It can do this whether the original change comes directly through the Reltio interface, or if it came through a contributing third-party system. Whenever data changes, Reltio can verify the change against these frameworks.
Can You Use Multiple Instances of Reltio's Data Cleanser on a Record?
Yes, Reltio's ad hoc data quality framework supports using multiple instances of these data verification systems in tandem. The process is called chaining. When you chain data verification steps to one another, you can decide how the data quality improvement process behaves according to certain conditions.
For example, you can configure Reltio to interpret multiple forms of addresses and standardize the structure for that data in your database. Some customers may feed concatenated addresses into the system while others use multiple address-specific sub-fields. Reltio can ingest that information and represent the entire address in a single uniform field.
The data cleanser will collect that single field and attempt to clean it. If it is not successful, it may pass the data onto the next step, which would use an alternate mapping of the address cleanser. For instance, it could attempt to interpret individual fields for the address line, the city, and zip code, and more.
Through chaining configurations capable of alternate interpretations together, you can establish a uniform structure for data even when the original source data varies widely.
Reltio Features Robust Metadata Support
Reltio supports searching for records based on any characteristic or attribute, including metadata. This means you can find records based on the data generated when saved, moved, or modified by users. Some examples of metadata you can use include profile creator, last update date, field exist, field missing, and more.
If you configure Reltio to support custom metadata management, you can go even further. Let's say you want to find a set of records that suffered reference data management (RDM) transcoding errors. You can do so by mapping your search specifically to records that feature transcoding errors of this type.
You can also search for things like unmapped values. This tells Reltio to bring you back a list of records with data that fail to reference mapped values. You can even configure Reltio to send that data through an API and produce a custom report that shows which records have unmapped values. This goes far beyond what out-of-the-box data quality cleansers are capable of.
Data Quality Ranking Explained
Reltio assigns a data quality (DQ) ranking to each record in its system. It applies this ranking to records based on a series of algorithms designed to help data governance professionals manage large volumes of data easily.
If your average record has 30 populated attributes, and you have a single record with only 18 valid attributes, that record will rank lower than your average DQ. Records that have lots of populated attributes, a significant number of relationships, and clearly structured data will earn a higher DQ score than those that don't.
This number gives you valuable information about the state of a record at a single glance. We're currently in the process of enhancing this capability to give customers even more control over how these scores are defined, and what kinds of data governance processes Reltio can apply to records that fall outside the mean.
If your business use case can benefit from improved data quality ranking processes, we encourage you to reach out to your customer success team or your account executive and let us know. Tell us how Reltio's data quality ranking system can better fit the specifics of your use case, and what kind of functionality you need to make the most of your data governance processes.
Data Governance with Reltio Webinar Explained:
Other Relevant Content on Reltio Data Governance:
- Data Governance with Reltio: How to Improve the Value of Data as an Asset
- Data Governance Part II: Role-Based Workflow and Collaboration Tools
- Data Governance Part III: Recognizing and Consolidating Similar Profiles