Data catalogs store information about enterprise data. They organize ownership, definitions, and relationships for the data available in a company's systems. They enable analysts to find the information they need for a project quickly while empowering compliance teams to effectively manage access to sensitive data.
Catalogs to make it simple for teams to navigate and control data assets - empowering users to:
Many types of metadata can be stored, managed, and discovered through a data catalog:
At each level of granularity, companies will track metadata on data lineage (i.e. what system each piece of data came from), when data was last updated, as well as granular relationships that show how entities relate to one another.
Data catalogs are unbelievably powerful tools when they are kept up to date. However, if information is not populated, becomes stale, or is not treated as the source of truth within the enterprise, the catalog can provide limited benefit to the organization.
Many cataloguing platforms can automatically generate certain metadata from business systems. For example, cataloguing tools will automatically sample a subset of the data in a system, and feed the information into artificial intelligence / machine learning (AI / ML) models to determine whether the data is sensitive (does it look like personally identifiable information) and what it represents (does it look like an email address or a company domain). With automation and effective logic, cataloguing tools can automatically generate and manage certain metadata with little to no human input.
However, there will always be aspects of the cataloguing process that are manual. Users will continue to create new custom attributes, or build valuable data sets that never existed before. Companies need to have a strong culture of data governance - combined with policies, procedures, and controls - to ensure changes are represented in the data catalog for the benefit of the broader organization.
Managing and organizing metadata throughout the entire modern data stack is critical to scalability of a data-driven enterprise. However, with the number of moving parts in the modern data stack, this can quickly become overwhelming. Here is a quick reminder of how complex data workflows can become:
Given such complexity, many data cataloguing solutions focus on three aspects of the workflow above.
Without a clear way to integrate metadata throughout the remaining components of the modern data stack, finding and leveraging data can rapidly become unwieldy.
We believe ELT and Reverse ETL solutions have an important role to play in metadata management
ELT solutions produce highly valuable metadata. Not only do ELT solutions have direct connections into event collection tools and business applications, but they already schematize and annotate information for analytics in the warehouse. ELT tools are well positioned to sync valuable metadata automatically to data catalog solutions, reducing manual efforts, and helping to integrate metadata throughout the entire modern data stack.
Reverse ETL solutions must be able to consume and deliver metadata. For Reverse ETL solutions, it is critical to not only sync raw attributes from the warehouse back into operational tools, but to also leverage, and include, key metadata as well. Similar to how visualization tools need to respect policy enforcement and role-based access control, Reverse ETL solutions need to ensure data is discoverable and accessible, but controlled at the same time.
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