context and team activities. Currently, the most common applications for Wikidsmart are to integrate CRM data sources and to manage large software development projects across multiple, far-flung teams.

-- Paul Venezia

Model-driven integration
The problem of integrating enterprise applications is not new. But the demand for integration and the complexity of implementing it keep increasing, thanks to rising adoption of SaaS and market forces that push teams to improve their collaboration and delivery capabilities.

Traditional enterprise application integrations have proven time-consuming and expensive, typically involving months of effort and significant cost. A big contributor to this overhead is the fact that integration schemes typically lack any formal representation of application data -- that is, metadata. As a result, the interpretation of the application data and its mapping to any integration scheme becomes a manual, tedious, and nonrepeatable process.

zAgile's Wikidsmart -- a platform for integrating applications, teams, processes, and collaboration within the enterprise -- addresses this problem in a unique fashion. Drawing on technology developed for the semantic Web, Wikidsmart supports the formal representation of systems via metamodels or ontologies that richly describe application objects, their attributes, and the nature of their relationships with each other. These metamodels may also be adapted to represent team collaborations and methodologies.

Metamodels allow application data to be integrated with process context and team activities, which traditional integration technologies have failed to achieve. To realize the benefits of metamodels, however, you must first understand the fundamentals of metadata.

What metadata really means
Metadata refers to data about data. It lays the groundwork for capturing structured information about a data element or resource that is machine-interpretable -- and enables use of that element in a variety of contexts within applications and components, as well as across systems.

Metadata exists within applications for internal consumption anywhere an application needs to manipulate elements, and occasionally, it is defined formally to facilitate sharing of the information across applications. It does not imply the representation of any specific aspect of an element. Instead, it defines the possible parameters for that element, including structural information (such as the address field in a database record), descriptive information (such as color or type), or administrative information (such as access rules or persistence).

The idea is that metadata does not imply the granularity or composition of a data element. A data element described by metadata could be a field, column, row, table, or database.

Metadata capture also does not necessarily follow any predefined organization or categorization, since application developers make choices based upon how one or many applications expect to consume the metadata, which may represent multiple facets of an element.

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