The Rise of Data Products: How Centralized Governance is Fueling Scalable Data Success

AUDIENCE: Business and Technical

LEVEL: Basic

Introduction

In the last two years, we have seen a surge in interest around operationalizing data products. This trend reflects an industry pivot toward creating reusable, modular data solutions. Data products often incorporate not just data but metadata, visualization code, and analytical tools. All of this together boost organizational efficiency. However, the enthusiasm that once surrounded data mesh architecture has declined, raising questions about its viability in today’s governance landscape. In this article we will discuss what’s driving these changes and what it means for the future of data infrastructure.

The Rise of Operationalized Data Products

One of the clearest observations is the industry’s focus on operationalizing data products. Organizations are increasingly implementing standardized, reusable components that blend data with metadata. But it doesn’t end there, they even include visualization and analysis tools.

For practitioners, it’s a game-changer: a scalable way to build, share, and reuse valuable data assets.

This modular approach benefits organizations by streamlining data processes, cutting costs, and improving time-to-insight across various business functions.

What Happened to the Data Mesh?

Despite the hype surrounding data mesh in recent years, we have noticed Ta distinct decline in enthusiasm. Three years ago, at Gartner Data and Analytics summit, we had so many companies push data mesh and over the last two years we have seen a steady decline. Data mesh, with its promise of decentralized data ownership and governance, was designed to offer agility and flexibility by distributing responsibility across business domains.

Core pillars of Dash Mesh: domain-oriented ownership, data-as-a-product philosophy, federated computational governance, and self-service infrastructure — are still relevant.

However, #federatedgovernance remains a significant stumbling block. The reality of achieving cohesive governance across distributed teams has proven challenging, and organizations are finding the coordination and consensus required difficult to sustain.

Governance Challenges: Why Data Mesh Falls Short

A major reason for data mesh’s struggle is federated computational governance. Federated governance, which relies on committees or collective decision-making to enforce policies, creates inconsistency. However, this model lacks the centralized accountability needed to enforce stringent policies across the board. Without a centralized approach, data governance standards can vary dramatically, undermining data quality and security and making it challenging for teams to collaborate effectively.

Centralized Governance and Data Ops: The Key to Success

Instead of a federated model, organizations are realizing the benefits of centralized governance. This model brings essential governance elements — such as data cataloging, quality checks, observability, lineage tracking, and access control — under a single management structure. Technologies from Privacera Alation offer ideal options for organizations that a looking for a centralized governance option, while having the flexibility of operationalizing data products. By centralizing these responsibilities, organizations can ensure consistent policies and efficient enforcement, allowing business units to create data products without worrying about governance misalignment.

Further, establishing centralized #DataOps frameworks can streamline data preparation, testing, and deployment. By implementing a consistent CI/CD pipeline, monitoring, and orchestration tools, organizations can enable faster, more reliable data product development. This approach supports a hybrid model, where data product creation is decentralized, but governance and quality assurance remain centralized.

Data Virtualization: Enabling Flexible Access Without Duplication

#Datavirtualization has emerged as a valuable tool for organizations adopting this centralized approach.

Data virtualization allows for multiple virtual views of the same data without moving or copying it, enabling efficient data access across departments while minimizing storage costs and complexity.

This flexibility supports the operationalization of data products by offering business units easy access to data assets while maintaining security and integrity. Look into technologies, such as Denodo that offers a very viable option here.

Conclusion

This becoming clear that organizations are gravitating for operationalized data products.

By focusing on centralized governance and standardized DataOps practices, organizations can enjoy the best of both worlds — centralized control with decentralized creation and access.

While the data mesh concept provided initial inspiration, the lack of effective federated governance has led many to favor centralized approaches that offer consistency and accountability. With these insights, organizations can confidently chart their path forward, building efficient, flexible, and scalable data ecosystems that deliver real business value.

Amir A. Rana H. Myles Suer Vincent Goveas Piet Loubser Balaji Ganesan