Turning Unstructured Data into Structured Data: A Step-by-Step Guide

Businesses today are swimming in data—but most of it is sitting idle.

Every day, your company generates call transcripts, PDFs, chat logs, social posts, and emails. And while these formats are rich with insight, they’re also notoriously hard to work with. You can’t easily query a customer’s email. You can’t analyze a scanned contract. You can’t visualize insights hidden in a video transcript—until that data is structured.

Unstructured data accounts for up to 80 percent of enterprise data. But without the right processes, it remains out of reach, excluded from dashboards, workflows, and decision-making.

That’s why learning how to turn unstructured data into structured data isn’t just a technical exercise—it’s a business advantage.

In this guide, we’ll walk you through a high-level, step-by-step approach to making your unstructured data useful. Whether you’re a new analyst, a business leader exploring AI, or a department head looking for better answers, this guide will show you how to start unlocking hidden value in the data you already have.

What is unstructured data?

Unstructured data refers to information that doesn’t conform to a predefined data model or structure. It includes everything from Word documents and customer support emails to video files, social media posts, and audio recordings. Unlike structured data—think Excel spreadsheets or SQL tables—unstructured data doesn’t live in neatly organized rows and columns.

While up to 80 percent of the world’s data is unstructured, its value often goes untapped because it’s harder to query, analyze, and visualize.

What is structured data?

Structured data is information that’s organized in a clearly defined format—typically rows and columns—making it easy to search, filter, analyze, and visualize. It lives in systems like relational databases, spreadsheets, and data warehouses. Common examples include customer names and addresses, purchase amounts, timestamps, and inventory quantities.

Structured data is the foundation of most analytics tools and business dashboards. It works well with SQL queries, can be visualized in charts, and is often the primary input for business intelligence and reporting systems.

Why give structure to your unstructured data?

But here’s the catch: most of the data generated by businesses today doesn’t start out structured. It’s buried in unstructured formats like support emails, sales call transcripts, or product reviews.

Unstructured data is the most underutilized asset in modern business. While most teams have dashboards for sales, marketing, and operations, vast amounts of qualitative, freeform, and multimedia data are still left out of the picture. Why? Because it’s messy. Harder to parse. And until recently, it was difficult to process at scale.

But that’s changing.

With the rise of AI, automation, and natural language processing, unstructured data has become much more accessible and valuable. Structuring this data unlocks new insights that are simply impossible to extract from rows and columns alone.

• You’re missing key context: Structured data tells you what happened. Unstructured data often tells you why.
• AI relies on it: Generative AI models, sentiment engines, and language-based analytics are all fueled by unstructured inputs. Models need structured data to train, predict, and adapt.
• Competitive advantage: Organizations that tap into unstructured data gain a richer view of customers, faster feedback loops, and a broader base for decision-making.
• Fast, accurate querying: Analysts and stakeholders can quickly ask questions and get answers.
• Automation and alerts: Systems can trigger actions based on thresholds or changes in the data.
• Collaboration and visibility: Dashboards, scorecards, and reports rely on structured data to give teams a shared view of performance.

The ability to transform that unstructured content into structured form is what unlocks its potential. Once structured, data becomes interoperable. It can move between systems, trigger workflows, feed AI, and inform decisions across the business.

From understanding how customers feel to tracking compliance risks, structuring unstructured data opens up a new frontier in data-driven work.

That’s why this work matters: the more data you structure, the more of your organization you empower with insight.

Step-by-step: How to convert unstructured data into structured data

Turning unstructured data into something useful doesn’t have to mean overhauling your entire data architecture. Transforming unstructured data is less about a single tool or product and more about following a smart, intentional workflow. But it does require a methodical approach that moves you from raw content to clean, query-ready outputs. 

Done right, it brings clarity and consistency to the types of data that were once considered too complex or messy to use. This isn’t just a technical process—it’s a strategic capability.

By following these steps, you can move from raw content to actionable intelligence, building repeatable processes that scale for different departments and use cases.

Here’s how to convert your data, step by step.

Step 1: Define your use case

Start with the end in mind. Are you looking to categorize support tickets to speed up response times? Extract dates and contract values to manage vendor obligations? Summarize product feedback from app store reviews? 

Whatever your focus, the goal should be specific and directly tied to a business outcome. That clarity helps you determine which fields matter—whether it’s dates, names, keywords, or tone—and avoid the trap of extracting “everything just in case.” Without a defined use case, it’s easy to waste time structuring data that no one ends up using.

Step 2: Inventory your data sources

This step is about surfacing what’s available and mapping where it lives. Unstructured data lives everywhere: tucked inside PDF contracts in a shared drive, buried in chat logs from your CRM, or embedded in call transcripts stored in your contact center platform. 

Look at tools like your knowledge base, cloud storage folders, email archives, or customer survey platforms. Prioritize based on accessibility and business value—focus on sources that have recurring impact or that feed high-priority processes.

Step 3: Extract raw data

Once you know what you require and where it lives, you can start pulling it into a processable format. That could mean using OCR to digitize scanned invoices or insurance forms, speech-to-text engines to transcribe customer calls, or API connections to pull in Slack conversations, support tickets, or social media mentions. 

The goal here isn’t to structure anything yet—it’s simply to get the data out of its original container and into a format you can work with, such as raw text, JSON, or tabular exports.

Step 4: Clean and prepare the data

Raw data is rarely clean. It might contain typos, inconsistent formatting, irrelevant headers, or encoding issues. Preparing it means standardizing dates, fixing character encoding problems, filtering out boilerplate content (like email signatures or disclaimers), and ensuring consistency across fields. 

For example: “$1,000.00,” “1000 USD,” and “1K” might all refer to the same value but without cleaning, they won’t be recognized as such. This is the foundation of everything that comes next, so it’s worth doing well.

Step 5: Apply structure using classification or extraction

Now, you begin to make the data useful. If you’re working with survey comments or reviews, you might classify each entry by sentiment—positive, negative, or neutral—or by topic, such as pricing, service, or product features. If you’re working with contracts, you might extract specific fields like renewal dates, payment terms, or client names. 

This stage often relies on a combination of natural language processing, rule-based parsing, and pattern recognition. It’s the point where the data stops being passive content and starts becoming something your systems can respond to.

Step 6: Transform into structured formats

This is the bridge between unstructured input and structured insight. With the relevant elements now extracted and labeled, it’s time to organize them into formats that your tools can ingest—like CSV files, SQL tables, JSON objects, or data frames. 

For instance, a set of call transcripts might now include columns for customer name, call date, identified topic, and sentiment score. A data set of scanned forms might now include extracted fields like invoice number, total amount, and due date. 

Step 7: Validate the output

Even with the best tools, structured outputs aren’t flawless out of the gate. You might find missing values, incorrect classifications, or inconsistent tagging. Validation means reviewing samples to check that extractions are correct, that formats are aligned, and that edge cases don’t get misinterpreted. 

For example, if a contract date is pulled from the wrong part of a document, it could trigger a renewal workflow months too early. Accuracy here protects downstream decisions, so build in quality checks before scaling up the process.

Tools and technologies that help

While the process of structuring unstructured data can sound complex, you don’t have to build it from scratch. A growing ecosystem of tools exists to help teams—from technical analysts to non-technical business users—tackle these challenges more efficiently.

The best tools handle automation, scale, and flexibility—while still offering enough control to tailor the output the way you want it.

Common tool categories:

• Document intelligence platforms: For extracting data from PDFs, images, and forms using OCR and entity recognition.
• Natural Language Processing (NLP) libraries: Libraries like spaCy or Hugging Face Transformers allow for deep text analysis and classification.
• ETL and ELT platforms: Tools like Magic ETL in Domo allow you to build automated data pipelines that include parsing, cleaning, and transformation stages.
• AI platforms: Tools like Domo.AI or other no-code AI platforms can classify, extract, and summarize text-based data in real time.
• Speech analytics tools: These convert call center recordings or voice notes into structured transcripts with searchable keywords or sentiment labels.
• Workflow automation tools: Platforms like Domo Workflows can trigger automated steps when certain keywords or classifications are detected in unstructured inputs.

Without the right tools, structuring unstructured data is manual, slow, and error-prone. With the right stack, even lean teams can automate the heavy lifting, reduce overhead, and start using more of the data they already have.

Use cases: Bringing structure to life

Turning unstructured data into structured insights isn’t just a technical win—it’s a business unlock. When you can convert qualitative, freeform content into usable fields and categories, it opens new paths to automation, analysis, and decision-making.

Here are some of the most impactful ways teams in various industries are structuring their unstructured data:

1. Customer support triage and insights

Before: Support teams sifted through emails manually to prioritize and assign cases.
After: Text classification models automatically tag incoming messages by urgency, product area, and sentiment, routing tickets to the right team and flagging high-risk cases.

Why this matters: Faster response times, better resource allocation, and a closed feedback loop that improves product development.

2. HR feedback and engagement analysis

Employee surveys and exit interviews are goldmines of unstructured insights. By using NLP to tag sentiment, themes, and recurring terms, HR teams can quantify workplace concerns and strengths.

Why this matters: You move beyond guesswork. Structured feedback supports strategic talent decisions and targeted culture initiatives.

3. Healthcare documentation and compliance

Clinicians document conditions, treatment plans, and patient concerns in freeform text. Structuring that content using named entity recognition and OCR allows teams to extract diagnosis codes, medications, and timelines.

Why this matters: Hospitals can improve billing accuracy, meet documentation compliance, and surface insights for care improvement.

4. Legal contract intelligence

Legal and procurement teams structure contracts and policy documents by extracting terms like expiration dates, payment clauses, and liability terms.

Why this matters: Missed renewals and compliance risks are reduced. You can automate renewals, manage obligations, and flag risk-heavy terms in advance.

5. Retail product review analysis

Unstructured customer reviews are parsed for themes—mentions of quality, shipping, or sizing—and categorized by sentiment.

Why this matters: Marketing and merchandising teams can identify product issues early, improve descriptions, and adjust positioning based on customer language.

6. Financial advisory compliance

Notes from financial advisors are transcribed and structured to flag high-risk phrases, track topics discussed, and verify adherence to regulatory requirements.

Why this matters: Teams reduce compliance audit time and gain a data trail that supports transparency and oversight.

7. Manufacturing maintenance optimization

Unstructured technician logs are parsed for recurring issues, machine IDs, and part numbers. Structured outputs allow for trend analysis across equipment types and locations.

Why this matters: Preventive maintenance becomes proactive. Downtime is reduced, and asset utilization improves.

Each of these use cases transforms something formerly intangible into something measurable, trackable, and impactful, enabling better business decisions, faster.

Tips for successfully structuring your data

Structuring unstructured data isn’t just about using the right tool—it’s about setting up the right approach, across teams and over time. 

Here’s how to do it well from the start

1. Start with one clear goal

Don’t try to structure everything at once. Choose a single use case, data source, or process that’s high-value and visible. Starting small allows you to prove value quickly and build confidence across stakeholders.

2. Prioritize clean inputs

Even the best extraction models will struggle if the inputs are noisy. Remove redundant characters, standardize formats, and pre-process text to improve downstream accuracy.

3. Keep humans in the loop—especially early

Whether you’re using rule-based tagging or AI classification, human validation is essential in the early stages. Review samples, score accuracy, and refine your logic before scaling.

4. Build for iteration, not perfection

What you extract today may not be what you need tomorrow. Structure your outputs flexibly so you can evolve your data model as the business changes.

5. Choose tools that match your team’s skillset

Whether you’re using Domo Magic ETL, Python scripts, or no-code AI platforms, the best tool is the one your team can actually use and maintain. Don’t overengineer.

6. Automate when confidence is high

Once your structured output is accurate and stable, build automation around it. Use workflows to trigger alerts, update dashboards, or initiate follow-ups based on structured outputs.

7. Validate and log everything

Track what’s being extracted or classified and log the results. This helps identify drift, monitor quality, and ensure the outputs stay aligned with your goals.

8. Watch for bias in text classification

If you’re using models to tag sentiment or categorize responses, be mindful of unintended bias in your training data. Periodically test for fairness across groups, languages, and formats.

9. Align outputs to business outcomes

Structured data is only useful if it’s actionable. Tie every field you extract or tag to a business question, workflow, or KPI. If you can’t use it—don’t extract it.

10. Share wins early and often

Showcase how structuring unstructured data created value—faster support, smarter reports, or proactive alerts. Momentum builds buy-in, and early wins help expand adoption across teams.

Bring your unstructured data into the conversation

Every day, your organization generates data that could answer strategic questions—data hiding in PDFs, transcripts, customer emails, or compliance reports. Left unstructured, that content remains invisible to your analytics stack. But structured correctly, it becomes part of the conversation. It fuels AI, triggers workflows, powers dashboards, and puts more meaningful context behind every decision.

This isn’t just about cleaning up data. It’s about expanding what’s possible with the data you already have.

With Domo, you don’t need a team of developers or a specialized NLP engineer to get started. You can use Magic ETL to clean and transform raw data, build Workflows to automate next steps, and tap into Domo AI to classify, extract, and surface insights from content that was previously out of reach. And because it’s all connected in one platform, the structured outputs flow seamlessly into dashboards, alerts, and apps—ready to act on.

When you structure your unstructured data in Domo, you’re not just organizing it. You’re activating it.

So whether you’re looking to reduce manual work, increase data visibility, or prepare for generative AI use cases, Domo helps you move faster—with confidence, clarity, and context.

Turn your unstructured data into structured, actionable insights in minutes—not months. Try Domo free today and see what’s possible.

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