4 Trends You May Have Missed About Structured Vs. Unstructured Data
Whether you are looking for the benefits of structured or unstructured data, there are some key differences between the two types. These differences include cost, compliance, and security. So let’s take a closer look. In the first part of this article, we’ll discuss how the two types differ.
Structured vs unstructured data differ greatly in format, structure, and accessibility. While structured data can be easily organized, unstructured data is typically more diverse and cannot be easily analyzed conventionally. As a result, both types require different approaches and tools for management and evaluation. In this article, we’ll discuss some of the differences between the two data types and which is better for your business.
Unstructured data is often stored in raw form on a personal thumb drive, on a local server, or in a data lake. It cannot be easy to organize, index, or analyze without specialized tools. It also poses a compliance risk and can cost a lot more to store.
In comparison, structured data has a predefined data model. This structure helps it be categorized properly. Typically, unstructured data is text-heavy but can also include images, audio, and video data. Unstructured data is not as easy to understand as structured data, and traditional software cannot effectively handle it. Examples of unstructured data include social media posts, audio and video files, and open-ended survey responses.
There are a few important differences between structured and unstructured data. For example, structured data contains sensitive information such as names, addresses, and phone numbers, but unstructured data can be stored in any format, regardless of how it’s used. For example, email and social media posts are usually unstructured and cannot be parsed by traditional analytics tools. Unstructured data includes social media posts, chats, emails, and data from social media and sensors.
There are some advantages to unstructured data, including its analytical value. Employees can use unstructured data to store and share information on the go, but the data owner should be responsible for its security. Unstructured data is often stored on personal computers or cloud accounts, so security is a key concern.
Data is the lifeblood of any organization. And it comes in all forms – from relational databases to Facebook posts. As a result, organizations must understand the differences between these data types to use them effectively.
Organizations must know the difference between structured and unstructured data to comply with legal and privacy requirements. Unstructured data has a high volume but little organization. To manage it, organizations can use an Emergent Software. In addition, visualization of unstructured data can identify potential compliance issues. But there is more to data management than just compliance.
Unstructured data includes social media posts, email content, and video. Unlike structured data, unstructured information is often observed and is not stored in a database. This type of data contains a variety of topics and can include thousands of words. Typically structured data tools don’t have the tools necessary to parse documents.
Compliance with unstructured data means storing, accessing, and sharing data correctly. Organizations can reduce the cost of breaches and damage reputational damage associated with data breaches by ensuring they use compliant data. According to an IBM report, the average cost of a data breach in a hybrid cloud environment was $3.61 million, and the most significant cause was failed compliance. In addition, regulatory fines for non-compliance are increasing every year.
Aside from the cost, another issue that can be problematic for businesses is regulatory compliance. Unstructured data can present problems for privacy and industry regulations, such as HIPAA. Using unstructured data can be expensive and may result in missed opportunities. In addition, unstructured data can cause problems with communication with customers.
Data warehouses are an excellent example of structured data. These databases are central information repositories and are used for analysis and reporting. These data warehouses require strict schemas, and data must be updated regularly. Using structured data also limits its use since it’s predefined for a specific purpose.
Unstructured data is difficult to process, and traditional databases aren’t designed to handle it. For example, XML, key-value, and JSON databases do not perform well with unstructured data. As a result, unstructured data processing is often offloaded to a secondary database. However, this requires extra space, which costs money. Consequently, some companies choose to avoid dealing with unstructured data altogether. Others expand their primary storage system to accommodate unstructured data. Either way, unstructured data management is expensive.