Boosting Data Governance

Make Your Policies Computational

Data Governance heavily relies on human input to accomplish metadata curation, data masking, data quality, data access, and more general compliance.

Witboost automates all of this!

Elevate your data governance practices with a focus on robust data quality standards and security measures.

With Data Access Management ensuring secure access, your organization can unlock the full potential of its data, driving informed decision-making and innovation while maintaining security, compliance, and integrity.

Data Quality

Enforce the creation of Data Quality controls by all the teams before they go into production

Compliance

Projects marketed as GDPR-sensitive must have data deletion mechanisms implemented

Data Privacy

Ensure all data projects implement row filtering and column masking pre-production

Data Catalog Completeness

Curate business metadata as part of the Software Development Lifecycle and then push them into your data catalog for 100% completeness

Business Glossary Coherence

Integrate your Business Glossary for better business terms accuracy, with LLM suggestions to choose the best business term for each data set

Data Sharing Agreement

Mandate clear declarations of service levels, intended use, limitations, and other key details from each Data Producer to enhance data understanding

Watch the video to learn more about Scania's Data Mesh evolution.

Learn more about Enel Group's Data Mesh architecture.

Learn more about Poste Italiane's Data Mesh Implementation.

Domain oriented Ownership

A unified workspace for each domain with full autonomy in delivering value, while leveraging the platform's capabilities end-to-end.

Self-Service data infrastructure as a Platform

Profoundly automate your Mesh with Templates. Each has full provisioning automation along the entire data product lifecycle.

Federated Computational Governance

Enforce your governance with computational policies along the delivery process. All handled by the platform team.

Data as a Product

Standardize Data as a Product thinking with Templates and boost interoperability across data silos, ultimately breaking them down.

Witboost is the ideal platform upon which to build a Data Mesh solution for companies of any size. When Vishnu Chintamaneni, director of Engineering and Anjali Gugle, Product Security and Data Strategy Manager at Cisco (one of the largest technology companies in the world, ranking 74 on the Fortune 100 list) envisioned “Stellaris” - a new data and API platform based on the Data Mesh architecture, the choice was obvious: build it on the Witboost platform.

The project aims at solving the scalability problem of their current architecture, due to highly coupled pipeline decomposition, hyper-specialized ownership, and loss of context. All this leads to ineffective data management and weak governance, stifling productivity and innovation. The solution is a Data Mesh architecture built on Witboost.

This will allow Cisco to become more data-driven, democratize data, making it available securely for proper use, increase data literacy and data quality, enable visibility, clarify ownership, and provide transparency. In other words, the goal is to solve the issues presented by centralized, monolithic data lakes by treating domain-based data as the end-product. This will empower separate business domains to host and serve their datasets in an easily consumable way and also enable analytics that truly reap the benefits of its data.