What are the Stages in Data Manager?

Assets Data Manager helps teams ensure data is complete, current, and correct.

Data Manager works by moving through a series of steps that progressively improve the quality of your data and reconcile multiple data sources to create a single, trustworthy source of truth — often called a golden record.

Data Stages

The steps that Data Manager moves through are called the Data Stages. There are five Data Stages:

1. Raw data – Fetch data from a file, database, or tool into Data Manager to create raw data.

2. Transformed data – Remove unwanted values and characters to create transformed data.

3. Cleansed data – Eliminate records that are stale, null, or unneeded to create cleansed data.

4. Data Manager objects – Reconcile multiple data sources to create Data Manager objects (golden records).

5. Schema objects – Import your objects into an Assets schema to create schema objects.

Configuring, running, and reviewing

Depending on your team and tools, configuring and testing these data stages may be simple, or it may require review and iteration with stakeholders to ensure accuracy and completeness.

We encourage teams to use Data Manager in an iterative way for each data source by:

• Configuring the features within Data Manager for each data stage.

• Running an operation to change the data.

• Reviewing the changed data with stakeholders and subject matter experts (SMEs).

How to use Data Manager

We recommend that teams:

• Work through the all Data Manager data stages per data source prior to starting the next.

• Ensure that each data sources for a given object class are mapped and stable before running cleansing and merging.

Some operations, such as cleansing and merging, cannot be performed while there is still unmapped data in any other data source for the same object class.

If the data doesn’t look as expected, you can modify your configuration and run the operation again. Once the results are satisfactory, move on to the next stage.