Data Catalog: Selection and Implementation

Govern and Discover
your Data Assets at Scale

Depending on where you are in your data journey, you may be looking to implement a data catalog or a custom metadata management strategy. Metadata management is an essential foundational capability in any modern data stack. An effective metadata management strategy and implementation is critical to a comprehensive data strategy.

We take a tactical approach with clients to align your strategy and implementation with value creation for your business.

We understand what metadata needs to be managed and help you build a governance framework to manage it effectively in the data product lifecycle.

Once requirements are precise, we will help clients select and implement an appropriate modern data catalog tool or a custom solution to act as a metadata repository and provide efficiencies across metadata management activities.

Metadata Management Strategy

The value that can be gained from implementing a modern data catalog can be immense for your organization.

It should serve as a knowledge repository where knowledge workers like data scientists, business analysts, security and compliance officers, and data engineers can look, understand, discover, and govern data assets at scale.

Often, we see clients who purchase or implement an open-source data catalog that sits there largely unused.

Data teams will treat the implementation as simply a technical endeavor. Its essential to think of your implementation strategically and address some of the various aspects of a successful metadata management strategy.

We will sit with you and your team and help you understand the various aspects of metadata management, formulate an implementation plan, and execute it. 

Governance and Workflow Development

Understanding how the data catalog fits into the larger data product lifecycle is one of the most helpful ways to start a successful data catalog implementation.  

Understanding what metadata needs to be extracted from the technical fabric, and what governance checks need to be put in place along the way, creates the foundation for the data product release process.  

To create these workflows requires a sociotechnical approach, leveraging automation in the technical fabric and implementing appropriate roles and responsibilities among the organization.  

This ensures all data assets are well curated and governed based on well-understood requirements.