A Granular Computing Methodology to Improve Record Quality for Master Data Management

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Master Data Management (MDM) is a corporate process that ensures the accuracy, uniformity, proper stewardship, privacy, semantic consistency, and accountability of a company's master data assets. Master data is the consistent set of business identifiers and attributes that describe the core entities of a company. A key aspect of a company's MDM solution is the creation of a "golden record" for every client of interest. These golden records are linked, synchronized, and aggregated using sets of heterogeneous information across distributed data repositories. As storage costs decrease and big data technologies mature, companies can store more complex, voluminous, and varied (schematized, semi-structured, unstructured, social media, and so on) identifiers and attributes. The automated creation of golden records presents seven significant challenges: (i) aggregating data acquired at varying levels of information specificity; (ii) compensating for incomplete or missing attribute values; (iii) attenuating the effects of imprecise or inaccurate data; (iv) minimizing the false positive rate, that is, avoid merging records belonging to different clients; (v) ensuring that records belonging to the same client are, in fact, merged, that is, minimizing the false negative rate; (vi) reducing the number of manual, end user, reconciliations; (vii) and reducing the number of ad hoc rules to deal with anomalies from the automated process. In close collaboration with our industrial partner, InfoMagnetics Technologies Corporation, we propose to address the above challenges via the design and development of a Granular Computing Methodology for MDM in order to improve client record quality. We will employ novel aggregation techniques on data, with varying levels of information specificity and across disparate data repositories, in order to improve the overall quality of the assembled golden records.