Data consolidation

Ensure good data in all your systems - and open up the valuable information in your company's data silos.

Increasingly, business decisions in organizations depend on analyses and reporting of corporate data. In addition to the preparation of the available data for meaningful reporting, the preparation of the data is particularly relevant. Poorly prepared data carries the risk of large losses due to incorrect analyses and conclusions.

Data consolidation includes the merging of data from different source systems, the removal of duplicate content and the enrichment of incorrect information.

As such a data quality function, data consolidation is also part of data migration to transfer data from system A to system B. Without prior data consolidation, this process is error-prone and not quality-controlled and duplicates are migrated.

Slide 1

Check out our tool for the optimization of your data quality:



When is data consolidation necessary?

Reasons for data consolidation

Do you use different source systems in which company data is available?

Data consolidation helps you to identify and merge related data records and overlaps in different systems. In this way, you will not lose any relevant information about your customers or the use of your products.

All related information (e.g. about a customer) can thus be identified in the different systems. This allows you to use combined information for your business decisions.


Your data records are incomplete or not clearly assigned?

Data can be enriched with information from reference data and reference datasets in the context of consolidation. The assignment of information from reference data enables you to use your data records correctly.

Data consolidation also enables you to enhance your data with additional information from external systems (Data Enrichment/Data Enhancement). This can provide both time and financial advantages for your business processes, for example, if address data is already completely available in the customer data records and does not need to be manually enhanced.


Are you planning to integrate data or migrate data to a comprehensive target system?

Data consolidation identifies overlaps of data in different systems before they are merged into one target system. This prevents inconsistent data in the target system (for example, due to different units or formats). In the course of the subsequent data migration, you receive complete and combined golden records in your target system


Identify duplicates in your data stock.

By cleansing duplicate content, you can cut down on your data management and save time for data maintenance as well as money for systems and storage (Data Cleansing).


Benefits and results of data consolidation with DataRocket

Use our data management software DataRocket for a successful consolidation of your data. This allows you to prepare your data quickly and securely, protecting you from the negative consequences of incomplete, duplicate or incorrect data.


Eine offene Tür erlaubt den Zutritt zu den Datensilos und Informationen


Close your data silos and slim down your IT landscape and data stocks. “Find instead of search” through data consolidation in a central system.

Hände sind schützend um einen Dollartag gefaltet


Reduce your effort through double processing, data management and operational costs. Save time and money.

Ein Zertifikat für Standards und Qualitätsansprüche


Profit from your good data base and be sure that it meets standards and quality rules.


How exactly does DataRocket support the consolidation of your data?

Data consolidation can be accomplished in DataRocket through an (automated or manual) workflow. The data from the source systems (e.g. a CRM system for managing customer data, an ERP system or a flat file such as an Excel list) is loaded into DataRocket, checked there according to predefined rules and consolidated. The resulting golden records are then the basis for further data analysis or data migration.

Golden Record

DataRocket acts as a hub in a company’s data landscape and as such accesses heterogeneous data sources. The data records from these sources are extracted and consolidated and then merged into Golden Records. This Golden Record or single point of truth is a master data record that combines the relevant attributes from all data sources.

data consolidation using DataRocket and its workflows