The global data volume is growing at a dizzying level; however, there are multiple variables, models, sources, and formats that contribute to this growth. The nature of these is not homogeneous.

In this context, a significant problem emerges regarding unstructured data, which is not organized in a predefined or standardized way.

This phenomenon presents many challenges for managing, analyzing, and interpreting information.

It is interesting to examine the problem of unstructured data, analyzing its causes, implications, and some possible solutions

What is unstructured data and what problems does it involve?

Unstructured data refers to information that is not organized according to a predefined pattern or structure. This includes free text, unlabeled images, audio data, and more. While structured data, such as that found in relational databases, organizes information into easily interpretable columns and rows. Unstructured data lacks of standardized organization.

The causes of the problem are many and can come from different sources:

  • Historical archives: over time, many documents have been archived in ways that are now obsolete and therefore difficult to consult.
  • Spontaneous generation: unstructured data can be generated spontaneously by users or systems without following a specific standard. For example, text notes and scanned documents are often unstructured information.
  • Legacy systems: in many organizations, data can be generated from legacy systems that do not follow modern standards of data organization.
  • IoT sensors and devices: devices such as IoT sensors can generate data in unstructured formats, creating challenges in integrating with more traditional systems.

The problem of unstructured data has negative implications for companies, PA and especially for Data Scientists. In particular, there are three main problems:

  1. Difficulty in analysis: the absence of a predefined structure makes it difficult to analyse unstructured data, limiting the ability to gain meaningful insights.
  2. Risk of Information Loss: without a clear structure, data can contain important information that is at risk of being lost or misinterpreted.
  3. Complexity in integration: integrating unstructured data with mechanical systems can be a complex task, requiring significant efforts to normalize and structure this information.

Innovative solution with AI

Tackling the problem of unstructured data requires the adoption of advanced technological solutions.  Machine Learning (ML) and Artificial Intelligence (AI) are critical tools for extracting meaning from unstructured information.

AI can be trained to recognize patterns and relationships in information, allowing for a better understanding of context and greater accuracy in analytics. For example, natural language processing (NLP) algorithms can search free text, while neural networks can analyse unlabeled images or audio.

The problem of unstructured data is an increasingly important challenge in the field of data management. Our goal is to address this issue with technologically advanced and high-performance tools.

At Drive2Data we develop intelligent visual recognition solutions that allow you to carry out investigations, searches and operations on analog archives and extract information from a multitude of heterogeneous documents.


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