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Reliance Measurement Technique in Master Data Management Repositories & MDM Reps on Clouded Federated Databases.

IP.com Disclosure Number: IPCOM000236236D
Publication Date: 2014-Apr-14
Document File: 5 page(s) / 46K

Publishing Venue

The IP.com Prior Art Database

Abstract

Reliance Measurement Technique in Master Data Management Repositories & MDM Reps on Clouded Federated Databases.

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Reliance Measurement Technique in Master Data Management Repositories & MDM Reps on Clouded Federated Databases .

Master data Management offers organizations the opportunity to use a Master data Management platform to extract insight from an immense variety of data. For example, a company can gain insight into customer behavior and product sentiment by performing analytics on large volumes of data from a variety of sources such as website logs and social media websites. Similarly, the same company could leverage the same platform to gain insight into user activities and potential security threats by performing analytics on large sets of audit data from numerous sources such as database logs , operating system logs, application server logs, and Customer Relationship Management (CRM)/Enterprise Resource Planning (ERP) application logs.

Master data Management is a promising technology for extracting knowledge and insight from large volumes and variety of data; however, this insight is only as good as the data from which it is extracted. In other words, the weakest point in the insight extraction process remains the relevance and trustworthiness of the input data , considering its uncertain and often unverified origins. This makes the old computer science concept of "garbage in, garbage out" a primary concern for Master data Management analytics. This issue has not been addressed by the Master data Management research and development community. This comes as no surprise as Master data Management is still an emerging technology and most of the work that has been done so far has focused primarily on providing the tools and infrastructure for effective large -scale storage and analytics processing.

A solution is needed that allows a customer to gain insight into the trustworthiness of all the data lying in the company's Master data Management repository

The disclosed solution proposes a technique for ranking and classifying the relevance and trustworthiness of the information stored in a Master Data Management repository , effectively producing a data heat map that ultimately boosts the customers ' confidence level in the analytics provider. The contribution herein is a technique to extend the data ingest process to automatically associate a tag (or label) with every piece of information as it is ingested into the Master Data Management repository, and then leverage that tag in producing a data heat map representing the trustworthiness of all the information stored in that repository. At a minimum, this tag includes the origin of the information , the date, and the time it was ingested into the repository. The origin is a multi-attribute data structure that consists of the identity of that origin (e.g., User A), the type of that origin (e.g., a blog), and a pointer to the actual origin (e.g., www.useracompany.com/blogs).

To produce the data heat map, the proposed technique presents an interface in which the customer enters some configur...