Cleanse and standardize data overview
Learn how to cleanse and standardize data with cleanse and validation functions.
You can't make great decisions using bad data.
To build trust in your data, you need to cleanse it. Data cleansing - often referred to as data cleaning or scrubbing - involves removing duplicate and irrelevant data, identifying and resolving structural issues, and flagging missing or incomplete data within a dataset.
Whether you use Reltio's out-of-the-box cleanse functions or your configure your own using the API, you'll love that freshly cleansed data smell!
Data cleaning is the process of identifying incorrect, irrelevant, and incomplete data and then replacing or modifying the data appropriately. Only when you cleanse your data, you can use it to gain insights that help your company to make better decisions.
Reltio supports out-of-the-box cleanse functions for Address, Phone, Email, Name, String, and some custom cleanse functions. All of these cleanse functions are typically pre-configured and already functional in the tenants you receive with your subscription. However, each of the functions can be tailored as per the requirements.
Data standardization, on the other hand, is the process of transforming data (available in different formats) to a standard format as defined by the customer. Standardized data follows a certain format and rules for consistency. It is enhanced in terms of the efficiency and also boosts the filtering capability.
Understanding Data Cleansing and Standardization