ETL for Unstructured Data: Examples, Best Practices

Most of the information teams rely on from day to day—customer reviews, support tickets, videos, or sensor logs—comes in messy, unstructured formats.

According to McKinsey, more than 90 percent of the data people create and share at work is unstructured. For a long time, much of that information was out of reach to data teams, considered too hard to prepare, too expensive to process, or too inconsistent to trust. 

But that’s changing. Artificial intelligence-powered ETL (extract, transform, load) processes now make it possible for teams to work with this data: to pull meaning from it, share understanding across the organization, and even create new sources of value.

This shift matters because unstructured data holds the context behind decisions, conversations, and customer experiences. The key is knowing how to prepare it so people can actually use it. That’s where ETL comes in. Let’s take a look at what ETL for unstructured data is, how it works, and some best practices for putting it to work on your team.

What is unstructured data?

Unstructured data is any information that doesn’t fit into a predefined data model or structured table. Unlike structured data, which lives in databases with labeled rows and columns, unstructured data comes in free-form formats. It may be text-heavy, visual, audio-based, or machine-generated—and it rarely follows a consistent schema.

Examples of unstructured data are all around us:

Documents and text files

• Word docs
• PDFs
• Email threads

Multimedia

• Images
• Audio recordings
• Video

Machine and sensor data from

• IoT devices
• Server logs
• Clickstreams

Social and conversational data

• Chat transcripts
• Survey comments
• Social posts

It’s the kind of data that makes up the majority of what people and teams work with every day. It carries the context behind decisions and interactions, such as a support ticket that shows the real voice of a customer or a machine log that pinpoints when a system first started behaving abnormally. That depth of meaning is why unstructured data is so important to powering AI and big data, which thrive on natural language, imagery, and behavioral patterns.

The challenge is that unstructured data isn’t ready-made for analysis. It can’t be easily searched or queried, and storing it at scale can get expensive fast. To make it useful, teams need processes that can organize, clean, and standardize it. By using ETL, you are giving unstructured data structure so you can develop trusted insights.

What is ETL for unstructured data?

ETL—short for extract, transform, load—is a process teams use to take raw, messy data and prepare it for meaningful use. At a high level, it involves three steps:

1. Extracting data from its source
2. Transforming it into a clean, consistent format
3. Loading it into a system where people can analyze it, such as a data warehouse or lake

With structured data, this is fairly straightforward. Fields like “transaction date” or “order total” already have clear labels and formats. But with unstructured data, the process is more complex. 

Each unstructured data source may look completely different, so teams often use a schema-on-read approach: the data only gets its “shape” when it’s pulled into the system. Combine this with the sheer variety and volume of unstructured sources, and ETL becomes far more challenging than with traditional, structured inputs.

How ETL compares to other approaches

ETL (Extract, Transform, Load)

The traditional method is best when cleansing and standardizing data upfront is a priority.

ELT (Extract, Load, Transform)

Unlike ETL, ELT loads data first, then transforms it within the warehouse—common in cloud-scale environments where compute power is abundant.

Data pipelines

A broader category that moves data from one place to another, often in real time, but may do less heavy lifting on transformation.

Despite these alternatives, ETL remains critical. It’s the step that ensures unstructured data is not just collected but actually reliable and ready to be used. Without it, teams risk building insights on incomplete or inconsistent information. With it, they can transform scattered, unstructured inputs into a foundation for accurate analysis and confident decision-making.

Why use ETL for unstructured data?

If unstructured data already contains so much valuable context, why not just analyze it as is? The short answer: without preparation, it’s unreliable. ETL provides the foundation teams can use so they can make sense of unstructured data and use it with confidence. 

The value comes from how ETL addresses the biggest challenges teams face when working with unstructured information: governance, AI readiness, integration, scalability, and resilience.

Governance

Unstructured data often includes sensitive information like customer details in a support ticket or patient notes in healthcare. ETL processes help teams classify, secure, and manage this information in line with AI data governance and privacy standards. It also supports broader frameworks around compliance.

AI readiness

Large language models (LLMs) and other machine learning tools thrive on rich, unstructured inputs, but only when those inputs have been cleaned, labeled, and standardized. ETL gives unstructured data the structure for AI systems to train effectively and generate accurate takeaways.

Enterprise integration

Most teams pull unstructured data from multiple sources—emails, chat platforms, IoT sensors, or social media feeds. ETL unifies those disparate inputs so they can be analyzed together rather than in isolation.

Scalability

Unstructured data grows exponentially, and manual processes can’t keep up. ETL tools are designed to handle massive, constantly expanding volumes of information.

Resilience

By ensuring that unstructured data is clean and consistent, ETL reduces the risks of basing analysis on incomplete, duplicated, or misleading information. That reliability is what allows teams to turn unstructured data into insightful information they can trust.

The benefits of ETL for unstructured data

When ETL processes are in place, people don’t just gain control over their data—they gain new capabilities that make their jobs easier and their insights more actionable. By removing duplicates, cleaning errors, and connecting data across silos, ETL helps convert raw information into actionable data that drives impact. Here are five benefits your team may experience:

1. Breaks down data silos

Unstructured data often lives in separate systems—chat transcripts in one platform, marketing assets in another, and sensor readings stored somewhere else entirely. ETL unifies these sources so teams see the whole picture instead of fragments. And as Harvard Business Review notes, silos aren’t just technical—they also hinder collaboration. Addressing them at the data level creates a stronger foundation for connected work.

2. Automates tedious work

Manual tasks like tagging, cleaning, and categorizing unstructured data can eat up hours. ETL automates these steps, freeing people to focus on higher-value analysis and decision-making.

3. Improves data quality

ETL processes remove duplicates, correct errors, and standardize formats, ensuring the data is accurate and trustworthy. It makes what they learn easier to validate and decisions easier to defend.

4. Enhances data management

As the variety of unstructured formats grows, ETL provides a consistent framework for storing, indexing, and retrieving that data. Teams spend less time wrangling information and more time analyzing it.

5. Increases agility

When ETL is running smoothly, teams can adapt more quickly to new data sources or changing priorities. Instead of building custom fixes each time, they can scale their workflows and respond quickly to new opportunities.

By making unstructured data cleaner, easier to access, and more efficient to work with, ETL helps teams move beyond data preparation into real problem-solving and innovation.

Challenges of ETL for unstructured data 

For all its benefits, ETL with unstructured data isn’t without hurdles. Teams often discover that the same qualities that make unstructured data so valuable—its richness and variety—also make it harder to manage. To make the most of ETL, it’s important to understand the most common challenges you’re likely to face.

Data volume

Unstructured data grows exponentially. Video recordings, customer reviews, or sensor logs can add terabytes of new information every day. Without the right infrastructure, unstructured data storage and processing quickly become overwhelming.

Data variety

Unlike structured data, which follows a uniform schema, unstructured sources are inconsistent. A single project might involve PDFs, social posts, and IoT streams, each with its own quirks. Designing ETL processes flexible enough to handle this variety is no small task.

Schema and transformation complexity

Because unstructured data has no built-in structure, it often requires advanced methods to make it usable. Teams may need natural language processing (NLP), image recognition, or metadata tagging to create a workable format. These transformations add time and complexity.

Integration challenges

Bringing unstructured and structured data together can be messy. A customer support transcript, for example, may need to align with transaction records or CRM fields. Without careful integration, insights remain fragmented.

Security and compliance risks

Unstructured data can contain sensitive personal or financial information. Pulling that data into ETL processes without adequate safeguards raises compliance risks around privacy and regulatory standards.

Storage costs and performance

Large volumes of unstructured data consume storage, slow queries, and strain analytics systems. Balancing accessibility with cost and performance is a constant challenge.

The good news is that these obstacles aren’t insurmountable. With thoughtful planning, such as choosing scalable infrastructure, adopting governance frameworks, and using AI-driven transformation methods, teams can minimize friction and keep their ETL pipelines efficient and reliable.

How ETL for unstructured data works

ETL for unstructured data follows the same high-level steps as traditional ETL, but with techniques adapted for the unique complexity of text, images, video, and sensor logs. Here’s how teams typically approach the process.

Extraction

The first step is pulling data from its original source. For unstructured data, that can mean:

• APIs from cloud apps
• Web scraping for online reviews
• Streaming IoT feeds
• Pulling logs from servers

Unlike structured data sources, these inputs are highly varied, so connectors and automated pipelines are essential to keep extraction consistent and repeatable.

Transformation

Once the data is collected, it will have to be cleaned and shaped into a usable format. This step is where unstructured ETL is most complex. Teams may apply advanced methods such as NLP, OCR, and computer vision. Many of these fall under ETL and machine learning techniques, which add structure and meaning to unstructured sources.

Natural language processing (NLP)

This technology helps parse meaning from unstructured text, such as emails, support tickets, or meeting transcripts, enabling sentiment analysis or keyword extraction.

Optical character recognition (OCR)

OCR converts scanned documents and images into machine-readable text, making formats like PDFs or handwritten forms easier to analyze.

Computer vision

The method analyzes image and video data, detecting objects, patterns, or behaviors that would otherwise remain hidden.

Metadata tagging and classification

This step adds descriptive labels to unstructured data, such as file type, topic, or sentiment, which makes large data sets easier to organize and search.

The goal of transformation is to remove noise, enrich the data with structure, and make it compatible with downstream systems.

Loading

Finally, the transformed data is moved into a destination where people can use it—commonly a data lake, warehouse, or lakehouse. For unstructured data, the loading strategy often depends on how teams plan to use the data.

Batch loading

Ideal for scheduled, periodic updates, such as pulling in new files once a day for reporting or compliance checks.

Streaming or real-time loading

This approach is best when fresh data is critical, such as monitoring fraud attempts, tracking sensor data, or analyzing customer sentiment in real time.

Getting this last step right is critical for performance. Well-structured loading ensures teams can query large, complex data sets quickly without driving up costs or slowing analytics.

Examples and use cases of ETL for unstructured data

ETL for unstructured data comes to life in real-world applications. Here are several ways teams across industries are using it to turn complex information into actionable insights.

E-commerce

Online retailers deal with mountains of unstructured feedback—customer reviews, social media posts, and chatbot conversations. ETL processes can extract this information, apply natural language processing (NLP) to analyze sentiment, and load it into a dashboard that product teams and marketers can use to improve offerings and campaigns.

Inventory management

Teams responsible for stock levels often rely on sensor data, machine logs, and supplier documents. ETL pipelines can consolidate these inputs, highlight discrepancies, and forecast demand more accurately. The result is fewer stockouts and better alignment between warehouses and storefronts.

Logistics 

From GPS data to delivery logs, logistics operations generate a steady stream of unstructured data. ETL helps bring all of it together—analyzing routes, identifying delays, and flagging issues before they cascade into larger problems. It improves efficiency and keeps supply chains moving.

Healthcare

Hospitals and clinics manage sensitive unstructured data like patient notes, imaging files, and lab reports. ETL processes not only help standardize these records for analysis but also support compliance with strict data governance requirements. Physicians can then make decisions grounded in reliable, well-structured information.

Financial services

Banks and insurers handle unstructured sources such as transaction logs, support transcripts, and claims documents. ETL pipelines can uncover unusual patterns that indicate fraud or consolidate interactions to give customer service teams a 360-degree view of each client.

Best practices for ETL with unstructured data 

Unstructured data can unlock valuable understanding, but only if it’s managed well. Without a thoughtful approach, teams risk high costs, inconsistent results, and compliance issues. Following a set of best practices helps keep ETL pipelines reliable, scalable, and secure so people can trust the outcomes. Here are seven best practices to keep in mind.

1. Prioritize data quality

Unstructured data often contains duplicates, errors, or incomplete records. Building in validation checks, standardization rules, and cleansing steps ensures the information people use for analysis is trustworthy.

2. Design for scale

Unstructured data grows fast, both in volume and variety. That’s why teams also need to follow proven data management best practices to ensure pipelines remain reliable and cost-effective at enterprise scale.

3. Use metadata tagging

Adding descriptive metadata—such as timestamps, file type, or sentiment—brings order to otherwise messy data sets. This practice makes it easier to classify, filter, and query large amounts of unstructured information.

4. Secure sensitive information

Unstructured sources often contain personal, financial, or healthcare details. Applying data governance best practices like access controls, encryption, and anonymization during ETL keeps sensitive data safe and supports compliance with privacy regulations.

5. Optimize for analytics performance

Storing massive amounts of unstructured data can create bottlenecks. Techniques like partitioning, indexing, or compression help improve query speeds and keep storage costs manageable.

6. Monitor costs continuously

Because unstructured data consumes significant storage and processing power, costs can escalate quickly. Regular monitoring and optimization ensure ETL pipelines remain cost-effective over time.

7. Keep people in the loop

Even with automation, it’s important to involve subject matter experts. Having data engineers, analysts, and business teams collaborate on ETL design helps ensure the transformed data meets real-world needs.

What to look for in ETL tools for unstructured data

Choosing the right ETL tool makes the difference between a smooth, scalable pipeline and one that constantly creates headaches. For teams working with unstructured data, the most effective tools share a few common traits:

• Flexible connectors: Ability to pull data from diverse sources such as APIs, social feeds, cloud apps, IoT sensors, and logs.

• AI/ML integration: Built-in support for tasks like natural language processing, image recognition, and text classification to help transform unstructured inputs into structured outputs.

• Real-time and batch options: Support for both scheduled loads and streaming pipelines, enabling teams to balance performance with immediacy.

• Scalability: Cloud-native design that grows with data volume and variety, avoiding constant reengineering.

• Governance and security: Features like encryption, access controls, and audit trails to keep sensitive data safe and compliant.

• Cost and performance optimization: Tools that offer compression, partitioning, or indexing to keep queries fast and storage costs manageable.

• Ease of use: Intuitive interfaces, low-code or no-code features, and collaboration options so business and technical teams can work together.

A strong ETL tool should reduce the burden on teams, not add to it. By focusing on flexibility, intelligence, and usability, organizations can ensure their unstructured data is ready for analysis without slowing people down.

Future trends in unstructured data ETL

The way teams handle unstructured data is evolving quickly, and three trends stand out:

1. Smarter automation

ETL processes are increasingly using AI to take on messy work like cleansing, tagging, and enrichment, freeing people to focus on higher-value analysis.

2. Streaming-first architectures

As timely insights become essential, batch ETL is giving way to streaming pipelines that can keep pace with live customer interactions or IoT data.

3. Broader access

No-code platforms are expanding ETL capabilities beyond technical teams, while federated approaches allow you to manage data across distributed environments.

Together, these shifts point toward an ETL future that’s more adaptive, collaborative, and well-suited to the scale and variety of unstructured data.

Put your unstructured data to work with Domo

Unstructured data holds the conversations, behaviors, and signals that shape meaningful decisions—but only if teams can make sense of it. ETL for unstructured data provides the foundation, turning raw, scattered content into information that people can analyze, share, and act on with confidence. This transformation isn’t just about cleaning up messy data; it’s about creating context that leads to informed decisions, deeper customer experiences, and measurable impact.

Domo is at the forefront of this shift. With Magic ETL and Domo AI, teams can prepare, analyze, and act on unstructured data alongside traditional sources in a secure, scalable environment. Whether it’s customer reviews, video content, chat transcripts, or IoT streams, Domo helps translate complexity into clarity.

Ready to see how your team can put unstructured data to work? Explore Domo’s AI and data solutions today.