27.04.2021

Data Science

Boostrs

Going back

Structured and unstructured data: what is it?

HR data analysis is a strategic topic for most organizations. However, this exercise can be complex for them because the form in which they collect and store it is not always adapted. Let’s zoom in on what structured and unstructured data are, their differences, as well as their advantages and disadvantages. 

 

 

Structured data and unstructured data: what are the differences?

 

In order to be able to analyze and exploit HR data in a relevant way, it is important to know in which form it is collected or stored.

 

Unstructured Data

 

Unstructured data is the data most frequently encountered within organizations. It is information stored in its original format, without applying any special processing to it. This data usually consists of textual documents (job descriptions in various formats, for example).

 

 Structured Data

 

Structured data corresponds to information formatted according to a predefined structure, allowing it to be organized together and analyzed. It can be numerical data or textual data (lists of standardized occupations and skills, for example).

 

Related: The challenges of HR data for companies

 

 

What are the advantages and disadvantages of these data?

 

As is often the case there are advantages and disadvantages to both structured and unstructured data.

 

The advantages of unstructured data

 

The main advantage of unstructured data is that it can be collected quickly, since it does not follow a specific format. Moreover, since its format is not fixed, it is going to be possible to format it according to the needs and thus multiply the uses of this data.

 

→ The disadvantages of unstructured data

 

The main disadvantage of unstructured data is that it cannot be exploited as is, since it is generally heterogeneous. Therefore, they require the use of data science specialists and/or dedicated tools to structure and analyze them.

 

Good to know:

Data science is a scientific discipline that uses techniques and models from the fields of mathematics and computer science to extract and “make sense” of structured and unstructured data.

 

 The advantages of structured data

 

As for structured data, its advantage is that it is easily exploitable by artificial intelligence algorithms. The structure they follow allows machine learning tools to identify and process information quickly.

 

→ The disadvantages of structured data

 

The main disadvantage of structured data for organizations is that it requires establishing and following specific writing format structures. Depending on their needs, choosing which fields to fill in can be time-consuming and resource-intensive. In addition, the definition of these fields is sometimes imprecise, which leads to heterogeneity in the filling of these fields if several people fill them.

 

 

Today, data structuring can be automated thanks to algorithms. Based on text recognition and matching technologies using the latest forms of artificial intelligence (NLP, neural networks), these algorithms are able to clean and structure the data of organizations. They represent a valuable time-saver for HR teams, allowing them to focus on the areas where they can add the most value.

 

Good to know:

The standardization of textual data, therefore of most HR data, consists of aligning the data to the same “language”. For example, the same job may have several names depending on the organization. Standardizing the names by replacing them with the job they represent makes it easier to compare them because they are transcribed into the same “language”.

Find out more about jobs and skills data:

Discover Boostrs’ Jobs and Skills Library APIs

Illustration credits: https://www.istockphoto.com/fr/portfolio/irina_shatilova