Discover definitions and differences between structured vs. unstructured data.
Data is the oil that fuels the growth of modern enterprises. But unless you have the tools to unlock the potential of data, you might be left stuck on the tracks as your competitors speed ahead.
With the rise of Big Data, the nature of the data that we work with has changed drastically. Data scientists like to refer to the ‘3 Vs’ of Big Data:
- Volume. We produce greater quantities of data than ever before. For example, Youtube users produce a staggering 5 hours of video every second.
- Velocity. Data is generated and collected faster than ever before. If the 1980s were characterized by the revolution of digitizing paper records into ERPs and CRMs, the 2020s are defined by collecting real-life web click events and emissions from the Internet of Things (IoT) sensors as they occur.
- Variety. Data once came in strictly formatted files - you could have your spreadsheets or some basic file systems. Nowadays, data is produced in multiple formats, from .wav files for audio, graph collection of social media activity on Facebook and Instagram, to massive data dumps of Twitter text messages and chats.
The 3 Vs of Big Data reshaped the data landscape as we knew it.
The past was traditionally marked with structured data, which was low volume, slowly produced, and of a limited variety. The present is more exciting: data is produced in greater quantities at a much faster rate and in many unstructured formats.
Here, we’ll take a look at the distinction between structured and unstructured data. We’ll show you how to take control of the many different forms of data to set you up for future success.
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What is structured data?
Structured data is data that is organized according to a predefined data model. Before data is collected, a schema is provided which specifies the form of the data in terms of rows and columns in a tabular structure, as well as the data types for each field within the table and any constraints that apply (for example, field X can only be 60 characters long).
Structured data is how we usually think of data - as a table within a relational database or an Excel spreadsheet file, where everything is neatly organized.
What are examples of structured data?
Examples of structured data include anything that you might find in a relational database or spreadsheet:
- The name and surname of a customer;
- A customer address separated into the fields Street, County, ZIP codes, State, Country;
- The UUID of the product ordered;
- The order total in the float (decimal) data type;
- The date an order was placed;
- … and many more.
Structured data can be of numeric or text type. Depending on which method you use to store it, those data types could also be broken down further, such as into DateTime objects, Integers, Floats, Character Fields, Text Fields, etc.
So, how does structured data differ from unstructured data?
What is unstructured data?
Unstructured data is deceptively named.
Rather than being devoid of any structure, these data have an inner structure. However, this is either unknown prior to data collection, or we don’t have a predefined data model when collecting data.
We don’t impose a schema at the point of data collection, but we might clean it and organize it post-collection.
What are examples of unstructured data?
There are multiple unstructured data sources:
- Images. From pictures of posts on Instagram to satellite imagery, images form a rich source of unstructured data.
- Text. Text messages on Twitter, posts on Facebook, support chat transcripts, email, and other sources of textual data are another example of unstructured data.
- Video. Youtube shots, as well as user-generated videos on other platforms, provide rich information in many different video file formats, such as .mp4, .mov, .avi, etc.
- Audio. Whether it be recordings of telephone conversations, statements for journalists and courts, audio queries on search engines, or simply music, audio files (mp3, wave, etc.) are a new source of unstructured data with huge potential.
- Sensor data. The Internet of Things (IoT) has revolutionized manufacturing and robotics and opened the doors to home automation. What’s more, mobile data follows users 24/7, making sensor data an indispensable source of unstructured data.
- Other data. We’ve barely scratched the surface of unstructured data. It includes anything from website browsing and clicking behavior, to modernized sources of information like facial recognition data, fingertips and biometric data, and much more.
Between structured and unstructured data, there is another data format: semi-structured data.
What is semi-structured data?
Semi-structured data sits at the intersection of structured and unstructured data. It uses a flexible schema but no predefined data model. Semi-structured data uses tags and semantic elements to organize data at the time of collection, but leaves the definitions of tags and semantic elements open. This is also called a self-describing structure.
What are examples of semi-structured data?
There are three main sources of semi-structured data:
- XML. XML (or Extensible Markup Language) organizes information into nested hierarchies. Each hierarchy is clearly defined from the offset with an XML schema, but the contents within those hierarchies are flexible.
- Metadata. Metadata is data about data. It often comes attached to unstructured data. For example, images often come equipped with information about the timestamp, location, and the device on which the photo was taken. Emails, which can contain fully unstructured text data, are reliably equipped with metadata about the subject line, body of the email, HTTP header, etc. Metadata is extremely useful for classifying unstructured data.
What is the difference between structured and unstructured data?
There are 7 main differences to keep in mind when working with structured or unstructured data:
- Data model. As specified above, structured data is characterized by a predefined data model that imposes a schema-on-write. In contrast, unstructured data has no data model at the time of data collection (but it might do during analysis, which is referred to as schema-on-read).
- Data storage infrastructure. Structured data is typically stored in relational databases (RDBMS) or data warehouses in a neatly organized fashion. Unstructured data, on the other hand, is usually dumped into a data lake or a specialized NoSQL database and only cleaned and analyzed later.
- Data pipeline architecture. This is the distinction between designing your data pipeline as ETL vs ELT. In ETL, data is first collected, cleaned, and only structured data is stored. In ELT, unstructured data is fully stored in its raw format within a storage software, only to be analyzed at a later stage. The difference in data pipeline architecture carries many engineering consequences depending on the pipeline that you choose, from resilience to performance tuning.
- Preprocessing needed. Every data source needs data cleaning. However, structured data requires less hands-on work (e.g. removing outliers, standardizing DateTimes, etc.) compared to unstructured data, which necessitates higher-level engineering and data science skills to be cleaned.
- Tools available. Structured data has been around for so long that we have specialized and fine-tuned tools to access, clean, and analyze it. From SQL (Structured Query Language) which allows us to perform easy data manipulations, to out-of-the-box algorithms for machine learning (regression, classification, clustering), the toolset for structured data is tried and tested. On the other hand, unstructured data tools are novel; there is no clear ‘best in class’ solution for the data operations needed to unlock the potential of unstructured data. A lot of analysis is based on data mining techniques, which use custom-built algorithms to analyze unstructured data.
- Searchability. Because of the organization of data, the structured kind can be searched much more easily, even by non-technical users. Unstructured data, however, requires specialized technology (e.g. ElasticSearch for text data) or a specialized skillset (e.g. vectorization of image) to perform searching.
- Types of insights. Structured data is often used to streamline and optimize operations, with its main power for insights lying in the prediction of future outcomes. In comparison, unstructured data is often more useful for discovery. Because of the unexplored relationships within unstructured data, we can often tap into novel insights, which reshape how we think about customers and products.
How can Keboola help?
Keboola is the all-in-one data platform designed to make the work of data practitioners easier. We believe that everyone should be able to manage their data and unlock the potential of structured and unstructured data.
Keboola was built with the process of insight extraction in mind:
- Automate data collection from third-party apps (and databases) with the Extractors, irrespective of predefined data models.
- Automate data cleaning and transformations with Transformations and Applications (bonus: it comes with data versioning). This allows you to set complex procedures for cleaning (un)structured data and set it on autopilot (set-and-forget principle to save you time).
- Flow data between different storages with Writers. From relational databases and warehouses to NoSQL databases and data lakes - we impose no limits.
- Schedule your data pipeline tasks with Orchestration. Make sure that your data is always fresh and ready for analysis.
Try running your data pipelines for yourself with Keboola’s forever-free account. You heard that right - Keboola gives you 300 free minutes every month, no questions asked.