Data matching compares two sets of the collected data and is usually based on various programmed loops and algorithms running it. It is done to discard the duplicate data and aid in better search results. It gives you a rich user experience and provides you with data cleansing, profiling, matching and data DE-duplication. Data Matching This technique has made it very simple for the various organizations to share information across different departments. This tool is famous for its use in the Government organizations, where they have to keep a check for frauds in order to keep the public money safe. In a commercial setting this software can be used to identify and keep a record of the shopping habits of the customers in order to build a better relationship with them. So, basically the ultimate aim of this is to regulate and optimize accuracy levels across every area of an organization.
The Data matching software does record linking, object identification and entity resolution. It finds connections between data elements. For instance name and address, the software rapidly removes all the duplicate entries from the database. The powerful matching engine identifies the linked records based on similarities or similar records that contain keyboard errors, or missing words, additional words, nicknames or name variations in different cultures. In the worst case scenario, every record in a database is to be compared to all the other records on the other database. Such tasks are computationally expensive and tough to be accomplished in feasible time.
The benefits of matching data are many, as it helps improves working, efficiency and reduces time spent in manually DE-duping or DE-duplicating contacts. This increases the database reliability and integrity by warranting the accuracy of the data. It also combines data from the number of distinct dissimilar sources. All the records are combined into one error-less record within the database in real time. This improves the customer management with complete views across various departments and databases. It also enables data-driven business decisions by means of better profiling capabilities of customers. It also improves business intelligence with data accuracy.
It has been seen that based on Global Data Quality Research 2016, 90 per cent of the financial institutions consider ever-increasing policies have driven the call for improved data analytic and management. 99 per cent marketers want to convert data to insight. Inaccurate data is undermining the ability to provide an excellent experience as found by 76 per cent of financial institutions. The process of matching data can be done in batches as well as real time. It can be done ad hoc when required. It can match records created instantly and determines if it is a duplicate. It eliminated typos, nick names, suffixes or acronyms and works on sophisticated name matching. Its usage has been found in e-commerce, business mailing lists, computing, healthcare, online fraud detection, and National census. Even though matching data have its challenges and complications as data comes from different sources, formats, age, errors, as well as unpredictability.