When working with corporate actions or fixed-income securities such as bonds that have variable coupon rates, the concept of bitemporality has become key for managing the information about these actions and securities. In financial trading operations, a bitemporal data management capability means being able to assign and manage multiple time stamps for the same item at different points in time.
With a bitemporal capability, data managers can more effectively identify and correct errors in their data, based on a history of that data item. Many investment data types are dynamic and can vary over time. These include portfolio exposure, performance and risk measurements, ratings, classifications, and statistical information such as coupon rates, analytics and ratios. Bitemporal data systems are necessary to handle these many areas of dynamic or volatile data, statistics and analytics, in order to achieve better and more accurate management information MI and financial and regulatory reporting.
That higher standard of reporting will better support risk management, regulatory compliance and performance attribution. Having such a bitemporal record of key data and changes to that data is indispensable for reporting, auditing and regulatory requirements. Errors in attributes such as bond coupon rates or conversion. Even better, this information can be corrected while retaining the history of what these items were believed to be at past points in time.
This allows user to look back in time and perform analysis based on the lineage and ensure compliance. It is able to provide a true picture, because it maintains the entire life cycle of a trade or adjustment. Still, each change requires all the relevant data to be stored all over again in the snapshot. Matters get more complicated if data consumers are dealing with multiple vendors or sources of bitemporal data.
According to this, they knew how much he was owed and shortchanged him anyway, which we know to be factually false. This is where Bitemporal Modeling comes in.
To show the disparity between the record and the actual pay-effective dates, we need to record both tracks of time. Enter bitemporality. To read this table, we want to index using the System Time columns.
We know that wherever the SysEnd value is null, the record is current. Again using the System Time columns to index, we see that Feb. This type of bitemporal model is just one of many ways to structure metadata, but it illustrates the concept well.
Implementing bitemporality is no small feat. Nevertheless, large-scale enterprises with high transaction velocity and an inherent need for precise historical reporting will find it an invaluable asset to their Data Quality.
You must be logged in to post a comment. Bitemporal vs. Nontemporal Models In order to understand what our record-keeping options are in this scenario, we have to begin with a nontemporal model. If there is no time-based record of these changes, future analysis of trading positions could be compromised—and organizations could face major fines by regulators.
With a unitemporal database, updates overwrite historical data, which can introduce enormous risk to both individual traders and entire companies. Bitemporal design provides an accurate picture of the entire lifecycle of a trade review, including when changes to counter-party names, transaction IDs or price corrections occurred.
We live in an age of increasing regulation, with no sign of abatement, and many large organizations are having to go through massive change. The financial services sector, for one, has had to make considerable changes since the financial crisis in , but organizations in all industries are seeing an increased call for historical data.
Bitemporal enables organizations to preserve data history, including the changes made to data, so it is easy to collect and present required information for audit and other purposes. Many, if not all, industries can benefit from support for bitemporal. Likewise, government intelligence and law enforcement agencies can use databases with bitemporal support to make sense of seemingly disparate data, enabling them to better understand motives and even better predict future events.
Businesses can tap into this data to determine customer patterns and behavior, for more strategic development of new products and services. While the cost per gigabyte of data is decreasing, organizations today are spending more on storing historical data because they are dealing with so much more of it. Organizations tend to hoard data in general, but regulations often demand it. Bitemporal data management helps keep storage in check because it avoids the need to set up additional databases for historical data.
Developers just want to be able to write queries that can easily access historical data. Further, it can be quite complicated to manage different timelines and versions in a traditional database, placing an additional burden on developers and the DBAs tasked with maintaining the data.
There is an aspect of bitemporal data management that is important to data governance.
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