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Link unstructured to structured data

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Link unstructured to structured data

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Organisations lean heavily on data to make smart decisions, form strategies and innovate. Data, also known as data, falls into two main categories: structured and unstructured data. Both types offer valuable insights that can improve efficiency and customer engagement.

Structured data: order and efficiency

Structured data is neatly organised and easy to read, making it easier to analyse and retrieve. Examples include databases, spreadsheets, ERP and CRM systems, SQL databases, XML and JSON files, tables in HTML documents and data warehouses. These organised data play a crucial role in communication between different systems and platforms, making information management more efficient.

Unstructured data: creativity and challenges

On the other hand, unstructured data is not organised and occurs in e-mails, social media, text documents, audio files, videos, images, PDFs, presentations, blogs and web pages. The International Data Corporation (IDC) predicts that about 80% of all data worldwide will be unstructured by 2025, and many large companies have already crossed this threshold. Unstructured data can be difficult to understand, but with the help of smart technologies, it can be analysed to extract valuable insights.

The rise of AI and automation

Technological advances, especially in artificial intelligence (AI) and automation, have changed the way we understand and use data. Large language models (LLM) powered by AI can understand and generate text, and are particularly good at processing unstructured data. This has led to smoother integration of structured and unstructured data in knowledge work processes.

Navigating through unstructured data with Generative AI

Using generative AI for unstructured data offers both benefits and challenges. Benefits include improved understanding of nuanced information, extraction of meaningful insights, automation of repetitive tasks and improved accuracy in, for example, health diagnoses. Challenges include increased costs, complexity in training, the need for specialised expertise and ethical considerations such as addressing potential biases.

Knowledge work and innovation in the digital age

Advances in technology make it possible to combine structured and unstructured data effectively in work processes. Digital transformation enables knowledge workers to use AI to analyse unstructured data, shaping the future of automation and competitive advantage. While structured data remains essential for clear communication, unstructured data opens up new opportunities for creativity and innovation. Understanding both types of data and their interplay is crucial for modern knowledge workers.

And that is exactly what M-Files is good at: bringing unstructured and structured data together for more insights, faster and more efficient work and optimal knowledge management. Curious what that looks like? Schedule an online demo and find out at a time that suits you.
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Knowledge files

Organisations lean heavily on data to make smart decisions, form strategies and innovate. Data, also known as data, falls into two main categories: structured and unstructured data. Both types offer valuable insights that can improve efficiency and customer engagement.

Structured data: order and efficiency

Structured data is neatly organised and easy to read, making it easier to analyse and retrieve. Examples include databases, spreadsheets, ERP and CRM systems, SQL databases, XML and JSON files, tables in HTML documents and data warehouses. These organised data play a crucial role in communication between different systems and platforms, making information management more efficient.

Unstructured data: creativity and challenges

On the other hand, unstructured data is not organised and occurs in e-mails, social media, text documents, audio files, videos, images, PDFs, presentations, blogs and web pages. The International Data Corporation (IDC) predicts that about 80% of all data worldwide will be unstructured by 2025, and many large companies have already crossed this threshold. Unstructured data can be difficult to understand, but with the help of smart technologies, it can be analysed to extract valuable insights.

The rise of AI and automation

Technological advances, especially in artificial intelligence (AI) and automation, have changed the way we understand and use data. Large language models (LLM) powered by AI can understand and generate text, and are particularly good at processing unstructured data. This has led to smoother integration of structured and unstructured data in knowledge work processes.

Navigating through unstructured data with Generative AI

Using generative AI for unstructured data offers both benefits and challenges. Benefits include improved understanding of nuanced information, extraction of meaningful insights, automation of repetitive tasks and improved accuracy in, for example, health diagnoses. Challenges include increased costs, complexity in training, the need for specialised expertise and ethical considerations such as addressing potential biases.

Knowledge work and innovation in the digital age

Advances in technology make it possible to combine structured and unstructured data effectively in work processes. Digital transformation enables knowledge workers to use AI to analyse unstructured data, shaping the future of automation and competitive advantage. While structured data remains essential for clear communication, unstructured data opens up new opportunities for creativity and innovation. Understanding both types of data and their interplay is crucial for modern knowledge workers.

And that is exactly what M-Files is good at: bringing unstructured and structured data together for more insights, faster and more efficient work and optimal knowledge management. Curious what that looks like? Schedule an online demo and find out at a time that suits you.
Knowledge files
Read also

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