In some organizations, source data quality is often inconsistent at best. Therefore, there is a need to understand how we go about making poor quality data better and dirty and inconsistent data clean. Some see this as the failure of data warehousing to deliver, but the problem is at the source level, often stemming from incompatibilities in reference data used by internal groups in the same organization. There is a lack of commitment to the standardization of data definitions at enterprise level, and software tools alone cannot solve the data quality problem.
The regulatory compliance imperative is forcing many companies to put their houses in order. The financial sector, for example, has been subjected to numerous regulations in recent years, Sarbanes-Oxley, Basel II, and anti-money laundering legislation. The crux of most of these regulations is the need to keep clear and auditable records of customer and trade transactions that are easily accessible.
The onus is on the company to show that the stored data is original, accurate, and that it has not been tampered with. As a result of these compliance requirements, financial institutions have to improve the quality of their data, which is already better than in other sectors.
In a recent data quality survey by Butler Group, the majority of respondents from the financial sector rated their data quality as adequate to good, compared with poor to adequate marked by others. The good is going to get better with the new European Union legislation, referred to as the Markets in Financial Instruments Directive (MiFID). This is going to change things at a much more detailed level as it requires buyers and sellers to find the best prices for executing a trade across all public channels.
MiFID also requires significant changes to post-trade reporting and settlement business rules, aiming to create a common securities marketplace. Thus, it accelerates the need for accurate and standardized reference data to help market participants overcome the effects of non-standardized taxonomies that are used to identify securities in different markets, and processing environments.
The pain of getting such a transparent market off the ground will be somewhat offset with the benefits that improved reference data will bring to BI systems, and to financial institutions as a result. This will give financial institutions new levels of insight into their own operations, as well as the workings of the market, allowing them to gather and deliver business intelligence better than ever before. So ask not what BI can do for your compliance, but what compliance can do for your BI.
Source: OpinionWire by Butler Group (www.butlergroup.com)
