Centralized Data Management

A centralized data management approach involves consolidating data from various sources into a central repository. This approach allows for easier management and analysis of data, as well as improved data quality and consistency.

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Examples of a centralized approach

Data warehouse, data lake, or data lakehouse?

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

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Data warehouse

Data warehouses are specifically designed for analyzing data that has been collected, contextualized, and transformed. If your organization requires advanced data analysis or analysis that relies on historical data from multiple sources across your organization, a data warehouse may be your right choice.

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Data lake

Data lakes store a wide variety of unfiltered data that is later used for a specific purpose. If your organization needs cost-effective storage for raw, unstructured data from multiple sources that you plan to use for specific purposes in the future, a data lake may be your right choice.

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Data lakehouse

Data lakehouses use best practices from both data warehouses and data lakes. They can handle both structured and unstructured data, which is stored in open formats allowing different engines to run simultaneously. Data lakehouse is a flexible and scalable solution for you.


Benefits of a centralized 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 data quality and consistency

With a centralized approach, all data is stored in one place, making it easier to enforce data standards and ensure that all data is accurate and up-to-date.

Increase efficiency

By storing all data in one location, it can be accessed more quickly and easily. This can be especially important for organizations that need to process large amounts of data quickly.

Increased security

With all data stored in one location, it can be easier to implement security measures such as access controls and encryption. This can help protect sensitive information from unauthorized access or theft.


Challenges of implementing a centralized approach

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


As the volume of data grows, it can become difficult for a centralized system to handle the load, leading to decreased performance and slower processing times, whereby organizations must consider devoting more resources or upgrading their technology infrastructure.

Technology and resources

Implementing a centralized data management system often requires new hardware, software, and personnel with specialized skills. This can be expensive and time-consuming, especially for small or mid-sized organizations with limited resources.

Planning and coordination

When planning your implementation of a new system, it can be challenging to coordinate between different departments, especially within a large-scale organization, as they may have different needs and priorities when it comes to managing their data.

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Accelerate and automate your analytical data workflow with comprehensive features that biGENIUS-X offers.

biGENIUS-X use cases

Overcoming challenges of a centralized data management approach

biGENIUS-X features that help ensure the successful adoption and maintenance of a centralized data management system.

Streamline data engineering with biGENIUS-X.
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Replace old-fashioned or hand-coded ETL

  • Benefit from the outstanding performance and scalability of your data solution in the long run.
  • Easily adapt to change with automatic metadata and documentation.
  • No longer have to worry about your ETL server being a bottleneck.
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Modernize your data warehouse

  • Modernize your data solution according to best practice architecture.
  • High-level of standardization ensures that your solution can be expanded whenever needed.
  • Flexible and adaptable to new business requirements.
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Build data lakes effortlessly

  • Use the history options if you want to store a long history of data in your data lake.
  • Benefit from near real-time data integration based on the generator configuration from biGENIUS.
  • Migration to another data lake platform can also be easily done.

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