Structured data vs. unstructured data in the age of AI

In the AI-first era of business, data lies at the heart of every organization, with the potential to power innovation and growth. But it’s not enough for a business to simply gather information — the real challenge is unlocking its true value to fuel smarter workflows, enhance customer experiences, and accelerate decision-making. In today’s enterprise landscape, two fundamental data types matter: structured data and unstructured data. Thanks to AI, organizations are revolutionizing how they engage with both, automating complex processes and uncovering intelligent insights that drive business forward.

This article explores structured and unstructured data — their differences, the challenges they bring, the value they hold, and appropriate strategies for managing each. Understanding these two types of data is essential for businesses to thrive in today’s digital economy, and that imperative has never been so strong as it is in the current climate of AI innovation.

Key highlights

• Structured data can be organized according to a predefined model like a spreadsheet that’s easy to arrange and sort
• Unstructured data, or content, is harder to organize and extract data from unless you have the right content management platform
• Semi-structured data serves as a bridge between the two data types
• Intelligent Content Management empowers enterprise organizations to leverage their content securely and at scale

What is structured data?

Structured data refers to information organized according to a predefined model or schema — think rows and columns arranged neatly in tables, where each field belongs to a specific category. The rigid format of these classic databases allows computers to quickly process queries using languages like SQL.

Because structured data follows strict rules when written (known as “schema-on-write”), it ensures consistency but requires upfront planning and organization.

Common formats

• Relational databases such as Oracle and MySQL
• Spreadsheets like Microsoft Excel and Google Sheets
• Enterprise resource planning (ERP) system records

Examples in use

• Customer databases containing names, addresses, and phone numbers
• Transaction logs detailing purchase dates and amounts
• Inventory lists tracking product IDs and quantities

 These types of datasets power critical business functions like financial reporting and CRM systems, where accuracy and speed matter most. But there are many types of business-critical files that do not fall into the category of structured data.

What is unstructured data?

Unstructured data, also called content, includes the contracts closing deals, the campaign videos telling brand stories, the offer letters bringing talent on board, and all the other files that make business go round. It lacks a fixed format or schema, and doesn’t fit neatly into tables because it often contains rich media or free-form text requiring interpretation during analysis (known as “schema-on-read”).

This makes unstructured data more complex — but also richer in context than structured data. And this content represents roughly 90% of an organization’s data, creating a huge opportunity for businesses. Yet in most organizations, and particularly those using legacy technology stacks, unstructured data remains largely fragmented across systems or locked away and hard to access. The explosion of digital communication means unstructured data now dominates enterprise environments — yet remains massively underutilized and often lacks governance.

Common formats

• Emails
• Videos
• Social media posts and comments
• Audio recordings and podcasts
• PDFs, Word documents, website files

Examples in use

• Customer reviews expressing opinions on products/services
• Marketing campaign videos telling brand stories
• Chat audio from support teams
• Meeting notes from key stakeholder meetings

 All of this content holds valuable insights waiting to be unlocked by advanced AI-powered analytics — and ideally those analytics are embedded right into content management platforms.

Comparing structured vs. unstructured data

To better understand the key differences between structured and unstructured data types, it’s useful to compare the attributes of each.

The type of schema application is a key distinction here. Structured data has defined rules around how data is entered — for example, it’s often organized in rows and columns. “Schema on write” means that the organization of the data is defined upfront, and therefore is quite simple to parse, even within giant databases.

But unstructured data can take on many forms — simple text, formatted word processing docs, images, videos, audio recordings — so the information and insights contained within these content files have to be dynamically interpreted (“schema on read”). The trick is in the interpretation, and traditional database tools can’t pull it off.

Now, with generative AI at the disposal of everyone, organizations can finally add structure to their unorganized assets — unlocking the hidden value of previously inaccessible data at scale.

Typical storage solutions for structured vs. unstructured data

Traditional storage options for structured data focus on enforcing schemas to ensure transactional integrity. They include:

• Relational databases: Store tabular info (Oracle Database, MySQL)
• Data warehouses: Store structured, pre-processed data to optimize efficient querying of large volumes (Snowflake, Amazon Redshift)
• Data lakes: Store raw, unstructured forms of files alongside their metadata (customer information from a CRM database, order details from an ERP system, inventory records from a POS system)
• NoSQL Databases: Handle document-based info without rigid schemas, enabling rapid scaling (MongoDB, Cassandra)

While structured data storage solutions are well defined, the storage of unstructured data has traditionally been more of a rogue effort. Particularly in the era of SaaS solutions and cloud-based applications, businesses have found themselves with different types of content stored in many places, making it hard to tap into the rich potential value of all of those files and content types.

That’s changing as organizations take advantage of Intelligent Content Management — a unified, AI-powered approach to managing, securing, and collaborating on content. Intelligent Content Management platforms centralize content in one place, while still allowing employees and other stakeholders to create data with the tools they ordinarily use. Beyond simple storage, Intelligent Content Management has built-in AI so organizations can gain strategic insights from content, automate workflows with agentic AI, and protect unstructured data at scale with evolved security and governance.

Use cases leveraging structured vs. unstructured data

Structured datasets underpin many foundational operations, including:

• Financial reporting that relies upon accurate transaction logs
• CRM systems managing standardized customer profiles
• Inventory control tracking stock levels precisely

These applications demand high reliability and speed, which traditional database technology delivers well.

Tapping into the value of unstructured data is a newer effort, but a lot of businesses have already excelled at some common use cases:

• Sentiment analysis mining social media and customer feedback to reveal brand perception trends
• Intelligent recommendation engines analyzing user behavior to power personalized experiences
• Healthcare diagnostics leveraging medical images that can be analyzed via machine learning to accurately detect anomalies

Blending both types of data can often yield the richest insights for industries:

• Retail: Combining sales figures (structured data) with social sentiment (unstructured data) to improve demand forecasting
• Financial services: Merging market stats (structured) with news sentiment analysis (unstructured) for more informed and timely investment decisions
• Healthcare: Combining lab results (structured) with doctor charting notes (unstructured) for deeper insight into patient conditions and treatment

The challenges of managing both types of data

Managing any type of data requires a thoughtful strategy and solid governance. The challenges of managing structured vs. unstructured data are somewhat different, however. With structured data, while mature technology has existed for a while, organizations often run into issues as the volumes of data grow. Scalability issues lead to performance degradation and inefficiency as datasets grow. In addition, structured schemas are quite rigid, so adapting to new business requirements quickly can be problematic.

With unstructured data, the challenges have more to do with the complexity of analyzing free-form text, audio, video, and other formats. Content that’s scattered across apps throughout the enterprise ecosystem compounds this challenge. In addition, considering that 90% of an organization’s data falls into the category of unstructured, storage costs can become unmanageable, and ensuring that all content abides with industry-specific regulations is another huge hurdle.

Organizations need to invest carefully by balancing new technology with strong operational practices. This helps create quality governance systems that support reliable decision-making, no matter the source of data.

Introduction to semi‑structured data

Semi-structured data is a type of data that doesn’t fit neatly into traditional tables like spreadsheets but still has some organization to it. It might have labels or tags that give meaning to some of the information contained within the file, but the way that information is arranged can vary. It’s not fixed in rows or columns.

Examples of semi-structured data can be as complex as JSON files powering web APIs or as simple as an everyday email. There’s a structure to an email (subject line, “to” field, body copy), but within that structure, there can be wide variation in the actual content. Semi-structured data is organized enough to be understood by computers but flexible enough to handle different types of information.

Semi-structured formats use tags or markers to separate parts of the data but don’t have strict rules about how those parts relate. This makes them more flexible than fully organized formats but easier to understand than plain text. They’re important for combining different data sources quickly, helping teams work faster and make decisions in fast-changing markets.

Best practices for managing structured and unstructured data

Regardless of the data type, there are a handful of golden rules that apply to managing any kind of data.

1. Integrate diverse sources thoughtfully

With both types of data, analysis and insight are only as effective as your ability to integrate data from diverse sources. That sometimes involves combining information in different formats — or even combining structured data and unstructured data. This approach helps create a comprehensive view of information, enabling better insights and decision-making.

2. Leverage advanced analytics tools

With data aggregated, you’re equipped to extract information and insights from it more easily. For instance, you can create customized, single-pane dashboards to gain insight into merged datasets, or apply AI to quickly extract information from large volumes of both structured and unstructured data.

3. Implement strong governance policies

With a well-vetted Intelligent Content Management platform, you can ensure accuracy, security, and compliance of your data through continuous audits supported by automated monitoring that prevents costly breaches, data loss, and compliance issues.

4. Prioritize quality assurance

Having the right storage, content platform, organizational protocols, and data policies will ensure that the data that enters your workflows will be pristine and functional. This is particularly important if you plan to institute AI for automated workflows or content generation — “garbage in, garbage out,” as they say.

5. Adopt scalable cloud infrastructure

Today’s cloud-native platforms provide elasticity to accommodate data spikes and rapid volume growth, so as your assets grow, your ability to manage and extract insight from your data is never compromised. In fact, it should grow with you.

By adopting these five strategies, companies can adapt more easily to fast-changing digital environments and get the most out of all their data types.

Leading organizations lean into both types of data

Understanding the inherent distinctions between structured vs. unstructured data is fundamental for enterprises aiming to adopt aggressive business strategies in the era of AI. Leading organizations recognize that while structured data formats are essential to many transactions and applications, unstructured data is just as valuable — and just as usable. With vast stores of organizational content at the disposal of every company, determining how to mine it for insights is a mission with a lot of potential payoff.

Building on tech stacks engineered to optimize for structured data, leading organizations are now investing in Intelligent Content Management strategies and platforms that will help unlock the value of “the other 90%” of data. When Intelligent Content Management, powered by agentic AI, becomes part of the core strategy, organizations can finally move at the speed of AI innovation. They can turn raw, often buried facts into actionable knowledge to fuel smarter outcomes, more efficient work, and elevated customer experiences.

Box lets you leverage all your unstructured data

Box, the leading Intelligent Content Management platform, unlocks value from unstructured data by combining secure cloud storage with powerful collaboration tools, native AI, automated workflows, and enterprise-grade security. Unlike traditional ECM systems that struggle to handle diverse file types and formats, Box provides a unified environment where all kinds of content — documents, images, videos, and more — can be stored, accessed, shared, and acted on. Critically, Box integrates with AI-powered tools that analyze content contextually to surface relevant information quickly.

 The advanced security features and intelligent automation capabilities of Box make it uniquely suited to harness unstructured data at scale. With robust encryption, granular permissions, and compliance certifications built in, businesses can confidently manage sensitive information without sacrificing accessibility or productivity. This flexibility enables organizations to break down information silos and ensure that critical insights hidden within unstructured data are easily discoverable by teams across departments.

*While we maintain our steadfast commitment to offering products and services with best-in-class privacy, security, and compliance, the information provided in this blog post is not intended to constitute legal advice. We strongly encourage prospective and current customers to perform their own due diligence when assessing compliance with applicable laws.