Structured vs Unstructured Data

You’ve probably heard data be described as either structured or unstructured before, but you may not know exactly what those terms mean. It’s important to understand the distinction, as each type of data brings its own unique benefits and drawbacks. Read on to learn more!

What is Structured Data

Structured data is data that is located in a file or record in a fixed field. It usually resides in a relational database, and it could consist of numbers, text, or a combination of the two.

Sourcing this data could be a manual or automatic process within the structure of a relational database. It just depends on the data model that is created: what data is included as well as how it is stored and processed.

Structured data uses SQL, Structured Query Language. This language can be traced back to the 1970s, when it was developed by IBM.

Benefits of Structured Data

Structured data lives up to its name: it lives in an organised way, in rows and columns, and this makes it easy to map it onto pre-defined fields. As structured data is often quantitative, consisting of numbers or quantifiable things, it is easy to search through whether you’re a human or an algorithm!

That means it can be used by business owners. You don’t have to be a data specialist to understand structured data. As long as you have a basic understanding of what it refers to, it’s straightforward both to access and to interpret.

Cons of Structured Data

As previously stated, structured data tends to consist of numbers or letters. It has limited scope for usage, then. This data, with its predefined structure, can only really be used for its initial, intended purpose. It’s not particularly flexible, and this hinders its usability.

There are also limited storage options when it comes to structured data. It’s usually found in data warehouses, structures with rigid schemas. This type of storage solution doesn’t lend itself to quick, frequent changes. Every time you need to change data requirements, you’ll spend time and money updating all the structured data that the warehouse contains.

Structured Data Examples

What might you expect from a database full of structured data? It could contain names, addresses, credit card numbers, dates… All kinds of concrete information that can be directly compared and counted.

In a business environment, the customer relationship team might use this data to come to conclusions about customer behaviour patterns. For example, they might discover that a particular demographic is more likely to make a purchase on a particular day of the week. This knowledge could inform future marketing campaigns.

It can also be used within online booking systems. The rows and columns of structured data are a perfect match for the dates, prices, and destinations that this system revolves around.

Of course, structured data is an accountant’s best friend. Financial transactions can be processed and recorded this way.

What is Unstructured Data

We’ve already explained what structured data is. Unstructured data is more or less everything else! For that reason, there’s a lot more of it than structured data. Industry estimates are around 80% of enterprise data can be categorised as unstructured.

Unstructured data isn’t predefined in its purpose like structured data is, but there’s so much of it that any business failing to work with their unstructured data is missing a huge opportunity. This data can be stored, processed, and analysed to obtain useful business intelligence.

Benefits of Unstructured Data

Unstructured data is stored in its native format. Its purpose isn’t defined until the owner decides to define it, meaning it’s extremely adaptable. Data scientists can extract, prepare, and analyse the parts that they need according to the needs of the business.

The data isn’t predefined, so it can be accumulated very quickly with minimal interference. It can be stored in a data lake with a “pay-as-you-use” pricing strategy, which can help to reduce business costs. Data lakes can store incredible amounts of data, so unstructured data is more scaleable.

Cons of Unstructured Data

Unlike structured data, unstructured data is more difficult to access. It isn’t organised in a way that makes it easily understood, processed, and analysed. Conventional tools and methods don’t always apply because unstructured data doesn’t have the same predefined data model as structured data does.

What does this mean for you? That you’ll likely need a data scientist (or someone with technical know how to query the data) to handle your unstructured data. They’ll apply advanced analytics techniques to transform it into useful information. It’s difficult to search and without processing, impossible to understand. It can’t be mapped onto relational databases in the same way as structured data can.

Tools to mine, arrange, and analyse unstructured data remain in development, but it’s a tricky field. Given the diverse data sources involved, it’s hard to find one method that applies across the board.

Unstructured Data Examples

Unstructured data has a wide range of sources. It could consist of text, social media activity, or even surveillance imagery! Your business probably generates unstructured data through social media conversations, surveys, and interviews with customers and staff members.

Businesses often use unstructured data to come to conclusions about customer behaviour and purchasing trends. They can also use it to make predictions about upcoming changes in the market.

Many online retailers use technology like chatbots to handle customer questions – these conversations become a source of unstructured data. This shows how analysing unstructured data can help businesses to direct customers to the appropriate answer for their query.

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