Clean

What it does

Data is difficult to use when records conflict, are inconsistent, or follow different formats. Clean defines the desired form of a record, then identifies, corrects and validates inconsistent data from internal or external sources, including information that has accumulated without regular maintenance.

Use cases

Clean can be applied to structured or unstructured data from any source, including business records and third-party information. Teams can define rules for fields such as contact addresses before using the information in customer service or operational workflows.

Benefits

Fewer errors can prevent failed deliveries caused by incorrect addresses and the costs of resolving them. Consistent customer information can help service teams respond accurately, while more reliable inputs support better-informed decisions.

How it works

Semantic rules and artificial-intelligence methods identify records that do not meet the agreed definition of clean data, then correct and validate them. Teams can cleanse progressively, check each result, repeat the process when data changes and reuse available, validated rules. Corrected records can also serve as a starting point for completing missing data. Linked-data standards help keep rules and connections traceable and reusable across sources.