Centralized vs. Decentralized Data Management

Editor’s Note: This post was originally published in April 2023. We’ve updated the content to reflect the latest information and best practices so you can stay up to date with the most relevant insights on the topic.

Organizations have debated centralized vs. decentralized data management for decades. As data warehouses and data lakes grew in popularity, centralized systems became the norm. Then, data mesh emerged as a new way to manage data, and the pendulum swung toward decentralized systems.

There are benefits to both centralization and decentralization, which is why it can be a challenge to decide if a centralized, decentralized, or hybrid data management strategy is right for your business. The first step is to understand the differences between centralized and decentralized systems. Then, you can determine the best approach for your business. 

What Is Data Centralization?

Data centralization emerged from the rise in popularity of data warehouses and data lakes. Simply put, data centralization manages the storage, cleaning, optimization, and consumption of data from a single point. It aims to minimize data silos by pooling disparate data together in a single place and making that data accessible to anyone who needs it. 

When data is centralized, ownership often resides within the centralized data team. They are responsible for maintaining, cleaning, and optimizing the data, as well as monitoring access to the data based on the organization’s governance and security policies.

Centralized approaches based on data lakes held great promise for many organizations, but the reality is that while pristine data lakes were the ultimate goal, they quickly deteriorated into data swamps. To address this issue, AI-native master data management (MDM) can be employed to clean up the data in the data lake, ensuring that users have access to accurate, consistent, and trustworthy information. However, despite having tools to help improve the quality of the data in their data lake, many organizations are exploring alternative options such as a data lakehouse or cloud data warehouse so they can better deliver the clean, integrated data their users need to make decisions. 

What Is Data Decentralization?

Data decentralization occurs when the storage, cleaning, optimization, and consumption take place outside of a central repository like a data warehouse or a data lake. Data mesh is a popular example of a decentralized approach. 

In a data mesh ecosystem, the data is distributed, with many more individuals across the organization assuming responsibility for ensuring the data is clean, integrated, continuously updated, and consumable by those who need it. A data mesh architecture embodies a number of key principles:

1. Bring the data ownership closest to the people who know the data best using data ownership by domain.
2. Treat data as a product to avoid silos and make the data teams accountable for sharing the data as a product.
3. Implement a new generation of automation and platforms to drive autonomy, making data available everywhere (self-service).
4. Govern data at its source with a new approach that avoids introducing risk.

For data mesh to work, organizations must not only clean and standardize their enterprise data. They must also ensure they have strong platform support, the right governance practices, and a well-defined plan for organizational change. AI-native MDM can help by serving as both a complement and an augmentation to distributed data initiatives. AI-native MDM employs AI to standardize data, promote better understanding across systems and domains, and create useful mappings between data identifiers across the organization in real time. 

Which Approach Is Right for my Organization?

Many businesses struggle to decide which data management approach—centralized or decentralized— is right for their organization. But in reality, it's not an either/or proposition. Most organizations will actually land somewhere in between, where they can take advantage of the best of both approaches. 

When organizations experience a hybrid of centralization and decentralization, they can treat data as a product by embracing clear ownership and user-centric design, inside or outside of a data mesh implementation. And, they can rationalize and standardize their core, mission-critical data without the need to fully decentralize the data that drives innovation.

For example, in a hybrid approach, a centralized team might manage the architecture, governance, access, and security, while decentralized domain teams—such as those focused on customers, suppliers, finance, or compliance—determine how to structure their data, set confidence thresholds, and prioritize enrichment sources.

Regardless of where the organization lands on the spectrum between centralized and decentralized data management, ensuring that their data is clean and trustworthy is a key, foundational step. By using AI-native MDM—and following the MDM journey—organizations can assess their data, improve its quality, and review it with end users so they can ensure it’s ready for production use. This helps ensure the data is a fit for analytical and operational use cases—increasing the organization’s ability to realize holistic value from their enterprise data.