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Embracing a Hybrid Data Governance Strategy: Balancing Control and Flexibility

Contributors: Chandler Kneer

In this eBook, our data and analytics experts explore the evolution and future of data governance, discussing why companies are increasingly embracing a hybrid data governance strategy to enable greater control and flexibility within their data management practices. Download the full guide here, and read an overview of what’s included in the eBook below.

The history of data governance reflects the evolution of data management practices, regulatory requirements, and technological advancements over the decades. Governance over data originated organically from the analysts, as there was a need identified for consistency to improve data quality and analysis efficiency. This led to a very decentralized strategy throughout organizations. 

Initially, data governance emerged in a decentralized manner, where the responsibility for managing and enforcing data governance policies, procedures, and standards were distributed across various departments or business units. Each unit managed its own data according to its specific needs and practices. 

As data management practices evolved, organizations recognized the need for a more structured approach, leading to the adoption of centralized data governance models. In this model, a single, central authority or team is responsible for defining, managing, and enforcing data governance policies, standards, and procedures across the entire organization. 

A Guide to Hybrid Data Governance Strategy

In this eBook, we explore the benefits and challenges of centralized and decentralized models and why the modern approach of a hybrid data governance model is effective. Also for companies looking to implement a hybrid data governance model, we have 7 key steps to keep top of mind and share a few examples of hybrid data governance practices you can adopt within your organization. A hybrid data governance model provides a balanced approach that can adapt to the needs of various organizational contexts, promoting both governance and agility. By combining the strengths of centralized and decentralized models, organizations can achieve consistency, compliance, and high data quality while maintaining the flexibility to address specific departmental needs.

Download the Hybrid Data Governance Strategy eBook Here

For guidance on establishing a hybrid data governance model or on our lessons learned from past experiences, reach out to Clarkston. Our data and analytics experts can help.

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Tags: Data Governance