Data is becoming a bigger focus for asset managers as regulators seek clarity and assurances around risk management.
Just last month the SEC was floating a proposal to get more data, and this against a background of regulators assessing whether asset managers and funds are systemically important financial institutions (SIFIs).
If firms want their risk management to be truly effective, then a significant part of the effort needs to focus on managing data. Historically this simply hasn’t been the case. Also, good decisions can only be made on good data.
Dirty data increases risks and drives ill informed decisions. So high quality risk data is central to better decision making and more attention needs to be paid to it. A huge part of data management is data governance. That is the need for a set of processes
to ensure that important data assets are properly managed throughout the firm.
Data also sits in many places. In order to get risk management working firms need to be able to collect and integrate data from multiple internal and external sources. This is big issue for many firms as integrating data across the business has historically
Likewise with reconciling different sets of data. There also needs to be master copies of the risk data. Not a single master, but a golden copy for different business units.
It is also important that any data management solution can handle all types of data. Risk data comes in many forms and it all needs to be normalised. The completeness of that data set also needs to be verified.
If asset managers want better risk management, they need to be able to trust their data. The quality of the data and getting the right data are basic fundamentals for robust risk management. Data consistency and completeness is key to the implementation
of an effective programme.
As regulatory and firm’s own requirements evolve, data quality issues will become increasingly apparent. Many surveys report improving risk data quality as a high priority and effective data management is a central part of that.