Improving Financial Services Data Quality- a Financial Company Practice

Technical Paper Review:

IMPROVING FINANCIAL SERVICES DATA QUALITY- A FINANCIAL COMPANY PRACTICE

Q1- The purpose of the effort

Improving Financial Services Data Quality – A Financial Company practice emphasizes on the importance of data quality, which plays a crucial role in the post-crisis recovery of the financial services industry. Organizations make decisions and serve customers based on the data they have at their disposal. The viability of the business decisions is contingent on good data, and a good data is contingent on an effective approach to data quality management.

Following the 2008 global financial crisis, financial corporations became more interested in taking additional measures to increase financial transparency, accuracy and quality of data. Low quality data can expose an organization to monetary damages in a variety of ways. These damages depend on the nature and uses of the data, as well as the levels of responses to the damages. In almost all cases, financial loss is inevitable. Hence financial services companies heavily depend on high quality data for their survival and profitability.

This paper presents the funnel methodology that was invented and adopted by one of the world’s largest banks to identify critical data elements for data quality assessment and improvement. This technique allows them to filter and select appropriate critical data elements out of the numerous data elements from different business areas, thereby improving their quality control processes. Additionally, it discusses their data monitoring activities through six sigma operations, and how these activities help improve their data collection process in other areas as well.

Q2- Approach, Tools and Technique used

Establishment of enterprise level Chief Data Office (CDO) with the objective of continuously improving data quality and consistency in the bank’s functional areas and lines of business was the first step towards adopting Funnel methodology. The following approach was used in selecting the right critical data elements (CDEs)

Step 1: Identification of initial CDEs

Along with engagement model by the combination of functional/business areas & the CDO and by taking crucial inputs from Subject Matter Experts (SMEs), the initial critical data elements were identified from larger volumes of datasets. Here, 35 CDEs were selected, which when measured, provide the best assessment of the quality of data.

Step 2: CDE Rationalization Matrix

In this step, a set of ranking criteria with respect to business process is chosen. Each of criteria is ranked on scale of 1,4,7 and 10, which represents their relative importance to other criterion. Further, each of the 35 CDEs