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Glossary
Data Management

Unstructured data

Unstructured data is data that is not in a predefined format and therefore cannot be easily processed by computers.

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What are Unstructured data?

Unstructured data refers to information that is not in a predefined format, as is the case with tabular data in databases, for example. They do not have a fixed structure and therefore cannot be easily analyzed using traditional data processing tools.

Find out more about the aspects of Data architecture and Data Organization out.

Characteristics of unstructured data

  • Missing format: No fixed structure, such as tables or columns
  • Variable content: Can include text, images, video, audio, or a combination of them
  • Big data: Can range from a few kilobytes to several petabytes
  • Difficult processing: Requires specialized techniques such as machine learning and natural language processing (NLP)

Opportunities from unstructured data

Overcoming the challenges of processing and analyzing unstructured data enables new insights, better decisions, and innovative business models.

  • Advanced analysis: Unstructured data provides valuable insights derived from structured data cannot be won. They provide a deeper understanding of customers, markets and business processes.
  • Improved decision making: By combining structured and unstructured data, companies can make more informed and data-based decisions.
  • Innovation and agility: Unstructured data can enable new products, services, and business models and help companies react faster to change.

Challenges of processing unstructured data

  • Storage: Unstructured data requires flexible storage solutions that can handle large amounts of data and different formats.
  • Processing: Analyzing and extracting information from unstructured data is complex and requires powerful computing power and suitable algorithms.
  • Query: Finding relevant information in unstructured data is significantly more difficult than in structured data.

Using unstructured data

  • Sentiment analysis: Analysis of opinions and sentiments in texts, e.g. in social media posts or customer reviews.
  • Customer analysis: Understanding customer needs and behavior through the analysis of texts, images, and videos.
  • Fraud detection: Identifying fraudulent activity in transactions or online activities.
  • Risk assessment: Assessment of risks in finance, insurance or other areas.
  • Maintenance forecast: Prediction of maintenance requirements for machines and systems.

Examples of unstructured data

  • Texts: documents, emails, social media posts, books
  • Pictures: Photos, graphics, scans
  • Videos: Movies, clips, webcams
  • Audio: Music, voice messages, soundscapes
  • Sensor data: Measurements from IoT devices

More variants

Find out more about Data architecture out.

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 Organization

So that the handling and responsibility of data is structured and everyone knows its responsibilities.

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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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