Decentralized Data Management

A decentralized data management approach involves distributing data across multiple systems and locations. This approach allows for greater flexibility and agility, as well as improved data ownership and governance.

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Components of a decentralized approach

Data mesh, data fabric, data governance

No matter which modern analytics solution your organization chooses to use for your valuable data, smart data automation can help with the process.

Data mesh

Data mesh allows for greater agility and flexibility. It emphasizes the decentralization of data ownership and governance. It involves breaking down data silos and empowering individual teams to take ownership of their own data domains.

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Data fabric

Data fabric is often used in complex, heterogeneous environments where data is distributed across multiple systems and locations. It provides a unified view of data, regardless of where it is stored or how it is accessed.

Data governance

Data governance is essential for ensuring the accuracy, consistency, and security of data across an organization. It involves defining data standards, establishing data ownership and accountability, and ensuring compliance with regulatory requirements.


Benefits of a decentralized data management approach

While decentralized data management has become increasingly popular in recent years, there are still compelling benefits to using a centralized approach for managing your organization's data.

Improved security with reduced risk of data loss

By distributing data across multiple nodes, it reduces the risk of data loss by ensuring that data is stored redundantly across multiple nodes. It also means that if one node fails or is compromised, the data can still be accessed and recovered from other nodes in the network.

Improved scalability and felxibility

By allowing additional nodes to be added to the network when needed, this approach makes it easier to handle large amounts of data and traffic. This can be especially important for organizations that need to process large amounts of data quickly.

Increase transparency and accountability

Because all nodes in the network have access to the same information, this transparency and accountability can help prevent errors and inconsistencies that can arise when different parties work with different versions of the same data across an organization.


Challenges of a decentralized data management approach

While a centralized data management approach has its advantages, it can also present some challenges for organizations.

High complexity and difficulty

Decentralized networks operate differently from traditional centralized networks, which can make them more difficult to set up and manage. Additionally, because there is no central authority, it can be challenging to establish protocols and governance frameworks that ensure effective communication and collaboration between nodes.

Possible slower performance

Decentralized networks rely on multiple nodes to store and process data, performance can sometimes be slower than with a centralized approach. This can be especially true for large-scale networks with many nodes, but advancements in technology have enabled decentralized systems to achieve higher performance levels.

Specialized knowledge and expertise required

This can be difficult to find, especially in smaller organizations with limited resources. However, there is an abundance of resources available, such as consultation that can help organizations develop the necessary expertise to successfully implement a decentralized data management system.

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Overcoming challenges in Implementing a decentralized data management approach

Strategies that can be employed to overcome these challenges and ensure the successful adoption of a decentralized data management system.

Standardization and Governance Frameworks

The establishment of standardization and governance frameworks includes developing protocols for communication and collaboration between nodes, establishing clear rules and processes for decision-making, and ensuring effective coordination across all nodes.

Phased Implementation of system

Phased implementation involves gradually integrating the new decentralized architecture with existing systems to test different components of the system, identify potential issues early on in the process and make adjustments before deploying them on a larger scale.

Building a Culture of Collaboration

Encourage open communication and collaboration between different departments and stakeholders, as well as promoting a mindset of experimentation and continuous improvement can help ensure that all parties are aligned with the goals of the project and are working together to achieve them.


What our customers are saying

See what industry leaders say about biGENIUS and their experience of working with us.

“Thanks to biGENIUS, we were able to lay the foundation for combining data from different sources into one report or dashboard.”

Evi Verschueren
Business Intelligence Team Lead, Smurfit Kappa

“The efficient DWH generator enabled us to achieve the required transparency with regard to marketplace performance within a very short time, in high quality and in compliance with BI best practices.”

Andreas von Ballmoos
Business Intelligence Lead, Scout24

“The biGENIUS team is knowledgeable about the inner workings of the application - stuff that you can't work out yourself as a customer.”

Tobias Rist
Data & Analytics Architect, Swica

“For us, it's really a central tool that should do a lot in helping people have a standardized approach for the whole firm.”

Sébastien Brennion
Business Intelligence & Analytics Engineer, Valiant Bank

“Since we have been using biGENIUS, we manage the development 100% internally. We did not only save a lot of money but, having built everything ourselves, we gained in efficiency as we are able to adapt any user requests right away.”

Marc Buthey
IT & Project Manager, Tirus International SA