How to ensure provision of accurate data to enhance decision-making

Naomi Clarke

Journal of Securities Operations & Custody, 2019, vol. 11, issue 2, 112-127

Abstract: Data is increasingly under the spotlight as regulators and clients demand more from financial institutions. In order to address this issue, this paper puts forward a framework for improving the accuracy and overall quality of data that is used to inform decision-making. Drivers for adopting a more systematic approach to improving data quality are explained in terms of the current regulatory and business context. The paper describes ‘fitness for use’ in terms of various aspects of quality that will be important to the various consumers in an organisation. The importance of data governance as an appropriate control mechanism is emphasised and a framework of necessary components is put forward. An overarching approach to corporate internal controls is vital to provide the strategic context and management backing for a data governance initiative and the various components are explained in detail. This then sets the scene for the specific data components of the framework. The paper describes how to catalogue data inventory and put meaning and relevance at the heart of explaining the data requirements of the business. Analysis of how the data is used in frontto- back processes provides the link to data lineage and describes accountability and ownership for data quality, so implementing governance within the day-to-day business. Ongoing monitoring of data quality and the types of metrics that should be used is discussed and an example of their use in a buy-side value chain adds real-world context. Finally, the meaning of data accuracy in relation to future trends in the world of ‘Big Data’ and analytics leads to the conclusion that organisations should be aiming for ‘good enough’ for a given situation. This, and future business success and profitability, however, is predicated on a fundamental understanding of the nature of the data that the business requires.

Keywords: data quality; data governance; data management; internal controls; Big Data; decision-making (search for similar items in EconPapers)
JEL-codes: E5 G2 K22 (search for similar items in EconPapers)
Date: 2019
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