Identity resolution is the process of determining that two or more data representations can be resolved into a single object representation. This is not limited to the names or addresses of people, but also includes product names, product codes, object descriptions, reference data, and more. Identity resolution is based on the fact that errors infiltrate data sets and prevent simple matching algorithms from working. It is achieved through a set of algorithms that include analysis, standardization, normalization, and then similarity scoring and comparison of records to determine when two (or more) records are resolved into a single entity.
Contextual information can refer to a person's acquaintances and social relationships, such as roommates and co-workers. Ultimately, identity resolution measures the degree of similarity between two records, often based on the weighted approximate coincidence between a set of attribute values, and compares the score to a “coincidence threshold”, above which a presumed coincidence is indicated. Deterministic matching is a more conclusive approach to identity resolution when data security or accuracy are an important requirement. Probabilistic solutions may be the best approach when managing identity needs on a large scale, especially with anonymous data. Data cleansing is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database.
It is an important process as it helps to maintain data integrity and accuracy. Data cleansing involves identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying or deleting this data. It also involves identifying duplicate records and merging them into one record. Data cleansing is an important step in preparing data for analysis. It helps to ensure that the data is accurate and up-to-date before it is used for any purpose.
Data cleansing can also help to improve the performance of applications that use the data. In conclusion, identity resolution and data cleansing are two different processes that are used to ensure accuracy in data sets. Identity resolution is used to determine that two or more data representations can be resolved into a single object representation while data cleansing is used to detect and correct (or remove) corrupt or inaccurate records from a record set.