What are Structured and Unstructured Data?

Introduction

In machine learning, the data and its quality are one of the most critical parameters affecting the performance and other parameters while training and deploying the machine learning model. It is assumed that if good-quality data is provided to a poorly performing machine learning algorithm, there is a high chance of better performance than ever from the algorithm and vice versa.

In this article, we will discuss the two common types of data: structured and unstructured data. Here we will discuss their definitions and the core intuition behind them, followed by some other meaningful discussion. Knowledge about these key concepts will help one understand the way of looking at the data, classify it correctly, and take the necessary steps.

Structured Data

Structured data is the type of data that is well-defined, well-structured, and has minimum errors and complexity. The structured data can be identified by looking at it as it is straightforward to understand, a minor complex, and one can quickly analyze it.

One of the best examples of structured data is excel file and google docs. The data with columns and rows are the most used and referred to as structured data. Structured data are beneficial for research work and visualization or analyzing processes.

It is known that the deep study of structured data is a straightforward and efficient process where the programming languages like structured query language or SQL can be used to gain insights from the data and use it efficiently for further work.

Also, in terms of machine learning algorithms, structured data can efficiently feed the machine learning algorithms. Machine learning and deep learning algorithms train faster on such data and perform best out of it.

Some machine learning algorithms are parametric algorithms, which assume certain assumptions or parameters in the data. For example, linear regression takes the data to be linear. In such cases, structured data can help a lot for training on such algorithms, whereas parametric algorithms can also be trained on data and result in better outputs.

The structured data are stored in data warehouses or storages where they can be easily accessed when needed and can directly be fed to the algorithms for training.

The typical example of structured data includes the survey that is performed by individuals very profoundly, the data collected from people very ideally, and some portion of the business data (~20%)

Unstructured Data

Unlike structured data, unstructured data is the type of data that is not well organized and prepared. This type of data is widespread and can be easily found on the internet, and businesses generate it quickly.

This type of data does not include rows or columns; it consists of those that are not well-defined and organized. The unstructured data are complicated t understand and analyze.

Working with this type of data is one of the most complex things ever in machine learning. It is a famous saying by data scientists that if you are working with unstructured data, then ~70% of the model-building time and effort should be given to unstructured data for data cleaning and preprocessing work.

This type of data is supposed not to be a good fit for the research work and some important business insights as, initially, it is unstructured and can lead to wrong assumptions or decisions.

This type of data is stored in data lacking or NO-SQL databases that are not relational.

Examples of unstructured data include surveys performed on larger populations but needed to be handled better or audio and video files.

Semi-Structured Data

There are only two types of data according to the structure of the data: structured and unstructured data, but sometimes there is also a third type of data, semi-structured data.

As the name suggests, semi-structured data is the type of data that is structured and unstructured. The semi-structured data is also 80% unstructured and can include some tags or descriptions about the data, unlike unstructured data. Using the titles or the descriptions of the data can be transformed into structured data sometimes and can benefit us in some ways.

Structured vs. Unstructured Data

Parameter	Structured Data	Unstructured Data
Complexity	Very Low	Very High
Stored in	Data Storages	Data Lacks
Algorithms Performance	Good	Very Poor
Preprocessing Needed	Very Less	A Lot
Robust	High	Less
Organized	Yes	No
Storage Needed	Very Less	Very High

Which to Use and Why to Use?

Gentle questions can come to our minds. Then if there are two or three types of data, which is better, and why use it?

After this discussion, structured data is one of the best fits for machine learning and deep learning algorithms, research works, and gaining data insights by visualizing the data.

But the critical thing to note here is that it is only sometimes valid that structured data is enough and efficient to train the model or the algorithm. Sometimes, only a limited portion of structured data may need more accurate results on the model. In such cases, unstructured data can help us a lot. By performing some data engineering techniques on the unstructured data, the information can be retrieved from the same. It may also help us train an accurate model with limited data.

Key Takeaways

• Structured data are the type of data that is very easy to understand and analyze and can is quickly fed to the algorithms for model building.

• Unstructured data is very complex-natured data that is mostly not considered for research and other essential works.

• Semi-structured data is all the unstructured data but with tags or descriptions, which can sometimes be used after applying data engineering techniques.

• Unstructured data are mostly not preferred, but they can sometimes be used with proper tools and techniques in case of data scarcity or limited data problems.

Conclusion

In this article, we discuss structured and unstructured data with their behaviors according to the machine learning algorithms, followed by some other important stuff related. This will help one to understand the data better and act according to it.