Did you know that there are two types of data, divided into structured and unstructured? These two categories are often confuse, so let’s look at these terms and their different uses in text analytics and text mining. Here are the things you didn’t know about these two data types. This information is essential for both text analytics and text mining.
You may not be aware of the differences between structured and unstructured data. Structured data is data that is neatly package and has instructions on how to use it. Unstructured data, on the other hand, is information that is not categorized and cannot be easily added to a spreadsheet. This information may be more beneficial than you think. But, before you get too excited about the differences, consider these points first:
When it comes to data management, there are many different types of data. Unstructured data is any non-traditional data, such as text or other types of content. Examples of unstructured data include social media, email threads, and geographic data. Unstructured data can be challenging to organize, but it can reveal some important nuances about current trends and customer behavior. For example, let’s say you run an online store and collect data about the customers who visit it. Your customers could fall into one of three categories: those who buy from the store, those who don’t buy, and those who don’t.
When analyzing unstructured data, you can find weaknesses and gaps in your organization. However, unstructured data analysis is more difficult because it requires you to work with snapshots and missing information is a common problem. That’s where text analytics comes in. Text analytics is the process of turning unstructured text into structured information. By leveraging techniques from other disciplines, data scientists can make sense of the unstructured text.
You probably don’t know how structured and unstructured data are separated. The former is commonly known as structured data, and it is highly ordered. For example, structured data is a spreadsheet in Google Docs. On the other hand, unstructured data does not have any predefined structure. These data include everything from imagery to text to audio files.
For example, retailers use image recognition to find similar products and services. Manufacturers use advanced text analytics to investigate warranty claims and elicit critical information. Even chatbots use natural language processing to route questions to the appropriate representatives. In short, unstructured data can be challenging to analyze and interpret, but the right tools can help. Learn more about how unstructured data is divide and how to use it effectively.
On the other hand, semi-structured data is mainly text with some metadata. Its message field, however, is unstructured. This makes it difficult for traditional analytics tools to read it. Semi-structured data includes email and social media. These data types can categorized into categories and are easier to analyze than their unstructured counterparts. For example, intent classification can help automated email reading.
You may have heard that email is structure, but what about the unstructured content inside? While email contains metadata, the message field is not structure and cannot parse by traditional analytics tools. However, some ways to analyze unstructured data are by leveraging text analytics. For example, a major magazine publisher used text analytics to analyze hundreds of thousands of articles for popularity. The publisher then extended the analytics to their entire content properties and cross-referenced hot topic results across all their properties. This way, they learned which topics appealed to their customers and which marketing messages resonated with them most.
Structured data is collect from various sources, including GPS sensors, network logs, web server logs, and OLTP systems. Unstructured data is derived from documents, such as word processing and PDF files. Structured data is stored in defined formats, while unstructured information is stored in native formats and processed when used. This data is the source of big data, so the more context we have, our models will be more effective.
Even if you can’t decipher the meaning of your data, you still need to understand its value and use it to make informed business decisions. Unstructured data is often overlook and under-utilized, yet it’s an invaluable source of insight. When analyzed, it provides essential nuances about current trends and customer behavior. For example, a customer on an online store might categorized into three groups: Those who purchase more expensive items, those who spend more on lower-quality items, and those who buy more frequently.