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Glossary
Data Infrastructure

Structured data

Structured data is data that is available in a defined format and can therefore be easily processed and analyzed by systems.

Meaning in detailContact us now

What are structured data?

Structured data is an essential part of modern data processing and forms the basis for many data-driven applications. They are characterized by a clear organization and a consistent structure, which makes it possible to store them in tables, databases or other organized storage structures. Thanks to their clear organization, consistency and machine readability, they enable efficient processing, analysis and use of information in companies and organizations of all types.

What are properties of structured data?

  • Formal definition: Structured data is based on a predefined data model that defines data types, relationships, and rules.
  • Organization: Data elements are organized in rows and columns, such as tables or relational databases.
  • Consistency: All data elements within a data set follow the same format and the same rules.
  • Machine readability: Structured data can be read and processed directly by computers without the need for additional interpretation or preparation.

What are the benefits of structured data?

  • Easy processing: Structured data can be processed and analyzed quickly and efficiently by computers.
  • High level of data security: Thanks to the clear organization and consistency, structured data is less susceptible to errors and manipulation.
  • Easy integration: Structured data can be easily integrated into various systems and applications. See Data Integration
  • Comprehensive analysis: Structured data is ideal for complex analyses and reporting. See data analysis

What are the applications for structured data?

  • Business intelligence: Analysis of sales figures, customer data, and market trends to support business decisions.
  • Finance: Account management, transaction processing, and fraud detection.
  • Inventory management: Inventory monitoring, ordering and supply chain optimization.
  • CRM (customer relationship management): Managing customer data, interactions, and support.
  • Human resources: Payroll, time recording and personnel data management.

Examples of structured data

  • Customer data: Name, address, phone number, email address, purchase history
  • Transaction data: date, time, product, price, quantity
  • Sensor data: temperature, pressure, humidity, measurement values
  • Financial data: Account number, account balance, transactions, balance
  • Inventory data: Item number, description, quantity, storage location

More variants

Note: Our team benefited from the support of AI technologies while creating and maintaining this glossary.

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Data Architecture

The right tools in the right place. Update the existing infrastructure or renew most of it.

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Data Governance

Not boring or stuffy at all: Aligning ideas and rules

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Data Culture

A culture and the processes that make everything possible together.

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Data Strategy

When what happens how and why — that explains the data strategy.

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Machine Learning

Not a gimmick, but real added value

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Reporting & Business Intelligence

Not just beautiful dashboards, but information at a glance.

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