Structured vs. unstructured data: A simple guide

Data grows bigger every year.

As a result, how that data is collected, stored, and managed can greatly affect its efficacy in different use cases. Whether data is stored in a relational database or through some other method can have a long-lasting impact on how useful and relevant that data is.

The two main types of data are unstructured and structured. Let’s take a look at each type, as well as structured and unstructured data examples.

What is structured data?

Structured data is primarily housed in a relational database, abbreviated as RDBMS, and is considered quantitative data. This means that when that data is collected, form fields are attached, identifying each piece of data. Things like phone numbers and zip codes are forms of structured data. However, it can also be composed of strings of text as well, like names.

When is structured data used?

Structured data is most often used when information is easily searchable. There are set variables to search from like zip codes or addresses. Some use cases include airline reservation systems and ATMs. For many businesses, however, structured data is most helpful when using customer relationship managers (CRMs) like Salesforce or HubSpot. These systems are set up to make it easy to store client data and make that data easily searchable so that pulling reports or further examining the data isn’t a chore.

What is unstructured data?

Unstructured data primarily can’t be searched or analyzed like structured data can. People often refer to unstructured data as qualitative data. It is not housed in a relational database, so querying unstructured data will return results that must be analyzed and interpreted by an expert.

To put it another way, structured data is facts and figures: zip codes, phone numbers, names, addresses. It is made up of numbers and stored by using well-defined fields so that the information can be searched easily with just a few keystrokes.

Unstructured data is just about anything else. It consists of text, audio and video files, and even mobile activity or Internet-of-Things data.

When is unstructured data used?

The application of unstructured data is most helpful when doing data mining or any other analysis that requires knowing more about the customer’s behaviors, buying habits, et cetera. It can also be used for predictive analysis.

This allows you to review data you’ve previously collected and extrapolate a hypothesis about future behaviors, enabling you and your brand to plan ahead for shifts in the market.

Benefits of structured vs. unstructured data

Structured and unstructured data don’t have an antagonistic relationship, as they both provide different information that is equally valuable depending on your goals—in other words, they both have benefits. But the differences between them can be stark, so it’s best to know the pros and cons of each so you can better understand their use cases. This will prevent you and your team from wasting time searching one set of data when you really need to search the other.

Structured data: Pros

• Accessible via machine-learning algorithms: Structured data allows you to use established search queries to find the information you’re looking for. You can easily set up search parameters and leave the database to pull the information you want. The report you get back will be readable with very little analysis.
• Easily understood by end users in a variety of areas: Because the information is stored with easily identifiable field names and subjects, the data can be interpreted by just about anyone. All you really need is context to understand what the data relates to. This makes it very easy for everyone to use, including those in marketing, advertising, and other business-related fields.
• Multi-access points and programs: Structured data can be accessed by many different programs because it is so organized. There are many tools readily available to analyze and pull structured data.

Structured data: Cons

• Limited use: Structured data is strictly stored in a limited set of fields and can only be used when that type of data is needed.
• Limited storage: Since structured data must be housed in databases, it requires a lot of “space.” It also can result in expenses down the road if the information or storage security must be updated to meet new standards.

Unstructured data: Pros

• Open interpretation: Unstructured data is stored in its native format; it is not translated into an easy-to-read format for a database. As a result, the data can be interpreted across a variety of applications and for a variety of use cases.
• Easy collection: Since there are no set parameters with unstructured data, you can collect quite a bit of it at a pretty fast rate.
• Data storage: Most unstructured data is stored in a pay-as-you-go type of system, which makes it easy to scale as your repository of data grows.

Unstructured data: Cons

• Less accessible: Because it is undefined, the data is open to interpretation. It is also not so easy to read and often requires the application of data science. This makes it less accessible than structured data to laypeople, such as those in business or marketing/advertising since it must first be interpreted before being shared.
• Fewer analysis tools: There are far fewer tools that can analyze unstructured data than structured.

The future of data is structured . . . and unstructured

Data is multifaceted and can provide us with a wealth of information. Because the very nature of structured and unstructured data means they store separate types of information, your business will no doubt need access and analysis of both to forecast expertly.