Introduction

Data management and data cleansing is one of the most challenging and most ignored problems within the financial markets industry. The issue caused by the quality of data is no more pronounced that with the risk modelling and management departments.

In addition to the wide variety of data sources providing data in slightly different formats, there are a number of industry drivers that are adding to the data cleansing conundrum. Those include additional regulations, increasingly complex instruments, new market participants, IT goals related to straight through processing and increased use of data aggregators.

Attitudes around fixing the data cleansing and quality problem are wide ranging. Some are compliant feeling that data management within investment banks will always be fraught with inconsistencies and incomplete information. Some throw responsibility back to the vendors, asking the data providers to work to standardise delivery formats. While others remain perplexed that the financial industry as a whole has so far failed to develop a common identifier scheme for financial data.

Key findings

• Two ways – Most banks split the data cleansing process between external and internal data.
• Most problems – It is the internal data that causes the greatest data cleansing challenge. But data quality among vendors is an industry wide issue.
• Clean enough – Data quality and cleaning is a relative concept within bank, and ‘clean enough’ is the workable option.

Conclusion

With the current economic crisis improving the methods and use of proper risk management techniques is now a headline concern. One of the main ways to improve a risk model is to improve the quality of the data that goes into it.

Investment banks employ teams of staff just to authorise and map a wide variety of data to the appropriate systems and applications. 100 percent clean data is never going to be an achievable goal, not within the modern, global investment bank.

There are many ideas around how to fix the data quality problems. However, the overwhelming attitude to data quality is one of complacency. It is an issue that has to be dealt with, but can never be fixed. Clean and consistent data is a relevant concept, not just between banks, but between models and even instruments. For the time being the financial industry is resigned to inputting data that is merely ‘clean enough’.

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Tagged with: 2009, data, technology, Technology Research Report