Unstructured data does not have a pre-defined format or structure and is not organised in a predefined manner. It is considered unorganised which makes it difficult to use traditional data analysis methods to extract insights from it. Examples of unstructured data include social media posting, text documents, emails, videos, audio files and images . This of data often requires specialised techniques, such as natural language processing, computer vision, and machine learning, to be analysed effectively.
Unstructured data vs structured data
Structured data refers to data that is organised in a pre-defined manner. It can be presented in tables or spreadsheets with clearly defined fields and data types. This can include databases, CSV files and Excel spreadsheets. This type of data is easy to sort, search and analyse and is often stored in a database.
Unstructured data however, does not have a pre-defined data model and isn’t organised in a predefined manner. It is often unorganised and difficult to analyse using traditional methods, but can still contain valuable insights when properly processed and analysed. Examples of this type of data include text documents, emails, social media posts, images, and videos.
What is unstructured data used for ?
Unstructured data is used for a variety of purposes. One common use is for text mining and natural language processing. Social media posts, customer reviews, and emails, can be analysed using text mining and natural language processing techniques to extract insights and sentiment.
Data forms such as images and videos can be analysed to extract information, such as object recognition and facial recognition. Furthermore, business intelligence data can be used to gain insights into customer behaviour, market trends, and other business-related information.
Fraud detection data detects fraud in areas such as financial transactions, insurance claims, and healthcare billing. Social media posts can be analysed to extract insights into public opinion and brand reputation. Emails can be analysed to extract insights from large volumes of data, such as identifying patterns, topics, and relationships between people.
Another use of unstructured data would be for Customer Service. Emails, chats, and call logs can be analysed to improve customer service operations. Medical research is another area where this data can be used. insights can be extracted from medical research papers and scientific literature.
While these are a few examples, there are many other ways unstructured data can be used, and this is an area where many innovations are being made.
How is Unstructured data stored ?
Unstructured data is stored in a variety of ways, including file systems, object storage, cloud-based services, noSQL databases, search engines and data lakes.
Text documents, images, and videos, can be stored on a file system, such as a local hard drive or a network-attached storage (NAS) device. Object storage systems, including Amazon S3, Google Cloud Storage, and Microsoft Azure Blob Storage, are designed to store large amounts of unstructured data. This includes data such as images and videos, in a scalable and cost-effective manner.
Cloud-based services, such as Google Drive and Dropbox, allow users to store and share unstructured data in the cloud. NoSQL databases, such as MongoDB, Cassandra, and Hbase, are designed to store and manage unstructured data, such as JSON and XML documents.
Search engines, including Elasticsearch, Solr, and Algolia, can be used to index and search unstructured data, such as text documents, in real-time. A data lake is a centralised repository that allows you to store all your structured and unstructured data at any scale. You can store data in its raw format, without the need for defined schemas, and then perform transformations and extract insights from it later on.
The storage method used will depend on the specific dataset, the scalability needs, and security constraints. Some companies use a combination of these storage methods to store unstructured data.
How DataBench can help you analyse your data ?
DataBench utilises several methods for analysing your unstructured data including text mining and natural language processing, machine learning, data visualisation, data cleaning and preparation, data integration, text extraction and regular expression.
Text mining and natural language processing techniques, such as sentiment analysis, topic modelling, and text summarisation, extract insights from unstructured data in the form of text, such as social media posts, customer reviews, and emails. Machine learning algorithms, including clustering, classification, and neural networks are used to analyse this data. For example, image and video data are analysed using convolutional neural networks for object recognition and facial recognition.
Data visualisation strategies such as graphs, charts, and maps are used to present the insights from this data in a clear and easy-to-understand format. Data often requires cleaning and preparation before it can be analysed. This involve removing duplicates, correcting errors, and standardising data formats.
Unstructured data may need to be integrated with other data sources, such as structured data, to gain a complete understanding of the information. Text extraction identifies and extracts structured information from unstructured data such as documents, images, and PDFs. Regular expressions are a powerful way to search, match, and extract text patterns from this type of data.
The process used will depend on the specific dataset and the insights desired. It often requires the integration of multiple techniques and methods, and the use of specialised tools such as natural language processing libraries and machine learning frameworks.
