When you think about your business, you’re likely to think of the types of data that exist today, including structured and unstructured data. Typically, structured data is stored in relational database management systems. They can be written in a language called Structured Query Language, initially developed by IBM in the 1970s. Now called Oracle, this language is used for database queries. Unstructured data, on the other hand, can be anything from text to audio, image, and video.
Unstructured data is a valuable source of information
Email, for example, is one example of unstructured data. While there is a general email format, there are no predefined categories. Unstructured data analysis tools can sort through email text using metadata and then classify them by topic. Social media data, too, is unstructured. NoSQL databases are popular for storing and searching this type of data.
Surveying is another example where unstructured data can be valuable. For example, surveyors can combine measurements with topographical or satellite imagery to estimate distances to find landforms. Likewise, accounting or inventory management records are not unstructured data. Each record contains several adjacent data fields, each with a unique access point. With the increased unstructured data, leaders can make informed decisions and respond to unexpected trends and changes in real time.
The information contained in unstructured data is invaluable for analytics and business intelligence. Whether it’s social media, surveys, or customer analytics, unstructured data is a valuable source of information. The insights it provides can help businesses answer essential questions and identify industry gaps. And if you’re looking to improve customer service or corporate brands, unstructured data is a valuable source of information. The benefits are many.
It requires expertise in data science
When connecting unstructured data to structured data, you need to know how to place and analyze it properly. While the information in these types of data may be readily available, regular users cannot extract any meaningful information. This requires expertise in data science, as normal users lack the knowledge and experience to understand how to link and process the data correctly. Unfortunately, due to the growing demand for such expertise in various industries, there is a shortage of professionals in this area.
While most businesses are comfortable with structured data, unstructured data has some challenges. First, unstructured data is not structured by definition. For example, data generated from social media applications differs from that caused by point-of-sale systems. Because it is undeveloped, a data scientist must specialize in data science tools to connect this type of data to structured data.
It is hard to store
If you think about the vast amount of data you collect, you’ve likely come across unstructured data. This type of information is hard to store and process, and it cannot be stored in the traditional column-and-row database format. In addition, this type of information is typically text-based and challenging to analyze. Examples of unstructured data include social media posts, email content, and miscellaneous documents. For example, you may collect unstructured data from customers through email communications. Similarly, you may collect unstructured data through phone calls, presentations, and miscellaneous documents.
While structured data is much easier to store and organize, unstructured data is often more challenging to manipulate and analyze. It also doesn’t fit neatly into predefined fields. As a result, finding valuable insights in unstructured data is difficult, and the average business user cannot handle it. In addition, unstructured data is often stored in databases, such as data lakes, specifically designed for this type of data. As a result, unstructured data is expensive and difficult to manage.
It requires a lot of storage space
The number of data sources continues to grow exponentially. Today, unstructured data accounts for a majority of data and is the largest in size. In recent years, data scientists have begun searching for tools that can help them manage this vast data. Automation and AI technologies are helping data scientists develop solutions for this problem. With unstructured data, companies can understand and use their data better, allowing them to improve business processes and increase customer service.
While there are many types of unstructured data, the text is the most popular. Unstructured text can come in many forms, including email, surveys, calls, social media, and medical research findings. Another growing category of unstructured data is machine data, which is rapidly increasing in volume. Companies are capturing data from sensors on manufacturing equipment and IoT-connected devices.