Importance of Data Cleansing in the Process of Record Linkage

Importance of Data Cleansing in the Process of Record Linkage

What is Record Linkage?

The procedure of collecting all the data associated with a single person and scattered in the multiple sets of information, and placing it in one place is identified as record linkage. This procedure of record linkage is significant in a situation when identifiers on the basis of a single individual are not available. Under such circumstances, record linkage is carried out with the use of a probabilistic technique or any other one that is capable of comparing personally identifying statistics for instance name and address that might also involve the chance of error or may change over time.

A Detailed Comparison of Strategies Used in Record Linkage

Record linkage is mostly carried out in an organizational or commercial setting. It is needed to eliminate the identical records from a set of records having people’s information. Methods utilized in record linkage are situated across a range between probabilistic and deterministic strategies. A probabilistic strategy makes use of a number of fields among the sets of information in order to determine the probabilities of similarities between both records of data. These probabilities are illustrated as weight or score of probability that are considered for every set of information while they are compared. In case the final score for a record pair is higher than a defined matching threshold, then they are considered to be the records that are associated with the same person. Therefore, the probabilistic strategy agrees with unpredictability between the sets of information with missing similarities. This means that it has the capability to relate records of information with errors in the fields of linking. On the other hand, deterministic strategies for record linkage extends from simple connections of databases by a reliable entity identifier to further complex stepwise algorithmic linkage. This also involves extra evidence to allow the difference between data records, which are similar. This means that it does not depend on an identical similarity of the entity identifier. Probabilistic linkage methods are comparatively more powerful against errors and consequently provides with improved quality of record linkage than deterministic techniques. Probabilistic strategies are also more flexible in a situation where high volumes of information need record linkage.

What is Data Cleansing?

In order to establish the optimal excellence level of links, a number of standardization and data cleaning strategies are engaged in the operating area of record linkage. These methods are prevalent in the packages of record linkage software and are commonly utilized throughout the record linkage units. A phase of data cleaning usually leads the record linkage process regardless of which linkage method is being utilized.

Data cleaning is occasionally identified as data cleansing or standardization. It is associated with modifying, eliminating or changing fields on the basis of their values. These fresh values will enhance the quality of information and therefore, makes it more valuable for the procedure of record linkage. Increase in the excellence of the primary records of information results in the enhanced quality level of the procedure of linkage. Larger volumes of personal identifying data widely enhance the validity of linkage results.

Data cleansing has been identified as one of the fundamental ways to enhance the quality of record linkage under a situation where intensely classifying personal statistics are not available. Data cleansing is one of the crucial stages in the procedure of record linkage that can take up the most of the struggle of record linkage itself. Sets of data with more insightful strength lead to improved outcomes of record linkage.

Strategies of Data Cleansing

A wide range of methods of data cleansing is utilized when it comes to record linkage. A few strategies of data cleansing strive to enhance the number of variables by breaking apart fields of free text. Further strategies of data cleansing simply pursue to change variables into a particular illustration without making any changes to the actual information. Some of the other additional techniques are planned to modify the data in the fields. This can be done by either eliminating invalid values, altering values or assigning values to the blank fields.

Data Cleansing and Quality of Record Linkage

In a framework of record linkage, the objective of data cleansing is to enhance the excellence of linkage. This includes minimizing the number of all such two records, which are falsely classified as associated with a single person and all such two records wrongly placed as not related to a single person. These errors are commonly identified as false positives and false negatives respectively. In the absence of data cleansing, a number of truly matched records may not be discovered due to the reason that the relevant qualities may not be adequately same.

The strategies for data cleansing usually minimize the inconsistency between the two values of the field in question. By eliminating the nicknames, a further reduced collection of names will be found among the records of information. Similarly, by eliminating the dissimilarities because of the punctuation, another inconsistency will be removed. This leads towards finding a large volume of appropriate similarities, as anticipated.

Data cleansing is a valuable process because of its capability to enhance the quality of record linkage. Data cleansing incorporates a wide range of techniques that will be suitable for particular situations. This includes the use of a fresh algorithm, which identifies and amends majority of the error types and anticipated complications. The algorithm is capable of data cleansing along with addressing all the inaccuracies and discrepancies in the information records or specified field values. When it comes to the utilization of these techniques, it is important to take great care. The importance of obtaining data with improved quality is more important than the time required to process a large volume of data. Therefore, the main focus is on maintaining high-quality data. As an integral part of record linkage process, it can be acknowledged that data cleansing is going to enhance the overall quality.