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

主数据管理(MDM)是确保公司主数据资产的准确性、统一性、适当管理、隐私、语义一致性和责任的公司流程。主数据是描述公司核心实体的一组一致的业务标识和属性。公司MDM解决方案的一个关键方面是为每个感兴趣的客户创建“黄金记录”。这些黄金记录使用跨分布式数据存储库的异类信息集进行链接、同步和聚合。随着存储成本的降低和大数据技术的成熟，公司可以存储更复杂、更海量和更多样(图案化、半结构化、非结构化、社交媒体等)的标识符和属性。自动创建黄金记录带来了七个重大挑战：(I)聚合在不同信息专用性水平上获得的数据；(Ii)补偿不完整或遗漏的属性值；(Iii)减弱不精确或不准确数据的影响；(Iv)最大限度地减少假阳性率，即避免合并属于不同客户的记录；(V)确保属于同一客户的记录实际上被合并，即最大限度地减少假阴性率；(Vi)减少人工、最终用户、对账的数量；(Vii)减少处理自动化过程中异常情况的特别规则的数量。我们与我们的工业合作伙伴InfoMagnetics Technologies Corporation密切合作，建议通过设计和开发用于MDM的细粒度计算方法来解决上述挑战，以提高客户记录质量。我们将对数据采用新的聚合技术，具有不同级别的信息专用性，并跨不同的数据存储库，以提高汇编的黄金记录的整体质量。

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.