In the world of machine learning, data is everything. The algorithms used in machine learning require data to be fed into them in order to learn and make predictions. However, data comes in many different forms and therefore we can categorize them into two main types: structured and unstructured data.
“Structured data is data that is organized in a specific format, such as a table with rows and columns so that it’s easy to read and analyze. Some examples of structured data are spreadsheets, information within a database.”
Structured data needs to be preprocessed, filtered (and normalized) to ensure that it meets the standards of the machine learning algorithm being built out. Working towards an objective of identifying patterns and trends in the data, structured data rapidly helps achieve this because of the format the information is organized in.
“Unstructured data on the other hand is data that has no specific format. This type of data is much more difficult to analyze. Some examples of unstructured data are images, audio files, social media posts.”
Two key areas where unstructured data is increasingly being used in machine learning are for natural language processing (NLP) and image recognition tools. Ultimately unstructured data needs to be transformed into a structured format, such as a bag of words for text documents or a matrix of pixels for images to understand the entire content.
Although this process takes longer and requires thorough understanding of the original content, unstructured data can provide insights that structured data cannot. For example, social media posts can provide valuable insights into customer sentiment and feedback that may not be captured in structured data.
Both structured and unstructured data are valuable for machine learning. As machine learning continues to evolve in the future, structured and unstructured data will continue to play a role in providing insights and predictions as different use cases emerge within industry.