Data Integration
Data integration is the process of combining data from various sources into a consistent and accessible format.
The aim of data integration is to break down data silos and provide a comprehensive overview of all relevant information.
Data integration technologies and processes
- Extract, transform, load (ETL): Extracts data from various sources, transforms them into a uniform format and loads them into a target system.
- Data warehousing: Saves data from various sources In a central Data warehouse, which is optimized for analysis and reporting.
- Data Lakes: Store all data in its raw form in a central Data Lake, the one for big data-analyses and Machine learning can be used.
- Master Data Management (MDM): Unifies and manages master data to ensure that it is consistent and up to date.
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Benefits of data integration
- Improved decision making: By combining data from various sources companies can get a more comprehensive picture of their business processes and thus make more informed decisions.
- Increased efficiency: Through the Integrate data redundant data inputs and processes can be avoided, which leads to an increase in efficiency.
- Improved collaboration: By providing unified access to data, employees from different departments can work better together.
- Increased customer satisfaction: By integrating customer data from various sources companies can gain a better understanding of their customers and thus improve customer satisfaction.
Data integration challenges
- Data quality: Data quality must be guaranteed before data from various sources can be integrated.
- Complexity: The integration of data from various sources can be a complex and time-consuming process.
- Costs: Implementing and maintaining a data integration solution can be expensive.
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Note: This glossary was created and maintained with the support of AI technologies such as Gemini and ChatGPT.
ETL, ELT, data lakes, and so many options...
Thomas Borlik
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