Unstructured data is a large and growing part of today's businesses. It's found in text documents, emails, social media, images, video, audio files, and IoT devices. It can be analyzed with machine learning, natural language processing, and other AI technologies to help companies better understand their customers.
How Can Unstructured Data Be Stored?
Unstructured data has no defined structure and cannot be stored in a database like a relational database management system (RDBMS). This lack of a recognizable structure means that unstructured data is difficult to index, search, or manipulate. It also takes up a lot of storage space, making it more expensive to store and maintain than structured data.
How Can Unstructured Data be Used?
Unstructured data can be analyzed with machine learning and other AI technologies to help companies better understand how they're performing. The insights that can be gained from these data sets are powerful and can drive better business outcomes. You can read here more info to know more about Unstructured Data.
The most common types of unstructured data are email, social media, and IoT logs. However, there are many other sources of unstructured data, including rich media files (audio and video), digital photographs, sensor and IoT data, and more.
Why Is Unstructured Data So Valuable?
Unstructured data has a wide range of uses, from customer communication to research and compliance. Whether it's a survey about your products, a complaint about your service, or an order for a product that has been delivered, this information can provide valuable insight into your customers and their experiences with your brand.
Having this data can also help you stay compliant with regulations, such as those related to the FDA and HIPAA. For example, it's important for healthcare companies to be able to track all of their patient's medical conditions and medications so that they can keep their records up-to-date.
How Can I Analyze This Data?
Using unstructured data to gain valuable insights is possible, but it requires a lot of work. It typically requires several rounds of data wrangling and parsing, often using machine learning algorithms to extract the most value from the data. Click here for more info to know how unstructured data is stored.
It's also hard to make sense of unstructured data without specialized skills, such as knowledge of data cleaning and natural language processing. Depending on the type of data, a data scientist may also need to be familiar with computer vision and other technologies that can help extract meaning from the data.
The best way to use unstructured data is to first define a clear set of business goals for the information that you will be collecting and using. This will help you decide what kinds of data you need to collect and analyze, and where it can be found.
There are a variety of ways to analyze unstructured data, from simple quantitative analysis to more advanced deep learning and natural language processing techniques. Some of these techniques are useful for detecting a burglary in a gated community, but others can provide insights into how a customer feels about a specific product or service. For a general overview of this topic, click here: https://en.wikipedia.org/wiki/Unstructured_data.
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