The Landscape of Enterprise and Corporate Data Today

Data integration and the efficient use of the available information in a business context are major challenges. A typical enterprise has critical applications from different vendors running on various technologies, platforms and communicating via different routes and protocols within and outside an Enterprise. These applications create disparate data sources, data silos and introduce enormous costs. To manage this complexity, an enterprise it eco-system is viewed as a set of interconnected (or partially connected) applications managing different processes of the enterprise, where separation of the applications often means replication of the same data in different forms. Each process manipulates different kinds of data and produces new data in a structured or unstructured fashion as it is depicted in Fig. 2.

Fig. 2. Classical Enterprise Information System

Existing technological approaches such as Enterprise Application Integration (eai) create middleware between all these diverse information sources making use of several architectural models with examples being Event Driven Architecture (eda) or Service Oriented Architecture (soa) which are usually implemented with web services and soap. Common approaches in the enterprises typically include Data Warehousing and Master Data Management.

Simple xml messages and other b2b standards ensure the “flow” of information across internal systems in an easy-to-use and efficient manner. In some cases this is enough but, for example, with a portfolio of over 30,000 offered products and services it is not possible to describe complex components with a handful of simple xml elements. There is a clear need for providing clear definitions or semantics to the data to facilitate integration at the data layer.

However, integration in the data layer is far from being a straightforward task and the Linked Data paradigm provides a solution to some of the common problems in data integration. The two technological approaches, i.e. eai and LOD, are not contradictory but rather complementary. soa architecture deployed in an eai approach works with service oriented whereas LOD works with hyperlinked resources (data, data sets, documents, ... ). Note that soa architecture needs many custom services where LOD uses only a few services (sparql, rest) and hyperlinking with the referenced resources. Both approaches have complementary standardization efforts (on metadata vs. services) which makes them better suited for different tasks. eai-soa approach is well suited for well-defined tasks on well-defined service data whereas LOD is more targeted for innovative tasks involving semantics (integrations, mappings, reporting, etc.).

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