This is a way of storing information where each piece of data is captured with two datetime values:

  • The effectiveAt: the datetime the piece of data is considered ‘valid’, in a business context
  • The asAt: the datetime the data was physically recorded in LUSID

The general principle is explained in this Wikipedia article, this FINBOURNE blog post, and also by this third-party blogger.

Consider a live price quote for a stock, for example:

Vodafone Group PLC (VOD) @ 20 Aug 2018 10:02 UTC = 176.14 GBX

The effectiveAt for this quote is 2018-08-20 10:02:00 UTC. UTC is Universal Time Coordinated, and represents time at longitude 0°.

The asAt would be the datetime the quote was actually saved into the system. This datetime may be slightly later than it’s effectiveAt, as there may have been a delay in the quote being published or uploaded into LUSID.

Whenever any data is retrieved from LUSID, the user must supply datetimes for both the effective At and the asAt (or asAt = ‘Latest’). These datetimes are then used to filter the universe of data, to locate the most appropriate value.

The primary benefit of storing data bitemporally is that it allows all data changes to be recorded and audited. Consider if this quote had been saved with the wrong value. Using the bitemporal model, we are able to correct the value retrospectively. The corrected piece of data would have the same effectiveAt as before, but the asAt datetime on the updated record would correspond to the datetime the correction was made in LUSID. Since LUSID stores both versions of the piece of data, you are able to retrieve the latest (i.e. corrected) value using asAt = 'Latest', or retrieve the original value using the asAt from the original record.

See the article on the Importance of Portfolio Creation Date for another example of the use of bitemporality.