Structured vs Unstructured Data: Compared and Explained

According to IBM, the global volume of data was predicted to reach 35 zettabytes in 2020. Since it increases daily, data scientists expect that the number will hit 175 zettabytes in 2025. Picture this: 35ZB holds approximately 1 trillion hours’ worth of movies. It will take 115 million years to watch all those movies. Those are some impressive figures, aren’t they? Well, there’s something even more impressive about the global data sphere. The prevailing part of data, which is 80 percent or so, is unstructured. This means structured data only has about 20 percent of all generated information.

In this article, you’ll get a closer look at structured vs unstructured data. Let’s see what the difference between the two is and why you should know it in the first place. Also, we will help you understand how to handle each data type and what software tools are available for each purpose.

Structured vs unstructured data in a nutshell

The key differences between unstructured data and structured data.

Structured data stands for information that is highly organized, factual, and to-the-point. It usually comes in the form of letters and numbers that fit nicely into the rows and columns of tables. Structured data commonly exists in tables similar to Excel files and Google Docs spreadsheets.

Unstructured data doesn’t have any pre-defined structure to it and comes in all its diversity of forms. The examples of unstructured data vary from imagery and text files like PDF documents to video and audio files, to name a few.

Structured data is often spoken of as quantitative data, meaning its objective and pre-defined nature allows us to easily count, measure, and express data in numbers. Unstructured data, alternately, is called qualitative data in the sense that it has a subjective and interpretive nature. This data can be categorized depending on its characteristics and traits.

With that summary, let’s move on to more descriptive explanations of the differences.

What is structured data?

For analytical purposes, you can use data warehouses. DWs are central data storages used by companies for data analysis and reporting.

There is a special programming language used for handling relational databases and warehouses called SQL, which stands for Structured Query Language and was developed back in the 1970s by IBM.

Structured data examples. Structured data is familiar to most of us. Google Sheets and Microsoft Office Excel files are the first things that spring to mind concerning structured data examples. This data can comprise both text and numbers, such as employee names, contacts, ZIP codes, addresses, credit card numbers, etc.

The typical structured data example: Excel spreadsheet that contains information about customers and purchases.

Pretty much everyone has dealt with booking a ticket via one of the airline reservation systems or withdrawing cash using an ATM. During these operations, we don’t normally think of what kind of applications we deal with and what types of data they process. However, these are the systems that typically use structured data and relational databases as well.

What is unstructured data?

The thing with unstructured data is that traditional methods and tools can’t be used to analyze and process it. One of the ways to manage unstructured data is to opt for non-relational databases, also known as NoSQL.

If there’s a need to keep data in its raw native formats for further analysis, storage repositories called data lakes will be the way to go. A data lake is a storage repository or system meant to store huge volumes of data in its natural/raw formats.

Taking into account the whole variety of file formats of unstructured data, it comes as no surprise that it makes up more than 80 percent of all data. Given this, companies ignoring unstructured data are left far behind as they don’t get enough valuable information.

Unstructured data examples. There is a wide array of forms that make up unstructured data such as email, text files, social media posts, video, images, audio, sensor data, and so on.

The travel agency Facebook post: an example of unstructured data.

As an example, we can take social media posts of a travel agency or all posts for that matter. Each post contains some metrics like shares or hashtags that can be quantified and structured. However, the posts themselves belong to the category of unstructured data. What we’re trying to say here is, it will take some time, effort, knowledge, and special software tools to analyze the posts and collect useful insights. If an agency posts new travel tours and wants to know the audience’s reactions (comments), they will need to examine the post in its native format (view the post via social media app or use advanced techniques like sentiment analysis).

The key differences between structured and unstructured data

Differences between structured and unstructured data in detail.

Now let’s discuss a few more important differences between structured and unstructured data:

Data formats: few formats vs plethora of formats

Data formats.

Unlike structured data, unstructured data formats are presented in a surfeit of different shapes and sizes. Unstructured data doesn’t have any pre-defined data model and it is stored in its native formats (aka “original” formats). Those can be audio (WAV, MP3, OGG, etc.) or video files (MP4, WMV, etc.), PDF documents, images (JPEG, PNG, etc.), emails, social media posts, sensor data, etc.

Data models: pre-defined vs flexible

Unstructured data, on the other hand, offers more flexibility and scalability. The absence of the pre-defined purpose of unstructured data makes it super flexible as the information can be stored in various file formats. Yet, this data is subjective and more difficult to work with.

Storages for analytical use: data lakes vs data warehouses

The bigger the data volume is, the more space it requires for storage. A picture with high resolution weighs a lot more than a textual file. Therefore, unstructured data requires more storage space and is usually kept in data lakes, storage repositories that allow for storing almost limitless amounts of data in its raw formats. Apart from data lakes, unstructured data resides in native applications.

There is the potential for cloud-use in both cases.

Databases: SQL vs NoSQL

Relational databases use SQL, or Structured Query Language, to reach the stored data and manipulate it. SQL syntax is similar to that of the English language, providing the simplicity of writing, reading, and interpreting it.

This is how SQL helps make queries.

Speaking of databases for unstructured data, the most suitable option for this type of data will be non-relational databases, also known as NoSQL databases.

NoSQL stands for “not only SQL.” These databases have various data models and they store data in a non-tabular way. The most common types of NoSQL databases are key-value, document, graph, and wide-column. Such databases can process huge volumes of data and deal with high user loads as they are quite flexible and scalable. In the NoSQL world, there are collections of data rather than tables. In these collections, there are so-called documents. While the documents may look like rows in tables, they don’t use the same schema. It’s possible to have multiple documents in one collection that have different fields. On top of that, there are few to no relations between items of data. The idea here is to have less relation merging going on and instead to have super-fast and efficient queries. Although, there will be some data duplicates.

The example of a NoSQL data structure.

Ease of search, analysis, and processing

From a historical point of view, since structured data has been here longer, it’s logical that there is a great choice of mature analytics tools for it. At the same time, those who work with unstructured data may face a poorer choice of analytics tools as most of them are still being developed. The usage of traditional data mining tools usually crashes into the rocks of the disorganized internal structure of this data type.

Data nature: quantitative vs qualitative

  • classification or arranging stored items of data into similar classes based on common features,
  • regression or investigation of the relationships and dependencies between variables, and
  • data clustering or organizing the data points into specific groups based on various attributes.

Unstructured data, in turn, is often classified as qualitative data containing subjective information that can’t be handled using traditional methods and software analytics tools. For instance, qualitative data can flow from customer surveys or social media feedback in a text form. To process and analyze qualitative data, more cutting-edge analytics techniques are required such as:

  • data stacking or investigation of large volumes of data, splitting them into smaller items and stacking the variables with similar values into a single group, and
  • data mining or the process of detecting certain patterns, oddities, and interactions in large data sets to express possible outcomes in advance.

Tools and technologies

Structured data management tools.

Among the most commonly used relational database management systems, data tools, and technologies there are the following:

  • PostgreSQL. It’s a free, open-source RDBMS that supports both SQL and JSON querying as well as the most widely used programming languages such as Java, Python, C/C+, etc.
  • SQLite. It’s another popular choice of an SQL database engine contained in a C library. It’s a lightweight and transactional system that doesn’t rely on a separate server process as it is rather inserted into the end-program.
  • MySQL. One of the most popular open-source RDBMSs that is fast and reliable. It runs on a server and allows for creating both small and large apps.
  • Oracle Database. This is an advanced database management system with a multi-model structure. It can be used for data warehousing, online transaction processing, and mixed database workloads.
  • Microsoft SQL Server. Developed by Microsoft, SQL Server is a reliable and functional relational database management system that makes it possible to store and retrieve data as per requests of other software applications.
  • OLAP applications. A unit of business intelligence (BI), online analytical processing (OLAP) stands for an advanced computing approach that answers multi-dimensional queries effectively and swiftly. OLAP tools allow users to work with data from different perspectives, because they combine data mining, a relational database, and reporting features. Apache Kylin is one of the most popular open-source OLAP systems. It supports large data sets as it is synced with Hadoop.

Unstructured data tools. As unstructured data comes in various shapes and sizes, it requires specially designed tools to be properly analyzed and manipulated. Also, there’s a necessity of finding a qualified data science team. Not only is it useful to understand the topic of data, but it is also crucial to figure out the relations of that data.

Unstructured data management tools.

Below you find a few examples of tools and technologies to manage unstructured data effectively:

  • MongoDB. This is a document-oriented database management system that does not require any rigid schema or structure of tables. It is thought of as one of the classic NoSQL examples. MongoDB uses JSON-like documents.
  • Amazon DynamoDB. Offered by Amazon as a part of their AWS package, DynamoBD is an advanced NoSQL database service for complete data management. It supports document and key-value data structures and is a good fit for working with unstructured data.
  • Apache Hadoop. This is an efficient, open-source framework used for processing large amounts of data and storing it on inexpensive commodity servers. Apart from being a powerful tool, Hadoop is also flexible as it does not require having a schema or a structure for the stored data. It helps with structuring unstructured data and then exporting this data to relational databases.
  • Microsoft Azure. Presented by Microsoft, Azure is a comprehensive cloud service for building and managing applications and services via data centers. Azure Cosmos DB is a fast and scalable NoSQL database that helps with storing and analyzing masses of unstructured data.

Back in the day, unstructured data analysis was typically manual, and a time-consuming process. Nowadays there are quite a few advanced AI-driven tools that help sort out unstructured data, find relevant items, and store the results. The technologies and tools for unstructured data incorporate both natural language processing and machine learning algorithms. As such, it is possible to adjust software products to the needs of specific industries.

Data teams to handle data

Unlike structured data tools, those designed for unstructured data are more complex to work with. Therefore, they require a certain level of expertise in data science and machine learning to conduct deep data analysis. Besides that, specialists who deal with unstructured data have to have a good understanding of a data topic and how the data is related. Given the above, to handle unstructured data, a company will need qualified help from data scientists, engineers, and analysts.

Structured and unstructured data examples and use cases

So, when you think of dates, names, product IDs, transaction information, and so forth, you know that you have structured data in mind. At the same time, unstructured data has many faces like text files, PDF documents, social media posts, comments, images, audio/video files, and emails, to name a few.

More often than not industries need to leverage both data types to improve the efficiencies of their services.

How structured and unstructured data is used in different industries.

Structured data use case examples

ATMs. Any ATM is a great example of how relational databases and structured data work. All the actions a user can do follow a pre-defined model.

Inventory control systems. There are lots of variants of inventory control systems companies use, but they all rely on a highly organized environment of relational databases.

Banking and accounting. Different companies and banks must process and record huge amounts of financial transactions. Consequently, they make use of traditional database management systems to keep structured data in place.

Unstructured data use case examples

Image recognition. Online retailers take advantage of image recognition so that customers can shop from their phones by posting a photo of the desired item.

Text analytics. Manufacturers make use of advanced text analytics to examine warranty claims from customers and dealers and elicit specific items of important information for further clustering and processing.

Chatbots. Using natural language processing (NLP) for text analysis, chatbots help different companies boost customer satisfaction from their services. Depending on the question input, customers are routed to the corresponding representatives that would provide comprehensive answers.

What is semi-structured data?

How data is organized in JSON.

While semi-structured data may seem like a happy medium, it is not like that. In today’s highly competitive environment, businesses need to use all data sources to receive information and use it correctly to reap the benefits.

The blurred line between structured and unstructured data

In the past, companies had no real way of analyzing unstructured data, so it was discarded while the focus was put on the data that could be easily counted. Nowadays, companies can use artificial intelligence, machine learning opportunities, and advanced analytics to do the tricky unstructured data analysis for them. For example, corporations like Google have made huge advances in image recognition technology by creating AI algorithms that can automatically detect what or who is on a photograph.

Truth be told, those lines between structured and unstructured data are a little bit blurred because most datasets are semi-structured these days. Even if we take unstructured data like a photograph, it still has components of structured data such as image size, resolution, the date the image was taken, etc. This information can be organized in a tabular format of relational databases.

Now that you know the characteristics and differences between unstructured and structured data, you can make an informed decision on whether or not you should invest in technologies to grasp unstructured data benefits. The best-case scenario for corporations is to adopt both data types, improving the effectiveness of business intelligence.

Originally published at AltexSoft tech blog “Structured vs Unstructured Data: Compared and Explained

Being a Technology & Solution Consulting company, AltexSoft co-builds technology products to help companies accelerate growth.