There are different types of rules that can be created in Loome Monitor.
An alert is used to define a data quality or process compliance exception that you want to keep track of and assign to someone to resolve. Learn how to create an alert here.
Annotate an existing list of terms, fields or tables with explanatory notes and classifications. Learn how to create a glossary based on source or source and manual data here or learn how to create a glossary with only manually entered data here.
Define a data set that requires a data steward to manually update additional attributes for reporting purposes. Learn how to create a reference based on source or source and manual data here or only manually entered data here.
Define a set of validation checks to be run for specific columns in your data set. Learn how to create a data quality rule here and then learn about adding validation checks for data quality here.
Check for differences between two data sets. You can compare two data sets in one rule, and view both in the one results table to see highlighted mismatches and orphan records from each data set. You will need to provide a connection for both your source and comparison data set, as well a query for each. The column names and data types will also need to match.
Learn how to create a rule here and learn more about reconciliation rules here.
As well as choosing from different rule types, you can also choose whether you would like to add manually created records to your data set. The option to add manually populated data is provided once you have chosen a rule type.
You can only manually populate rows in a glossary or reference. After you select ‘Manual Data’ or ‘Both Source Query And Manual Data’ as your data source, you can also choose to add additional records to your source data set on the results page, or you can create a data set comprised entirely of columns and rows that you have added in Loome.
Learn more about creating a manual rule using this guide here.