Some data matching tasks don’t succumb to a single matching condition. In these cases, it’s common to use an ordered list of rules to process the data one at a time, reducing the unmatched items gradually as each rule is applied.
An example cascade for cash flows might be:
- Match entries with identical references on both sides
- Match entries with exact numeric value
- Match entries falling on same value date
- Match totals per day
Depending on your background and area these may seem reasonable or not. Sometimes a balanced total for the month is good enough, sometimes only exact multi-column criteria is appropriate.
Of course, a single outcome is also not necessarily desired. An exact match might trigger the release of an order, while approximate matches get flagged for manual confirmation and omissions are ignored for a grace period before being queued for a support function. Many flavours and combinations are possible.
