Guide to Structured vs Unstructured Data: with Real-World Examples

Every day, your business creates data—customer details, social media feedback, product inventories, emails, sales reports, and more. Making sense of all that information starts with understanding what type of data you’re dealing with.

At the most basic level, data comes in two main forms: structured and unstructured. Knowing the difference between them isn’t just technical jargon—it’s a practical way to figure out how to store, analyze, and act on the information your business already has.

In this guide, we’ll break down what structured and unstructured data actually mean, how they differ, where each type shows up in real life, and how you can use them to make better decisions. Whether you're just starting to think about data strategy or looking to get more value from the data you already collect, this article will give you a clear foundation.  

What is structured data?

Structured data refers to information that is organized in a predefined manner, typically in rows and columns. This format allows for easy entry, storage, and analysis.

Characteristics:

• Schema-defined
• Easily searchable
• Quantitative in nature

Examples:

• Customer databases: names, contact details, purchase history
• Financial records: sales figures, budgets, profit margins
• Inventory systems: product IDs, stock levels, supplier information

What is unstructured data?

Unstructured data lacks a predefined format, making it more complex to process and analyze. Despite this, it holds valuable insights, especially when combined with structured data.

Characteristics:

• No fixed schema
• Rich in information
• Qualitative in nature

Examples:

• Emails and chat logs
• Social media posts and comments
• Multimedia files such as images, videos, and audio recordings

Introducing semi-structured data

Between structured and unstructured data lies semi-structured data. It doesn’t fit neatly into traditional databases but contains tags or markers to separate elements.

Characteristics:

• Flexible schema
• Hierarchical or nested structure
• Often used for data exchange between systems

Examples:

• JSON and XML files
• Email metadata (to/from fields, subject lines)
• Application and server log files

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Why understanding data types matters

When you know the difference between data types, you can grasp why distinguishing between structured and unstructured data is vital:

• Informed decision-making: Recognizing data types helps in selecting appropriate analysis tools, leading to more accurate insights.
• Resource optimization: Efficient data management reduces storage costs and improves processing times.
• Enhanced customer experience: Combining different data types can provide a holistic view of customer behavior, enabling personalized services.

How businesses use structured and unstructured data every day

Understanding how companies leverage both structured and unstructured data can provide valuable insights into practical applications. 

Here are several real-world examples:

1. DHL: enhancing pharmaceutical logistics

DHL Global Forwarding's Temperature Management Solutions group specializes in temperature-controlled logistics for the pharmaceutical and biotech industries. Maintaining precise temperature ranges is critical, as deviations can compromise product efficacy. 

By integrating structured data, like shipment details and temperature logs, with unstructured data, such as customer feedback and incident reports, DHL can monitor shipments in real-time, quickly identify issues, and ensure compliance with stringent industry regulations. 

2. audibene: streamlining hearing care services

audibene, a rapidly growing hearing care company, faced challenges with data governance and siloed information. By adopting Domo, audibene unified structured data (like sales metrics and appointment schedules) with unstructured data (including customer service interactions and feedback). This integration allowed for more efficient reporting, improved data access across departments, and enhanced decision-making processes.

3. Traeger Grills: real-time business monitoring

Traeger Grills implemented Domo to gain real-time insights into its business operations. By combining structured data (such as sales figures and inventory levels) with unstructured data (like social media sentiment and customer reviews), Traeger can monitor performance metrics instantly. This holistic view enables proactive decision-making and fosters a data-driven culture. 

4. Grubhub: personalizing food recommendations

In 2023, Grubhub analyzed millions of orders to identify emerging food trends. By combining structured data (such as order quantities and timestamps) with unstructured data (like customer reviews and social media mentions), Grubhub discovered a significant increase in unconventional orders. For instance, there was a 33 percent rise in pineapple pizza orders and an 89 percent  increase in pickle orders, reflecting shifting consumer preferences.

5. Healthcare industry: improving patient outcomes

In healthcare, combining structured data (like electronic health records) with unstructured data (such as physician notes and patient feedback) can lead to better patient outcomes. For instance, analyzing unstructured clinical notes alongside structured lab results can help identify patterns and inform treatment plans.

What to watch out for when managing different types of data

Working with structured, semi-structured, and unstructured data can unlock major business value, but it also introduces challenges that organizations need to anticipate and manage proactively. 

Here are key issues to keep in mind:

1. Integration complexity

Different types of data often come from different systems—CRM tools, web platforms, sensors, emails, social channels, cloud services. Combining them in a meaningful way can be technically complex and resource-intensive.

• Challenge: Data silos and inconsistent formats make unification difficult.
• Tip: Use data integration platforms (like Domo, Snowflake, or Talend) that support multi-format ingestion and transformation.

2. Data quality and consistency

Structured data can still suffer from issues like incomplete fields, duplicates, or outdated values. Unstructured data introduces a whole new level of variability—think sarcasm in a customer review or inconsistent spelling in user-generated content.

• Challenge: Dirty data leads to misleading insights or wasted time during analysis.
• Tip: Invest in data quality frameworks and regular audits. For unstructured data, explore NLP tools that can detect sentiment or normalize language variations.

3. Storage costs and scalability

Unstructured data—especially images, videos, and log files—can balloon storage needs quickly. Storing and processing this data cost-effectively requires scalable infrastructure.

• Challenge: Cloud storage and compute costs can spiral without proper management.
• Tip: Use tiered storage and automated lifecycle policies. Leverage data lakes or lakehouses to handle large, diverse data sets efficiently.

4. Security and compliance

Different types of data carry different levels of risk. For example, structured data may contain financial records, while unstructured files might include sensitive legal documents or personal identifiers buried in text.

• Challenge: It's harder to monitor and protect unstructured data, which can be stored across many systems and formats.
• Tip: Apply encryption, role-based access controls, and classification frameworks across all data types, not just databases.

5. Lack of internal expertise

Analyzing structured data often only requires spreadsheet or SQL skills. Unstructured data analysis demands deeper expertise—natural language processing, machine learning, or specialized visualization tools.

• Challenge: Teams may not have the skills needed to work with more complex formats.
• Tip: Start with small, focused use cases. Consider upskilling internal staff or partnering with vendors and platforms that offer guided analytics (like Domo).

6. Tool and platform mismatch

Not all tools are designed to handle every data type. For example, traditional BI tools may struggle with image or audio data, while some big data platforms may be overkill for simple structured reporting.

• Challenge: Choosing the wrong tool can result in wasted investment or limited usability.
• Tip: Map your tools to your actual data and business questions. Choose flexible platforms that support both structured and unstructured use cases.

By anticipating these challenges, businesses can set themselves up for long-term success. The goal isn't just to collect more data but to make that data useful, trusted, and actionable.

Not sure where to start? Use this checklist

Whether you’re leading a department, running a business, or stepping into a new analyst role, the first step toward using data effectively is simply knowing what you have and how to work with it. This checklist is designed to help you build a strong foundation, one step at a time.

1. Audit your current data sources

Begin by mapping out where your data lives. You likely already have more than you think, from structured spreadsheets to unstructured customer messages.

Include sources such as:

• CRM and ERP systems (e.g., Salesforce, NetSuite)
• Website analytics tools (e.g., Google Analytics)
• Email marketing platforms
• Social media platforms and reviews
• File storage systems (e.g., Google Drive, Dropbox)

Why this matters: Knowing where your data lives helps you prioritize which tools to integrate and what kinds of insights are possible.

2. Classify your data types

Label each data source as structured, semi-structured, or unstructured. This doesn’t need to be technical—it’s simply about understanding format and complexity.

Example:

• Sales reports = structured
• JSON files from APIs = semi-structured
• Customer support emails = unstructured

Why this matters: This step guides your tooling and helps you decide what kind of analysis is realistic at each stage.

3. Identify your most important business questions

Don’t start with “all the data.” Start with one or two focused, strategic questions—something actionable that matters to your business right now.

Examples:

• What’s driving customer churn?
• Which marketing channels convert best?
• Are certain support issues appearing more frequently?

Why this matters: A focused goal keeps your effort scoped and achievable—and builds momentum with early wins.

4. Choose tools that match your needs and skills

Use tools your team can operate today and grow into more advanced platforms over time.

Good starting points:

• Structured data: Excel, Google Sheets, SQL, Power BI
• Semi-structured: NoSQL tools, JSON viewers, integrations like Zapier
• Unstructured: Domo, cloud NLP tools (e.g., Google Cloud Natural Language), sentiment analysis dashboards

Why this matters: Adopting overly complex tools early leads to slow adoption and wasted time.

5. Create a shared, central source of truth

Data is most useful when everyone works from the same version. Set up centralized dashboards or integrated systems that bring key metrics into one place.

Consider:

• A BI platform (like Domo) that pulls in real-time structured and unstructured data
• Scheduled reports shared across teams
• Alerts and notifications triggered by key thresholds or changes

Why this matters: Shared visibility encourages collaboration and aligns teams around consistent data.

6. Build data literacy into your culture

You don’t need to be a data scientist to work with data effectively, but your team should be comfortable asking questions, spotting trends, and making decisions based on evidence.

Ideas to encourage this:

• Host quarterly data demos or insights reviews
• Offer short internal trainings or lunch-and-learns
• Start small: teach teams how to interpret one dashboard

Why this matters: When data becomes part of everyday thinking—not just a reporting task—it creates long-term value and drives smarter decisions.

Bonus tip: Start where you are, not where you think you “should” be. Many successful data strategies begin with a single report, a single insight, or a single team willing to test new ideas. Over time, these habits scale.

Bring your data together with Domo

Differentiating between structured, semi-structured, and unstructured data is foundational for any modern organization. Structured data brings speed and clarity. Unstructured data adds context and depth. Semi-structured data offers flexibility.

Understanding how each type functions—and how they work together—sets you up to manage resources more effectively, uncover insights faster, and make smarter decisions across your organization.

If you're ready to bring all your data together in one place and turn it into real-time, actionable insight, Domo makes it possible. Whether you’re working with spreadsheets, cloud platforms, marketing dashboards, or raw social media feeds, Domo helps you unify structured and unstructured data into a single, easy-to-use platform.

Start transforming the way your business uses data—explore what Domo can do for you.

Quick glossary: terms every data-curious professional should know

Schema: A structure that defines the format of data, such as field names and data types.

Metadata: Information about data, like when a file was created or who authored it.

SQL (Structured Query Language): A language used to interact with structured databases.

NoSQL: A non-relational database model used for storing semi-structured or unstructured data.

Data lake: A centralized repository that stores raw data in various formats.

NLP (Natural Language Processing): A field of AI focused on interpreting and processing human language.

ETL (Extract, Transform, Load): A process that collects data, formats it, and loads it into a system for analysis.

BI (Business Intelligence): Tools and processes for turning data into insights, often through dashboards or reports.