What is Data Consolidation?

In data consolidation, data is gathered from multiple sources and consolidated into one, centralized location. The goal of data integration is to merge disparate data sources into one centralized database. Data consolidation helps companies analyze and make informed decisions based on their data, especially when they have large amounts of data from different sources.

Why is Data Consolidation Important?

Reduces Costs

The process of consolidating data facilitates the elimination of redundancy and inaccuracy in data reporting. By establishing a single source of truth for all collected data, operational costs can be reduced and efficiency can be improved. It is easier for businesses to identify areas of overspending or misspending when they have access to accurate data, allowing them to make more informed financial decisions.

Saves Time

The benefit of bringing all your vital information together in one place, you can save time by not having to go back and forth between various sources. Using data warehouses, consolidated operations and management processes can be easily viewed, providing a 360-degree view of business processes.

Enhances Decision-Making

Decisions can be made more effectively if data is accurate. Information that is relevant and precise is shared across departments and everyone benefits from it. A better understanding of customer data can also enhance customer interactions, allowing for more accurate and timely customer service.

Data Consolidation Techniques

Data consolidation can be accomplished via a variety of methods, depending on how much data there is and what the data is being used for.

Extract, Transform, Load (ETL)

The ETL process involves extracting data from a number of sources, standardizing it, and integrating it into the application for which it is to be used. Depending on the complexity of the project, this method can be managed using ETL tools or hand-coded for small tasks.


It is possible for data engineers to script code that sends data from multiple sources to a central location for data consolidation.

ETL Tooling

Data consolidation jobs requiring more complex data processing can be handled more efficiently with ETL tools, which are more efficient than hand-coding. An example of a data warehouse is a tool that retrieves data, transforms it into a unified format, and loads it into its destination.

Extract, Load, Transform (ELT)

Rather than loading data into a centralized staging area before transforming it, ELT involves extracting data and loading it into a centralized staging area. As a result of Probyto AI’s flexible, reliable, scalable, and robust approach to data consolidation, modern data consolidation needs are met.

Data Virtualization

By applying a virtualization layer to data, the information can remain in its original source while still being accessed through a centralized interface. Although this method is simple to use, there are limitations in terms of its scalability and comprehensive reporting capabilities.

Data Warehousing

Data warehouses are central repositories that contain data from multiple sources. There are three components in the system: the bottom tier (data storage), the middle tier (analytics engine), and the top tier (data viewing, analysis, and reporting). Creating a data warehouse with Probyto AI is a great idea.

How to Get Started with Data Consolidation

  1. Identify Possible Challenges and Resources

Considering potential challenges is the first step in deploying a data consolidation process. Be prepared in case of a problem by considering the sources and storage methods of data. It may be more straightforward to extract or consolidate your data assets if you have an understanding of the legacy infrastructure or storage methods you are using.

  1. Consider Professional Services

It might be necessary to consult a data professional when dealing with complicated configurations or unique data requirements. It is possible to leverage prebuilt, flexible, and scalable solutions by hiring a dedicated data engineer or by engaging a team of professionals. Take care to ensure that your data team addresses every aspect of your consolidation requirements, including compliance, replication, and backups.

  1. Use an ELT Tool

When dealing with large or growing data collections, it’s recommended to automate the consolidation process using an ELT tool. The ELT tools simplify data consolidation by combining data from various sources, transforming it using preset rules, and delivering it seamlessly to the destination of your choice. In order to consolidate data effectively, Probyto AI can help.

  1. Make a Data Consolidation Plan

Information can help you identify potential challenges associated with your data sources. Automate the process of building your data pipeline if you choose to do so. Make sure you build a data consolidation architecture in accordance with a defined schedule that makes use of the resources you have. Before deploying your new process, ensure that it has been thoroughly tested. As soon as you launch, you will be able to take advantage of enhanced analytics and reporting features.


Businesses must consolidate their data in order to grow and succeed. By adopting an efficient data consolidation process, companies can make the most of their data collection. Among various techniques, using an ELT process provides the best results, improving business processes and strategy. The success of your business depends on the ability to take advantage of data consolidation and make it an important component of your enterprise.

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