Improving financial services data quality – a financial company practice

• Published in: International journal of lean six sigma Vol. 6; no. 2; pp. 98 - 110
• Main Authors: Shi, Chuan, Jugulum, Rajesh, Joyce, Harold Ian, Singh, Jagmet, Granese, Bob, Ramachandran, Raji, Gray, Donald, Heien, Christopher H, Talburt, John R
• Format: Journal Article
• Language: English
• Published: Bingley Emerald Group Publishing Limited 01.06.2015
• Subjects: Decision making; Economic crisis; Financial services; International finance; Management decisions; Management science & operations; Privacy; Quality; Quality improvement; Quality management/systems; Six Sigma; Six Sigma; Statistical analysis; Voice of customer; Critical data elements; Signal-to-noise ratios; Financial industry
• ISSN: 2040-4166; 2040-4174
• DOI: 10.1108/IJLSS-11-2013-0056

Purpose – This paper aims to propose a funnel methodology that selects business data elements for data quality improvement practices at a financial company. Data quality is crucial in post-crisis recovery of the financial services industry. This allows the bank to monitor its critical data assets and improve its business operation by Six Sigma engagement that benefits from the good quality of data. Design/methodology/approach – A funnel methodology is invented. It utilizes a rationalization matrix and statistical methods to identify critical data elements (CDEs) for data quality efforts from numerous candidates across business functions. The “Voice of the Customer” is achieved by including subject matter experts, whose knowledge and experience contribute to the entire process. Findings – The methodology eliminates redundancy and reduces the number of data elements to be monitored, so that attention becomes focused on the right elements. In addition, the methodology ensures that the conduct of the data quality assessment is framed within a context of the functional area’s business objectives. Originality/value – Measuring and improving data quality form a solid foundation of every Six Sigma engagement. When presented with large data elements, determining what to measure can be an arduous task. Having a proven systematic and valid process to reduce the CDE candidate pool becomes an operational necessity of paramount importance, and this justifies the value of the proposed methodology. Its implementation is described by a Basel II case study. The methodology is not restricted to financial services industry, and can be used readily in any other industry that requires data quality improvement.