Structured vs. unstructured Data: What's the difference?

Businesses across industries generate huge amounts of data, and using it to generate insights via data science and AI has created significant competitive advantages. There are two main types of data: structured and unstructured. Most data can be segmented into the two categories depending on what data is collected, how it's stored, and what it can be used for. Understanding these two groups and how to work with them is crucial for any organization looking to leverage data to improve their operations and decision-making processes.

Structured vs. unstructured data

Structured data is organized and formatted, usually collected and stored in databases and spreadsheets. It can be easily analyzed, searched, and organized, and it's also usually easy to access and import into various software solutions for data analysis and machine learning. Unstructured data, on the other hand, refers to data not easily organized or formatted, such as documents (PDFs or scans), images, videos, social media posts, etc. This data type doesn't follow a specific format, making it difficult to organize, search, and analyze. 

The key differences between structured and unstructured data are organization and accessibility, the tools required to process the data, and how much data can be stored and processsed. Structured data is easier to manage and access, and can be processed with spreadsheets, database, and/or business intelligence software. It's also easy to store large amounts of structured data.

Unstructured data can be challenging to organize, especially if the data collected come from different sources and consist of different data types. Processing unstructured data is also more challenging, requiring machine learning models to extract insights. This data type also takes up more storage space, and can require specific storage systems like datalakes.

How do companies use structured and unstructured data?

Businesses use structured data and unstructured data in different ways, depending on their needs and goals. Structured data is usually leveraged for data analysis, and can be used to make data-driven decisions when managing inventory, driving more sales, improving customer experiences, and more.

For example, an e-commerce companies keep meticulous records of their inventory at every warehouse. These records are structured data, and can be quickly processed via business intelligence software to populate an easily understandable dashboard that highlights patterns of product movement, and notifies employees when stock is low or in high demand. The company can use this information to make better decisions when buying products, moving stock between warehouses, and more.

Leveraging unstructured data for insights can be more challenging, but it can also yield game-changing results for businesses that invest in the necessary resources and prioritize the right use cases. For example, the same e-commerce company that uses structured data to improve inventory management might collect product reviews with the intention of finding and analyzing any patterns that emerge in customer sentiment, satisfaction, and brand perception. To accomplish this, the business will need to invest in data storage and machine learning operations (MLOps) technologies, along with data scientists or ML engineers experienced in training natural language processing (NLP) models.

Once the business has acquired these resources, their ML team can annotate a dataset of product reviews to capture product and business sentiment, train a model on the labeled data, and use the deployed model to find emergent patterns. Business leaders can use the model’s classifications to make informed decisions on what kinds of products they can offer to improve customer satisfaction and brand loyalty. Other machine learning solutions built on unstructured data include: 

1. Recommendation engines that personalize ads and products shows on the screen based on what the customer in question has purchased or searched for

2. Image classification algorithms that sort product listings based on their picture, making sure that products are tagged correctly and easy for customers to find

3. Segmentation models built on traffic or security video data that alerts appropriate officials to unsafe situations or activity

4. And much more!

There are many innovative software solutions that make it easier and faster for ML teams to train powerful algorithms that can transform businesses. For example, Labelbox Catalog makes it simple for data scientists to explore, filter, and search huge amounts of data (both structured and unstructured). Teams can use this tool to curate datasets for labeling, find specific examples of data, and even understand the patterns, gaps, and possibilities of the data they have.

What is semi-structured data?

While most data fits squarely into the structured or unstructured categories, there are some data types that have characteristics of both. Semi-structured data doesn't fit neatly into a traditional structured data model, like a relational database, but also isn't completely unstructured.

Semi-structured data often has a well-defined structure, but that structure can be variable or flexible, and there may not be a strict schema or data model governing it. Unlike traditional structured data, semi-structured data often allows for nested or hierarchical data structures, and individual data elements may not have a fixed type or format. Semi-structured data is also typically easier to work with than unstructured data, as it usually has some level of organization or metadata that can be used to extract insights or information from it. 

Examples of semi-structured data include:

1. XML and JSON files: these data formats are widely used for storing and exchanging semi-structured data, such as documents, data records, configurations, and more.

2. Web pages: Web pages often contain semi-structured data, such as HTML tags and attributes that can be used to extract information about the page's content, structure, and metadata

3. Social media posts: Social media posts, such as tweets or Facebook updates, often contain structured or semi-structured data, such as hashtags, mentions, or geolocation

4. Log files: Log files generated by software applications, operating systems, or network devices often contain semi-structured data, such as timestamps, error messages, and status codes

5. Sensor data: Data generated by temperature sensors, humidity sensors, or GPS devices often have a semi-structured format that includes metadata and timestamps

How to leverage your unstructured, structured, and semi-structured data with Labelbox

Leveraging any data for machine learning can be a time-consuming task, even if the data in question is relatively easy to work with. ML teams face many common roadblocks, including:

1. Wade through an ocean of data to find the right data to label for their use case can eat up ML engineers’ and data scientists’ time

2. Labeling data with home-grown or open source annotation tools can be hard to scale, maintain, and troubleshoot

3. Labeling data with external vendors can cause data security issues and slow down the process with opaque workflows

4. Monitoring model performance metrics and operations during the model training process

Labelbox enables ML builders with powerful, intuitive tools that optimize and accelerate these processes. Labelbox Catalog allows teams to view all their data — whether it’s labeled or unlabeled, structured or unstructured (or semi-structured), and includes various data types from multiple sources. This tool also allows users to zoom into each data row to view metadata, sort and filter data by any value, save slices of filtered data to find later, and more, making it easy and fast to gain a better understanding of your data.

Labelbox Annotate offers a customizable labeling solution for any datatype, customizable labeling workflows that ensures annotation quality without sacrificing speed, full data ownership and transparency into the labeling process, and built-in world-class collaboration solutions to help teams work together efficiently.

Labelbox Model empowers teams to evaluate model performance and track its improvement over iterations, find and fix labeling and model errors, and compare model runs.  

Final thoughts on structured vs. unstructured data

Most organizations collect vast amounts of structured, unstructured, and semi-structured data. Leveraging this data for insights that drive informed decision-making and tools to monitor and optimize various operations can help businesses realize significant benefits. Structured data, or data collected in columns and rows, can be easily stored in databases, easily analyzed via spreadsheets or business intelligence applications, and leverage for fast insights in sales, marketing, finances, and more.

Unstructured data, such as images, videos, audio, and text, require more complex storage and processing solutions, and often need machine learning models to extract relevant insights. While training machine learning algorithms for computer vision and NLP can be challenging, solutions like Labelbox Catalog, Annotate, and Model can help make this process faster and easier. Once complete, ML models can help businesses better understand and sell to their customers, reduce or accelerate menial tasks, provide relevant, contextual information to support various processes, and much more. 

To learn more about how you can better leverage data and build powerful AI/ML models fast, read The complete guide to data engines for AI.