When developing a data catalog, it’s important to have a systematic process. Your data catalog should allow you to highlight important points and group assets into common sets. You should be able to tag assets with a common theme. In addition, you should use governance actions to ensure that your data catalog is always up to date.
Automation tools can be incredibly helpful in the data curation process. They use artificial intelligence and machine learning to identify similar data sets and relate business terms and attributes to them. They can also track the movement of data through pipelines and systems. This helps you determine the impact of any changes made to your data assets. Other features include pre-built reports and dashboards to analyze the usage of your data assets. In addition, a knowledge graph can show you the relationships between data assets.
Data profiling is an effective way to help data consumers understand your data easily. It summarizes information such as the number of tables and files in your database, as well as the dimensions and measures within each table. It can also help you create relevant summaries of the data. It also helps you create guidelines for data storage.
A data catalog also helps data consumers discover related data across multiple databases. For example, an analyst may need to pull together all of the customer information that is stored in different databases (more information). Such consolidated information can help him meet his business goals.
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