What you need to know about structured vs unstructured data.
Data sourcing for business insights is crucial in today’s market. However, it’s important to know where to start to be most effective. For example, structured data and unstructured data are terms we hear a lot in the tech industry, but what are they and how can they help your business?
What is structured data
Structured data is web data in its ‘cleanest’ form. In structured datasets there are no extra copies or corrupt files because they have already been collected, indexed and structured in an identical format such as JSON, CSV, HTML, or Microsoft Excel. From here the data can be analyzed easily by systems and algorithms for high-level insights. Examples of structured data include publicly available information such as stock data, social media information or any website listing their product information and pricing.
Advantages of structured data
The main advantage of structured data is that it is a comprehensive set of data that also includes historical data. Fewer resources are required to collect and use it. When businesses collect and make use of data, structured data is often the preferred option because it is less time consuming to collect and overall, more efficient in the sense that structured data can be quickly analysed, considering it doesn’t require any further processing.
Disadvantages of structured data
The main disadvantage in making use of structured data is that it does not include real-time data. This is not suitable for enterprises that are looking to prioritise speed of information in their decision-making processes. Secondly, structured data has limited storage. Structured data has ‘fixed schema’ and shifts in needs can cause companies to waste time and efforts on matching up data warehouse compatibility.
What is unstructured data?
Unstructured data is collected through web scraping techniques. It contains information in a range of different formats, entries appear repeatedly throughout a given dataset and can contain corrupt files. This data needs to go through a complex ‘cleaning’/’formatting’ procedure before it can be saved, analysed and shared with teams or fed to algorithms. Examples of unstructured data include text files, reports, and audio/video files. Typical applications include word processing and tools for
editing media.
The main advantage of unstructured data is that it can be collected in real-time. This means it is available for collection as soon as it is created, which allows businesses to react fast to opportunities or any potential issues in operations. Another advantage is that unstructured datasets are flexible because they come in a variety of formats which can cater to the different needs of a business when switching between applications.
Structured vs. unstructured data – the main differences
Here are some of the main differences between the two types of data sets:
- Structured datasets have a single format, whereas unstructured datasets come in various formats.
- Structured data typically resides in data warehouses, whereas unstructured data is commonly saved in data lakes.
- Structured data can be used by anyone, regardless of technical backgrounds unlike unstructured data which requires data specialists
- As there are a range of options available, it’s important for businesses to do their research beforehand – whether it be structured or unstructured – to ensure that they choose the best option for them and achieve their business goals.
