What is Unstructured Data? – New Stack

This is the first in a three-part series.

Our world is constantly evolving digitally, with data growing exponentially every second. The rise of AI technology will only accelerate this process. However, not all data is created equal. A staggering 80% of newly generated data is unstructured. This percentage is expected to increase as the industry advances and technology develops. Most importantly, unstructured data is plentiful and a valuable source of rich information that can provide insight for informed business decisions.

So what exactly is unstructured data, and how does it differ from structured and semi-structured data? How can unstructured data be effectively processed, analyzed and searched? This blog explores the complexity of unstructured data and explains how to process, analyze, and query it.

Structured Data vs. Unstructured Data vs. Semi-Structured Data

Let’s start by learning about different data types: structured, semi-structured, and unstructured.

structured data

Structured data follows a specific format and can be easily stored and analyzed using traditional data management tools such as SQL. Examples of structured data include customer information, transaction records, and inventory lists.

semi-structured data

Semi-structured or partially structured data is a mixture of structured and unstructured data. It contains some level of organization, such as metadata and tags, but it’s not completely structured. Semi-structured data is commonly found in XML files, JSON documents, and other data types that follow a specific schema. This type of data cannot be stored directly in a relational database, so it is usually stored in his NoSQL databases such as wide column stores or object/document databases.

unstructured data

Unstructured data refers to data that does not have a specific format or structure. This data type is often created by humans in the form of text, images, videos, emails, social media posts, etc. However, unstructured data can also include less common examples such as protein structures, executable hashes, human-readable code, etc. The possibilities are endless.

Here are some concrete examples of unstructured data, both machine-generated and human-generated:

• Sensor data: Data collected from various sensors such as temperature, humidity, GPS and motion sensors.
• Machine log data: Data generated by a machine, device, or application (such as system logs, application logs, event logs).
• Internet of Things (IoT) data: Data collected from smart devices such as smart thermostats, home assistants, and wearable devices.
• Computer vision data: Data generated by computer vision technologies such as image recognition, object detection, and video analytics.
• Natural Language Processing (NLP) data: Data generated by NLP technologies such as speech recognition, language translation, and sentiment analysis.
• Web and application data: Data generated by web servers, web applications, and mobile applications, such as user behavior data, error logs, and application performance data.
• e-mail: Email messages typically contain unstructured text, images, and attachments.
• Text message: Text messages are informal, unstructured, and may contain abbreviations and emojis.
• Social media posts: Social media posts vary in structure and content, including text, images, videos, and hashtags.
• Voice recording: Human-generated voice recordings include phone calls, voicemails, audio files, voice memos, etc. These are considered unstructured data.
• Handwritten notes: Handwritten notes may be unstructured and may contain pictures, diagrams and other visual elements.
• Meeting notes: Meeting notes can contain unstructured text, pictures, and action items.
• Transcript: Speech, interview, and conference transcripts may contain unstructured text with varying degrees of accuracy.
• User Generated Content: User-generated content on websites and forums may be unstructured data such as free-form text, images, and video files.

Analyzing unstructured data is difficult

Working with unstructured data can be difficult due to the lack of standardized formats. Moreover, when it comes to querying and analyzing data, things get even more complicated, especially when compared to structured and semi-structured data.

When working with structured or semi-structured data, it’s easy to search or filter for specific items in your database. For example, to get the first book from a specific author in MongoDB, you can use the following code snippet ( pymongo).

This query method is similar to traditional relational databases that filter and retrieve data through SQL statements. The basic idea is the same. Databases built for structured or semi-structured data are mathematical (e.g. <=, string distance) or logical operators over numbers and strings (EQUALS, NOT). For traditional relational databases, this is called relational algebra. So it always returns an exact match for the set of filters provided.

However, traditional relational databases and data management tools cannot handle the complexity of unstructured data analysis. For example, if a user wants to find similar shoes based on a collection of photos of shoes taken from different angles, a relational database understands the nuances of shoe style, size, color, etc. based only on raw pixels. I can not do it. the value of those images. This poses significant challenges for industries and companies using unstructured data. How can unstructured data be transformed, stored and similarly retrieved for structured/semi-structured data?

How to search and analyze unstructured data

Specialized software and techniques such as machine learning, and more specifically deep learning, are used to address the challenges of analyzing and retrieving unstructured data. Machine learning is an artificial intelligence technique that enables computers to learn from unstructured data without being explicitly programmed. Most machine learning models convert a single piece of unstructured data into a list of floating point values ​​(commonly known as embeddings or embedding vectors) before searching and analyzing the data to gain insights. To do.

For example, a good ResNet-50 convolutional neural network can represent the image below as a vector of length 2048. The first three and last three elements of this vector are: [0.1392, 0.3572, 0.1988, …, 0.2888, 0.6611, 0.2909].

Embeddings produced by a well-trained neural network have mathematical properties that make them easy to search and analyze. For example, embedding vectors of semantically similar objects are close to each other in terms of distance. As a result, vector math enables you to understand, search, and analyze unstructured data.

Why should I work with unstructured data?

Dealing with unstructured data can be challenging, but it’s still valuable for developers and businesses. Unstructured data makes up a whopping 80% of both existing and newly generated data, especially in the age of AI. It contains a wealth of information that can provide valuable insight into customer behavior, market trends, and other key business metrics needed for more accurate decision making. Advances in technologies such as natural language processing and deep learning will make managing unstructured data easier over time.

Additionally, working with unstructured data can help uncover hidden patterns and relationships that are difficult to detect using traditional methods. Working with unstructured data also leads to innovation and product development. We have already seen groundbreaking applications, services, and products emerge that extract value from unstructured data using large-scale language models (LLMs) such as OpenAI’s ChatGPT. rice field. There will be more in the future.

summary

This post explained the meaning and instances of unstructured data. We also explored the difficulties and techniques involved in processing and analyzing unstructured data to make informed business choices.

In future posts, we will delve deeper into vector databases, a simple yet effective solution for storing, indexing, and retrieving unstructured data using the power of embeddings generated by machine learning models. It also introduces Milvus, a highly scalable and effective open source vector database, and details how Milvus can power your AI-powered applications. Please stay tuned for further details.