# data integration

> combining data from different sources and providing a unified view

**Wikidata**: [Q386824](https://www.wikidata.org/wiki/Q386824)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/Data_integration)  
**Source**: https://4ort.xyz/entity/data-integration

## Summary
Data integration is the process of combining data from different sources and providing a unified view. It is a type of process that falls under the broader discipline of data management. Data integration enables organizations to connect databases and create cohesive data ecosystems.

## Key Facts
- Data integration is classified as a type of process and is a subclass of data management
- The concept is described as "combining data from different sources and providing a unified view"
- Data integration has aliases in multiple languages including "information integration," "integrating," "intégration de données," and "Datenintegration"
- It is practiced by geographic information specialists
- The topic has a Wikipedia title of "Data integration" and is described in 10 languages including English, German, French, and Korean
- Data integration is associated with the Quora topic "Data-Integration" and has a Zhihu topic ID of 19664630

## FAQs
### Q: What is data integration?
A: Data integration is the process of combining data from different sources and providing a unified view. It involves connecting databases and creating cohesive data ecosystems through common standards and practices.

### Q: Who uses data integration?
A: Geographic information specialists practice data integration, along with researchers, computer scientists, and professionals in memory, cultural, and research institutions who work with historic data.

### Q: What are related concepts to data integration?
A: Related concepts include ontology-based data integration, edge data integration, customer data integration, and data spaces, which are ecosystems that connect databases through rules, standards, and governance frameworks.

## Why It Matters
Data integration is fundamental to modern information systems because it enables organizations to break down data silos and create comprehensive, unified views of information. In an era where data is generated from countless sources - databases, applications, sensors, and user interactions - the ability to integrate this data is critical for decision-making, analytics, and operational efficiency. Without data integration, organizations would struggle to get a complete picture of their operations, customers, or research findings. The unified view provided by data integration supports everything from business intelligence and customer relationship management to scientific research and digital transformation initiatives. As data volumes continue to grow exponentially, effective integration becomes even more crucial for maintaining data quality, consistency, and accessibility across complex information systems.

## Notable For
- Serves as a foundational process for creating unified views from disparate data sources
- Has established itself as a distinct discipline within data management with multiple specialized approaches
- Supports critical applications in research institutions, cultural organizations, and citizen science projects
- Enables the creation of data spaces - governed ecosystems that connect databases through common standards
- Has developed specialized variants including ontology-based, edge, and customer data integration approaches

## Body
### Classification and Scope
Data integration is formally classified as a type of process and is a subclass of data management. This positioning indicates its role as an active, procedural discipline rather than a static concept. The process involves not just technical data combination but also the establishment of unified views that make integrated data usable and meaningful.

### Specialized Approaches
The field encompasses several specialized approaches including ontology-based data integration, which uses semantic frameworks to guide integration, and edge data integration, which focuses on processing data at network edges. Customer data integration specifically addresses the representation of customer information across enterprise systems, while data spaces represent broader ecosystems that connect databases through governance frameworks and common standards.

### Technical Foundations
Data integration relies on various technical foundations and tools. The RAISE - Restore Data Integration Suite provides specialized support for memory, cultural, and research institutions working with historic data. Datalog, a declarative logic programming language created in 1986, provides foundational capabilities for data manipulation and integration tasks. Modern platforms like ontop enable querying relational databases as Virtual RDF Knowledge Graphs using SPARQL, demonstrating the evolution toward semantic web technologies.

### Research and Development
The field has attracted significant research attention, with notable contributors including Norman Paton (Professor of Computer Science at University of Manchester), Christian Bizer (German researcher), Marie-Christine Rousset (French computer scientist), Yannis Tzitzikas (professor at University of Crete), Christian Meilicke (computer scientist), and Paulo Pinheiro (Brazilian-American computer scientist). Research groups like the Ontology Engineering Group at Universidad Politécnica de Madrid contribute to advancing integration methodologies and tools.

### Applications and Impact
Data integration serves critical functions across multiple domains. In research institutions and cultural organizations, it enables the combination of diverse historical and contemporary data sources. For geographic information specialists, it provides unified views of spatial and attribute data. The process supports citizen science initiatives by making diverse data contributions accessible and analyzable as unified datasets. These applications demonstrate data integration's role in enabling comprehensive analysis and decision-making across complex information landscapes.

## Schema Markup
```json
{
  "@context": "https://schema.org",
  "@type": "Thing",
  "name": "data integration",
  "description": "combining data from different sources and providing a unified view",
  "url": "https://en.wikipedia.org/wiki/Data_integration",
  "sameAs": [
    "https://www.wikidata.org/wiki/Q1076677"
  ],
  "additionalType": "type of process"
}

## References

1. [Source](http://data.loterre.fr/ark:/67375/TSO-WBDZJK00-P)
2. Quora
3. [Source](https://vocabs.dariah.eu/tadirah/integrating)
4. [OpenAlex](https://docs.openalex.org/download-snapshot/snapshot-data-format)