In today’s complex business environment, data grows and changes at a rapid pace. Making the best use of data both within the enterprise and externally is essential to corporate success. The challenge is locating, analyzing and categorizing this information to make the most informed business decisions.

In many enterprises, data is splintered, with each department managing its own information, often unaware of how it affects the rest of the organization. Most have turned to technology to help solve the problem, implementing several disparate systems that contain similar information but do not interact with each other. As such, duplicate data may be processed in different systems, creating replicas, stale work and different data structures.

During the past several months, there has been more talk about master data management being a key answer to the siloed data challenge. Enterprises are looking to MDM to gain a better view of business entities and to ensure data quality efforts are consistent.

However, today’s rapidly changing business world – with mergers, acquisitions, layoffs and new market positions – the “management” of MDM is a difficult task. MDM requires extensive understanding of business terminology and the context and importance of data. Furthermore, much of the corporate data MDM needs to capture and organize is unstructured, buried in emails, corporate Intranets and white papers.

A hybrid semantic Web MDM approach can solve these tough issues.

The semantic Web is starting to gain greater recognition in the enterprise world. Most think of semantics in a consumer-facing search environment, not fully recognizing the intelligence semantics can conjure within the enterprise walls.

Semantic Web technologies provide early identification and analysis of customer data, purchasing trends, market deals and competitive information. They uncover unstructured data not only from within an organization’s network, but also from the deepest corners of the Web.

Incorporating morphological, logical, grammatical and natural language analysis, semantic Web technologies translate data with higher precision and recall when searching for information. They also enable enterprises to load disparate data content into semantic data stores for immediate data integration and allow advanced query capabilities. The combination of these capabilities creates a great counterpart to MDM solutions.

Implementation-wise, semantic Web technologies allow IT managers to integrate master data without needing to understand the data model or writing complex SQL statements. Additionally, the semantic Web offers quick and precise data analysis for enterprise users to manage and discover relationships among master data based on semantic modeling and reasoning.

Semantic Web technologies can also enhance MDM solutions by locating and managing duplicate data. For example, if there are records of the same customer but filed with different names (Enterprise, Inc. and Enterprise Corp; Hank Brown and Henry Brown), semantics can assist MDM and identify the different meanings of the same customer and give true insight into the customer’s information.

MDM is of critical importance across a wide range of business sectors, from manufacturing and distribution through the point of sale and beyond, to after-sales and support. At every step along the way, there is a need to identify products, processes, people and rules. Combined with semantic Web technologies, enterprises can not only quickly identify critical business data, but they can extract the meaning of the data.

Semantic Web technologies can give a huge competitive advantage when combined with MDM solutions. If the purpose of MDM is to give an accurate, intelligent snapshot of all the moving parts of an organization, then the semantic Web can improve upon this with better data identification, classification, mapping and evaluation. Though MDM and semantic Web technologies are still relatively new to enterprises, the possibilities of the combined solutions are great.

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