Mergers and acquisitions often involve the transfer of data on large scale. Particularly, in mergers where two or more companies integrate to become one, the databases of both companies and transferred and combined. Depending on the size of the companies involves, the process of data transfer can take weeks, even months. In the current age of information and technology, data has the utmost importance. Any mistakes made in the process can cost the organization immensely both monetarily and in terms of value. According to a report by Forbes Magazine, the business loses more $10 million dollars every year on average due to poor data.
Decisions made based on false or incorrect data cause organizations to suffer considerable losses both financially and in terms of goodwill. In order to make the process of data transfer and merging more efficient and accurate, major companies now make use of latest data matching products. Data matching allows large amounts of data to be transferred from different databases in reliable and time efficient manner. In order to fathom the pivotal role played by data matching in mergers, it is important to have better understand what data matching is and how it works to make mergers between organizations smoother.
Data matching 101
Data matching is essentially the ability or act of identifying corresponding results in enormous databases within seconds. These corresponding results can pertain to personnel working in an organization or the contact details of a company’s employees. Data matching, alternatively known as record linkage, allows companies to identify identical or even similar data within a large database. These similar or duplicate records can be merged into one to increase efficiency and decrease clutter. In a way, data matching plays a role in cleaning up data within large databases.
Making use of mathematical algorithms and logic, data matching tools allow people to handle large amounts of data in efficient and convenient manner. There are essentially two types of data matching processes. The first one is deterministic record linkage which is based on a certain number of corresponding or matching keywords. Whereas the other type of data matching is based on probability. The records linked through this process are not identical but are similar to one another. In both processes, unique keywords or identifiers are used to find certain sets of data within a huge database.
In simpler terms, data matching is an effective and reliable solution for managing data in large databases. It helps to keep the process of record keeping accurate allowing organizations to make more effective decisions with greater efficiency. Data matching tools were first developed for businesses with gigantic warehouses with thousands and thousands of inventory to keep track of. With the digitization of records, data matching has significant applications across several different industries such as healthcare, finance and national security.
Role of Data Matching in Mergers
A merger of two, in some cases more than two, companies into one organization requires a great amount of work. From the transfer of employee records to details of projects under progress in both companies, all of it encompasses a vast amount of data. At a stage where two companies are merging together to work as a single unit, there’s a plethora of data from both sides which needs to be reviewed, recorded and merged to form new records for an organization created by the merger of two organizations.
Data matching professionals derive key drivers to find identical records in the databases by investigating the type of data across the companies. For instance, the data matching professionals ascertain potential areas of similarity as well as differences between the records of each organization. Once the compatibility of the data has been evaluated by data matching professionals, the identical records are merged to form new ones.
Merging the records of two different organizations is a quite daunting task which requires great expertise and experience. There are several factors which can potentially affect the merging process. The record keeping methods vary in different organizations which can hinder the process of data matching and merging. Even the software and technology used by each company plays a role in how the data is managed, transferred and merged during a merger.
Importance of flawless quality data matching in Mergers
It is extremely important that the merging of records is done in flawless and accurate manner. If the quality of data matching procedures during the merger is below par, it could impact the integrity and reliability of data. Bad data can cause immense financial loss and in some cases even physical harm. For instance, if the two companies merging together as financial institutions, even a minor mistake during the data matching process can potentially cause losses worth millions of dollars. On the other hand, if the organizations involved in the merger are health care institutions, any discrepancies in the data matched and merged can have severe implications. As often patient history is also recorded in the databases of these institutions, a poorly matched data could cost someone’s life.
Due to the immense implications of bad data matching, it is imperative to carry out important tasks such as a merger using the most reliable data matching products. When data worth millions even billions of dollars are at stake, it is wise to opt for the most reliable data matching services. Despite having revolutionized the system of record keeping in a large organization by improving efficiency, data matching tools can also result in huge losses if the quality of data matching is poor.
What can impact the quality of data matching during a merger?
Predominantly, there are two major factors which can impact the quality of data matching during a merger. The first and most important factor which has a direct impact on the quality of data being matched and merged is the data matching software being used. With increasing popularity of data matching tools in different applications, several subpar data matching services and products have flooded the market. Therefore, it is vital that the data matching service or software being used to link and merge records is of the utmost quality and is extremely reliable.
The second factor which can adversely impact the quality of record matching and merging is the expertise of the personnel handling the process. In most occasions, the inexperienced people using data matching tools results in erroneous record linkage. It is extremely important that specialized data analysts who are equipped with the necessary expertise carry out large-scale data matching and merging exercises are hired for this job.
In summation, data matching plays an integral role in mergers between organizations. Considering the vast amount of data involved in a merger between two large companies, data matching carries out a tedious and humungous task within a matter of hours. It ensures that the transition from two different companies into one uniform organization is smooth and flawless.