What is Unstructured Data?
In today's digital age, organizations generate essential data from various sources, such as customer interactions, social media activity, online transactions, and sensor and data analytics. This data is classified as structured and unstructured data. Structured data refers to data that is organized in a predefined manner and can be easily searched and analyzed. On the other hand, unstructured data does not have a predefined format or schema and is not easy to search or analyze.
Examples of Unstructured Data
Unstructured data comes in various formats: text, images, audio and video files, social media posts, and sensor data. This data is typically unorganized and needs a specific structure or schema, making it more challenging to analyze. Despite these challenges, unstructured data plays a crucial role in business operations. Organizations collect this data to gain insights, get business intelligence, make informed decisions, and improve business processes. For example, customer feedback gathered from social media can help organizations improve their products and services, while sensor data can help predict equipment failures and prevent downtime.
Searchability and Ease of Use
Structured data are generally easier to search and utilize, whereas unstructured data requires processing before search and analysis is possible. Analyzing unstructured data enables the creation and analysis of new tools based on particular use cases. These programs generally use machine-learning techniques to learn. Structured data analysis may be using machine intelligence, but the huge volumes manage unstructured data and the variety of unstructured data required it. Some years back researchers were able to use keyword search tools in data search and find some basic information about data. E-discovery was one such example. But unstructured data is rapidly growing, requiring analytics that can also learn from user actions.
The Challenge of Analyzing Unstructured Data
However, the challenge lies in analyzing unstructured data effectively. Unfortunately for business users, traditional relational databases and data management tools are not designed to analyze unstructured data. For example, when a user searches for similar shoes given a collection of shoe pictures from various angles, this would be impossible in a relational database since understanding shoe style, size, color, etc., purely from the image’s raw pixel values is impossible. Therefore, specialized software and techniques, such as natural language processing and machine learning, are needed to extract insights from unstructured data.
NLP and ML and unstructured data
Natural language processing (NLP) is a branch of artificial intelligence (AI) that deals with interactions between computers and human language. It enables computers to understand, interpret, and generate human language. NLP techniques analyze unstructured data, such as customer reviews, emails, and social media posts, to gain insights into customer sentiment, preferences, and behavior. Machine learning is another specialized technique that analyzes unstructured data. It is a type of AI that allows computers to learn from unstructured data stored somewhere without being explicitly programmed. Machine learning algorithms are trained on large datasets of unstructured data to identify patterns and make predictions. For example, machine learning classifies images and videos based on their content or predicts equipment failures based on sensor data.
This is where vector databases are helpful. Vector databases help search across images, video, text, and audio files, and other unstructured data via their content rather than keywords or tags (often input manually by users or curators). When combined with powerful machine learning models, vector databases can revolutionize semantic search and recommendation systems. The increasing ubiquity of unstructured data has led to a steady rise in machine learning models trained to understand such data. word2vec, a natural language processing (NLP) algorithm that uses a neural network to learn word associations is a well-known early example. The word2vec model can turn single words (in various languages, not just English) into a list of floating point values or vectors. Due to how models are trained, vectors close to each other represent similar words, hence the term embedding vectors.
This is where vector databases are helpful. Vector databases help search across images, video, text, and audio files, and other unstructured data via their content rather than keywords or tags (often input manually by users or curators). When combined with powerful machine learning models, vector databases can revolutionize semantic search and recommendation systems. In conclusion, unstructured data presents both challenges and opportunities for organizations. While it is more challenging to analyze than structured data, it also contains valuable insights to help organizations make informed decisions and improve their operations. Furthermore, with specialized software and techniques, such as vector databases, natural language processing and machine learning, organizations can unlock the power of unstructured data analytics and gain a competitive edge in today's data-driven world.