Unstructured Data Guide: What It Is, Use Cases, and Benefits

Unstructured data is data, such as images, text documents, social posts, internet of things (IoT), videos, emails, photos, and audio files, that lacks a set format and is hard to organize in rows, columns or fields. As a result, it’s harder to store, process, and retrieve. While this data is harder to search, it’s packed with valuable insights such as customer feedback, perceptions, opinions, tone, and sentiment. The good news? You have a treasure trove of it. In fact, it’s estimated that up to 80% of data is unstructured. The bad news?‌ Only 18% of unstructured data is put to use. So most of its potential remains untapped, preventing organizations from deepening their understanding of customers, enriching customer profiles even further, and creating contextually rich AI and Customer 360 experiences.

This guide will explain unstructured data, how it’s used, how it differs from structured data, and where to find it so you can realize its full potential.

Think of unstructured data as the unruly sibling and structured data as the compliant one. They each bring their own gifts and potential in your family of data.

Let’s look more specifically at how structured and unstructured data differ.

• Format: Structured data comes in fixed formats such as numbers. Unstructured data, however, can come in any format — video, audio, image, or text.
• Storage: You can store structured datasets in tables and SQL databases. Unstructured data, due to its volume and nature, calls for different and bigger storage solutions — data lakes, which are usually object storage solutions, and NoSQL databases.
• Usability: If you’re after quantitative results, your best bet is to analyze structured data. To get usable information and insights from unstructured data you’ll need advanced techniques with built-in intelligence, such as natural language processing (NLP) and machine learning. Unstructured data can give you what structured data can’t: deep qualitative insights into your customers’ motivations and pain points.
• Volume: The space you need to store structured data in a relational database is a fraction of the space unstructured data takes up.

Semi-structured data is the middle ground between structured and unstructured data — it doesn’t have a predefined schema like structured data, but it can be stored and searched more easily than unstructured data. Semi-structured uses metadata, such as tags or semantic markers, to create a hierarchy and to separate distinct elements within datasets. For example, the raw data from an audio recording is unstructured but the audio transcript with a tagged headline, snippets, or alt-text is semi-structured.

Since unstructured data can take so many forms, let’s look at its most common sources.

Text files are usually rich with unstructured data. You’ll find it in customer emails, notes, customer logs, and chatbot chats. Your pdfs can also contain unstructured data.

If you’ve heard the term “big data”, you probably know that most of it is multimedia. According to one estimate, our digital world generates over 400 terabytes of data daily — much of it in the form of videos, digital photos, audio files, podcasts, and medical images. Every time you join a digital meeting or conference you generate unstructured data. Your security camera footage is full of unstructured data, and so is every customer video and webinar you record.

X, LinkedIn, Facebook, TikTok, Instagram, and YouTube are some of the most popular social media sites. Each channel contains troves of unstructured data. YouTube videos, customer interviews, Instagram comments on your recent post, and Facebook posts are examples of unstructured data.

Your company’s website is brimming with unstructured data. HTML and XHTML provide markup tags that serve as the building blocks for web display, but the content between the tags is unstructured.

How long can you go without your phone? Each voicemail you generate and retrieve and each customer message is rich with unstructured data. Messaging data falls in this category too.

IoT devices and sensors are loaded with unstructured data. A grocery retailer, for example, may use IoT sensors to monitor and optimize food storage temperatures. Data from medical testing, weather monitoring systems, motion sensors, and GPS systems is also unstructured.

Archived documents, scanned historical records, and other such data you have collected over the years in external hard drives or network drives is often unstructured. Government agencies usually retain a lot of unstructured historical data in their archives.

Your company’s unstructured data can be a source of compelling insights into your customers, market, and business performance.

Let’s look at four powerful use cases for unstructured data.

Artificial intelligence: No matter how advanced your AI models are, they're only as good as the data they work with. For AI agents to understand your customers and business, they need access to your proprietary data. Without this information — which primarily lives in all kinds of unstructured data - they produce generic, unreliable results. But how can you actually get this information to AI agents? That's where vector databases and retrieval augmented generation (RAG) come in.

A vector database is designed to store and manage unstructured data by converting it into numerical "vectors" that capture its meaning and relationships. This allows AI to easily find patterns, such as identifying similar images or analyzing sentiment in customer reviews, making it simpler to process and understand complex, unstructured data.

While large language models (LLMs) excel at generating responses using public data, RAG enhances these responses by bringing private enterprise data stored in vector databases or data lakes to the AI generated response. This brings further context to the question being asked to AI and improves accuracy, making it ideal for real-time or domain-specific tasks like customer support or detailed reporting.

To sum it up: A high-quality, unified data foundation – rich with insights from all your data, especially your unstructured data–is essential because it ensures that your AI agents are making decisions based on the most accurate and up-to-date information about your business and customers. Using technology like vector databases and RAG can bring insights from unstructured data to AI agents, empowering them to make decisions and take meaningful actions. Unstructured data is essentially the foundation that makes AI – particularly generative and agentic AI – possible.

Unstructured data sources, like customer service calls, transcripts, customer feedback, sensor data, and social media, can elevate customer service in numerous ways. For example, analyzing call transcripts can help you spot common issues and improve your self-service options, making it easier for customers to find answers on their own. Sensor data from products, like cars, can predict when maintenance is needed, so you can reach out to customers before problems happen. Social media feedback can help you update your self-service content and make it more relevant, ensuring customers get the help they need faster. And when you use the power of AI, data and CRM to bring all this data together into detailed customer profiles, you can move from proactive service and even turn service into sales opportunities.

Analyzing unstructured data from sales emails, CRM notes, and meeting recordings helps you learn about your customers and how customers perceive your product and their intent to buy. For example, you can look for trends that have led to successful deals in the past or keywords your buyers use frequently that may explain a recent drop in sales.

You can use these new learnings to refine your sales strategies, retain your customers, and personalize your products or services.

With the dramatic increase in the amount of data we generate daily has come a dramatic increase in cyberthreats. In recent years, data security and protection have become top priorities for most executives and data experts.

Unstructured data from online transactions, emails, chat logs and other sources can help your security teams identify anomalies and flag potential threats. For example, an unusual phrase or transaction pattern may indicate fraudulent activity. Combing through unstructured data for red flags with fraud detection automations can help your organization monitor and prevent cyberattacks and the risks they pose — financial and reputational damage.

• Enhanced insights. Unstructured data can lead to qualitative information that adds rich context and can improve business-critical decisions. With the right technologies and expertise you can identify problem areas, streamline operations, enhance your services or products, and improve marketing performance.
• Customer understanding. Analyzing your customers’ own words and reactions helps you understand their preferences and behaviors. And if you combine unstructured data from customer interactions and social media with structured data, such as past sales transactions, you start to form a unified customer profile — a well-rounded view of who your customers are and what they expect from you. With this in hand, you’re enroute to the holy grail of customer satisfaction: personalized offers and services served at the right time, each time your customers need them.
• Competitive advantage. Our digital world demands hyper-competition. Sourcing, processing, and analyzing unstructured data from online reviews, competitor videos, and social posts can help you spot market trends before they mature. You can use this powerful information to stay ahead of your competitors.

• Large data volume/scale: Unstructured data can take up massive storage space. In many companies, this data is either not captured or is spread among several data silos, and storage space doesn’t become an issue until someone decides to unify it. If you are looking for the right storage solution for your company, think about the volume of data you’ll need to store.
• Data complexity: Since unstructured data lacks pre-defined structure, it's challenging to analyze without specialized tools. Common tools employed to analyze unstructured data include natural language processing (NLP), business intelligence software, like Tableau, and machine learning.
• Data analysis: Large amounts of data in a wide variety of formats are famously difficult to analyze. Extracting the goodness of unstructured data in the form of insights is not only time-intensive — it calls for “intelligent” processing power.
• Data governance and management: Once data is unlocked, who has access to it? How does it remain secure and private at all times and across all users and use cases? How do you ensure the right policies are applied across all applications that use your data? Whether data is structured or unstructured, it must remain secure, protected, and compliant at all times.

The right data strategies can make all the difference when it comes to managing data throughout its lifecycle. Let’s look at three best practices for unstructured data.

Start by identifying key objectives — whether it's improving customer engagement, simplifying operations, or improving decision-making — and determine how unstructured data can help you achieve your objectives. For example, if your goal is to boost customer satisfaction, consider analyzing customer reviews, support emails, and customer social media reactions.

Linking unstructured data strategies to specific goals will keep your efforts focused and measurable. It will also help you prioritize what types of unstructured data to gather and analyze.

A unified data management (UDM) platform consolidates and unifies your data sources within a centralized repository. Setting up a cohesive data framework in your platform will keep your data, regardless of format, accessible, usable, and secure. Your data management framework should ideally incorporate protocols for data ingestion, metadata tagging, and centralized storage solutions such as data lakehouses or hybrid cloud environments.

Your data framework should also incorporate clear data governance policies. This way you can maintain data quality and stay compliant with regulations, which is particularly important in industries such as finance and healthcare.

product.data is a platform that unifies your unstructured and structured data on the Salesforce platform regardless of where it comes from. Because it is integrated with the Salesforce metadata framework, you can turn data into the standard objects and fields your teams already know and work with.

Check out our infographic, “5 Winning Strategies to Activate Unstructured Data”, to see how product.data untapped unstructured data into business value.

Then, watch how product.data streamline business processes, as it surfaces critical customer context hidden in unstructured data – like pdfs, audio files, and videos – directly to autonomous AI agents