Data cleansing at a glance
Get a high-level view of Data cleansing.
Take it from the top with Data cleansing. Get the overview here, then see the sub topics in this section to drill down to the details.
What is data cleansing?
- Data profiling
- This stage of the process involves examining your source data for anomalous patterns, including:
- Determining the quality, range of values, consistency, and completeness of data within a source and across all sources
- Identifying the source attributes that qualify as good elements for matching purposes
- Identifying the source attributes that must never be used in the matching process. These attributes may negatively impact the performance or result of the matching.
- Identifying the reference data, consistency, and commonality of the referenced data across sources
- Identifying the attributes that can be used for faceted search
- Data mapping from customer data sources to the target model within the Reltio Data Cloud
- Data cleansing
- This stage of the process involves identifying incorrect, irrelevant, and incomplete data and then replacing or modifying the data appropriately. The process involves removing duplicate and irrelevant data, identifying and resolving structural issues, and flagging missing or incomplete data within a dataset. This stage is also referred to as data cleaning or scrubbing.
- Data standardization
- This stage of the process involves 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.
- Data validation
- This stage of the process involves ensuring the accuracy and improving the quality of source date before you load it to Reltio Data Cloud.
Who is Data cleansing for?
This content is curated for these Reltio user roles defined in topic About roles:
Business User
Data Steward
Developer
Why would I use Data cleansing?
You can't make great decisions using bad data. To build trust in your data, you need to cleanse it.
Data accuracy is an essential component to track the quality of data. As the volume of data and complexity of systems increase, errors in data become inevitable. Such data errors can lead to incorrect business decisions.
As a Configurator, you want to configure data validation functions to ensure that the data meets the business needs.
As a Data Steward, you must be able to view exceptions for the data that is loaded in the Hub so that the corrective actions can be taken. The data is validated against the rules that the Configurator has configured.
As a Business User, you want to search and identify the data in the Hub that reduces the accuracy of the business decisions.
When would I use Data cleansing?
Cleanse data as you load it in your Reltio Data Cloud and validate the ingested data against a set of validation functions to confirm the correctness of the data.. Only after you cleanse your data can you use it to gain insights that help your company to make better decisions.
Where do I find Data cleansing?
Reltio Multidomain MDM
Reltio Customer 360
Your Reltio tenants are preconfigured with out-of-the-box cleanse functions for Address, Phone, Email, Name, String, and some custom cleanse functions. Each of these cleanse functions can be tailored to your requirements, and you also can configure your own using the API.
Configure Data Validation Functions (DVFs) in the . For more information, see topic Data Modeler at a glance.
View DVF results in . For more information, see topic Profiles at a glance.