One of the more tedious and time-consuming processes for businesses, yet one which must be completed at regular intervals, Data Matching is data reconciliation. It can be quite a process for businesses of all sizes, but larger businesses tend to find it more difficult and lengthy, due to the vast quantities of information which must be managed on a daily, monthly and annual basis.While data reconciliation is a necessary process, performing it by hand can take a great deal of time, and ties up employees who could be doing other things, and bringing in revenue for the company. The loss of revenue earned while employees are performing data management tasks, combined with the increased labor cost and the potential for costly errors, makes it highly beneficial for companies to automate this process utilizing computer-based reconciliation tools.
Data reconciliation programs help companies of all sizes (or even individuals) recoup the time and money which are frequently wasted on tedious data management tasks. To do so, such programs utilize the process of deterministic matching to match up bits of information contained within two sets of data. Deterministic matching is an automated system, yet it offers users a great deal of control over the data reconciliation process, as users are able to set the parameters for the program to use, and determine which bits of information should be kept or discarded.There are three different types of matching which are typically utilized by data reconciliation software. The first is equal mapping, which is exactly as it sounds: the process of finding matches between data with identical values. The second type of mapping, within mapping, allows the program to find matches where the second value is within a tolerable variance of the first.
The size of the tolerable variance is set by the user, so it's easy to customize the program to yield the degree of exactness desired in the results.The third type of mapping, called contra mapping, works by matching values that, when combined, will yield a value that is within a tolerable variance of zero. Similar to within mapping, users are able to set the tolerable variance for this process as well. The different types of mapping that are available with data reconciliation programs, combined with the degree of control afforded to the user, make these programs highly customizable, and suitable for a wide variety of applications.
Many companies hesitate to invest in data reconciliation software, because of the cost of implementing such a system. While the program can cost several hundred dollars, this is a nominal expense, as far more important is the amount of money which will be saved on labor costs. The process will be exponentially faster, meaning that employees will have to spend less time on it, and while the program does the work for them, they are free to focus on other tasks. The program also reduces the risk of errors, which can cost a company dearly in terms of not only time and money, but a suffering reputation as well.Therefore, while the cost of implementing the system must be considered, it should be viewed as an investment, with all likelihood of a high potential return.