Data integration

Definition

Data integration is the process of combining data from various sources into a coherent data set to provide a unified and comprehensive view of this information. The aim is to bring together disparate data in such a way that it can be used for analysis and decision-making.

Background

The need for data integration results from the growing number of information systems in companies, which often work in isolation from each other. With the increase in digital data sources and the need to process and make data accessible quickly, efficient data integration is crucial for business success.

Areas of application

Data integration is used in almost all industries that rely on data analysis and management. It is particularly relevant in areas such as healthcare, financial services, retail, and manufacturing. In industrial companies, data integration supports supply chain management, customer relationship management, and performance analysis, for example.

Benefits

The main benefits of data integration lie in improving data quality and availability. Integration makes data more timely, accurate, and complete, resulting in better analytical insights and more effective business decisions. It also enables a faster response to market changes and internal requirements.

Challenges

The challenges of data integration include dealing with various data formats and systems, ensuring data consistency, and data protection. Complex data landscapes require advanced technologies and methods for data cleansing, transformation, and consolidation.

Examples

An example of the application of data integration in industry is the combination of customer data from a CRM system with operational data from an ERP system in a B2B retailer portal. This enables a more comprehensive analysis of customer interactions and transactions, resulting in improved customer service strategies and more efficient business processes.

Summary

Data integration is a critical process that makes it possible to combine information from various sources and deliver it in a form that is useful for analysis and decision-making. It is essential for companies that pursue data-driven strategies.