Historical data is becoming more crucial to managing risk, but to make it useful, data updates must be reconciled with the moments actual changes in data occurred, says Xenomorph’s Brian Sentance.
There has been much talk recently about integrated data management, as the post-crisis focus on risk management demands a more integrated approach to how the data needed by the business can be managed and accessed within one consistent data framework. Much of the debate has been around how different asset classes are integrated within one system, or how different types of data—such as market and reference data—should be managed together.
However, there has been little discussion on how historical components can be integrated into the data management infrastructure. This will have to change if the needs of regulators, clients, auditors and the business are to be met in the future.
Why is history and historical data becoming more important to data management? There are many reasons. First, data management for risk needs historical data in a way that simply was not necessary for the reference data origins of the industry over a decade ago.
Another reason would be the increasing recognition that market data and reference data need to be more integrated, and that having one without the other limits the extent of the data validation that can be performed. For example, how can terms and conditions data for a bond be fully validated if the security is not valued by a model and prices not compared to the market?
As another example, how many data management staff were overloaded by the “false positives” of price movement exceptions during the highly volatile markets of the financial crisis? I would suggest many organizations would have saved hours of manual effort if the price validation thresholds used could have automatically adjusted to follow levels of market volatility derived from historical price data.
Regulators and other organizations in the financial markets now want to know more of the detail behind the headline risk and valuation reports. The post-crisis need for an increase in the granularity of data should be taken as a given. This is progressing to an extent where external and internal oversight bodies not only want to know what your data is now, but want the ability to see what the data was at the time of market or institutional stress. Put another way, can you easily reproduce all the data used to generate a given report at a specific point in time? Can you also describe how and why this data differs from the data you have today?
“But I already have an audit trail on all my data,” I hear you say. Yes, that is a necessary condition on being able to “rewind the tape” to where you were at a given time, but is that sufficient? An audit trail could be considered as a sparse form of historical “time series” storage for data, but as we all are aware, there are not many pieces of “static” data that do not change over time (corporate events being the main cause behind these kinds of changes). The main issue with audit trail use here is that it can only represent the times when the data value was updated in the database, which is not necessarily the same time as when the data value was valid in the real world.
So for example, for the sovereign, that forces a change in the maturity dates of its issued bonds. You can only capture when your data management team implemented the change in the database, not necessarily when the change was actually made in the market. Hopefully, the two times may turn out to be the same if your data management team is efficient and your data suppliers are accurate and timely. But don’t count on it, and don’t be too surprised if a regulator, client or auditor is displeased with your explanation of what the data represents and why it was changed when it was. We are heading into times where not knowing the data detail beneath the headline numbers is no longer acceptable, and historic storage of any kind of data—not just market data—will necessarily become much more prevalent.
Source: Xenomorph, 13.07.2011
Filed under: Corporate Action, Data Management, Data Vendor, Market Data, Reference Data, Risk Management, Standards, Compliance, Consolidation, Data Management, Data Strategy, Data Vendors, Market Data, Reference Data, Regulation, Risk Management