Unstructured Data Management: Benefits, Challenges, and Tools

As the global volume of data continues to grow, most of it no longer fits into structured formats. In fact, by 2025, unstructured data is expected to make up nearly 80% of the world’s digital information, according to Seagate and IDC’s joint study.

The question is: how might it be possible to handle information you can’t structure and control and put it to use for your business benefit? That’s when unstructured data management – and professional data management services – come into play.

If you haven’t been paying much attention to unstructured data, we’re here to help you fix that. Learn why it’s important, how to make it work for you, and what tools to use – this article will answer any troubling questions you may have about unstructured data.

Article Highlights:

• You see unstructured data every day, in the form of chats, images, video, social media content, customer reviews, et cetera. And all of it can bring you business opportunities;
• According to Komprise, 38% of organizations need more visibility into their data usage and value;
• Azure, IBM, and Amazon all offer reliable tools to manage unstructured data – but you need to work with trusted experts to make the tools work.

What Is Unstructured Data?

Unstructured data is any type of information that is not stored in a traditional database or spreadsheet, including text (e.g., user reviews, documents, or social media chat history) and non-text content (e.g., media, visuals, and sound). Geographical and IoT streaming info are among the newer types of unstructured data.

This type of information is everywhere, and it’s growing fast. The global datasphere is projected to reach 181 zettabytes by 2028, with most of that growth driven by unstructured content.

But as this data grows more important, companies struggle to manage it effectively. According to the 2024 Komprise report, organizations face several obstacles when it comes to unstructured data:

• 40% highlight inconsistent metadata and tagging as a roadblock to automation;
• 38% struggle with a lack of visibility into data usage and value;
• 35% face challenges with data sprawling across hybrid environments.

As the volume and complexity of unstructured data continue to increase, so does the need for thoughtful and tech-enabled data management strategies. According to the same Komprise report, these are the unstructured data capabilities that companies are planning to prioritize in the near future: 

Managing unstructured data in the right way offers real potential for businesses ready to take action, and this article will show you where to start. But first, let’s take a step back and look at your full data landscape.

Structured vs. Unstructured vs. Semi-Structured Data

In the big picture, the totality of your corporate information comprises three data types: structured, unstructured, and semi-structured. To get the most value out of each, you should know the key differences between the three.

• Structured data is well-organized and most often contains text and numbers. In other words, it is already built in some structure (spreadsheets, PoS systems, SQL databases, etc.), which makes it easy to find specific information, categorize it, and compare data pieces. Examples: names, gender, age, billing info, addresses, etc.
• Unstructured data is not that simple. It usually comes in the form of a large block of text, an image, or a sound that you can’t directly insert into a spreadsheet. Examples: documents, customer feedback, social media posts, voice transcriptions, call center recordings, etc.
• Semi-structured data also can’t be readily entered directly into a database, but is still organized to some extent using categorization, meta tags, or hashtags. In other words, this information may be grouped together, but there is no structure within each group. Examples: emails, HTML, NoSQL databases, resource description frameworks (RDFs), et cetera.

7 Real-Life Unstructured Data Examples

Now, let’s take a look at real-life examples of unstructured data types. This overview will help you understand how much unstructured data your business stores compared to structured data sets.

1. Documents

Let’s start with business documentation, the broadest category of content. Reports, presentations, and legal documents contain a treasure trove of unstructured data. Even though these documents form a significant portion of the organization’s knowledge base, much of that information may remain unused because it’s not structured.

To get the most out of text documentation, companies should manage unstructured data with AI using text analysis. This technique – often powered by NLP (Natural Language Programing) and a large language model (LLM) – helps to identify valuable insights hidden in text and turn them into actionable knowledge.

2. Webpage Content

Any web page likely contains text and images. The bigger the website is, the more unstructured data it includes: videos, audio, and fill-in forms. 

While websites are created with HTML (semi-structured data), the code itself can’t grasp the page’s full meaning and value. However, web pages may contain insights on customers and competitors that can help companies understand the market better. 

In this case, machine learning is the best technology to use, as it helps mine valuable information, while also managing and continuously tracking it across multiple sources.

3. Media Content 

Nowadays, anyone can produce images, video, or audio content – from smartphone users all the way up to entertainment companies. This data is stored in databases, but we still don’t always have a clear picture of what the content is all about. Dealing with unstructured data in this context may be of value to various industries. 

For example, video analysis in a shop may help retailers better understand customer behavior patterns more deeply. But to ensure quality, protect privacy, and establish the right controls, companies need to adopt the right practices. 

Applying NLP to extract text from audio and using sentiment analysis are key steps to achieve desirable results.

4. Social Media Activities

Social media is everywhere: photos, videos, comments, opinions, likes, and statistics. It may be somewhat categorized using hashtags, but social media content is for the most part unstructured. And don’t forget that interaction between businesses and customers on social media platforms is active around the clock. 

This active participation can be a gold mine for business owners who want to know their target audience better and understand typical preferences and behaviors. One of the best machine learning methods to use for that is the event detection algorithm.

5. Customer Feedback

Positive customer feedback demonstrates that you’re moving in the right direction, while negative feedback hints at weak spots that need your attention. In any case, reviews are one of the most valuable indicators of business success and one of the most difficult to analyze.

Feedback may come in the form of a Google review or a phone call. No matter what it looks like, any review can take a unified form with the help of unstructured data management tools.

6. Survey Responses

Marketing or employee questionnaires often contain open-ended questions, which can provide a better understanding of the interviewee’s opinion or decision-making process. And this text, unlike closed-ended answers, is unstructured information, which is usually harder to process.

To interpret the data faster and more productively, you should use generative AI for data management, saving lots of time and reducing the risks of human factors impacting the research.

7. Chat Recordings

These days, communication takes many forms and often happens online via messengers, video, and audio conferencing tools. For example, if your company has a customer support team, it processes large volumes of data daily, often containing the most valuable insights about your customers.

Using AI technology, you can first transcribe voice into text. Speech emotion recognition will help categorize the customer’s mood, while natural language processing will identify conversation themes and products mentioned.

Business Benefits of Unstructured Data Management

As you can see, unstructured information can be found in every business department. But is it worth your effort? 

First of all, what do we mean by managing unstructured data? These are activities aimed at collecting, organizing, and structuring the files which have no established structure in the first place.

Now, let’s move on to the list of potential benefits you stand to gain from if you decide to manage all data flows, including unstructured ones.

1. Optimized Workflow

With a data management system set up correctly, your employees will always know not only where to find information they need, but also how to use it. It reduces extra time needed to find a document and, subsequently, allows a team to focus on the core responsibilities.

2. Improved Decision-Making

“Information is a source of learning. But unless it is organized, processed, and available to the right people in a format for decision making, it is a burden, not a benefit”. – William Pollard

Only complete information transparency can lead a business to fruitful results. Make sure you’ve done everything possible to gather data from each department and provide your team with a clear picture of the overall vision. Soon, you’ll begin to see how it can lead your employees to quick and effective results based on facts.

3. Polished Customer Experience

Unstructured data management tools allow you to constantly monitor all your client communication channels: phone calls, live chats, reviews, emails, etc. The next step is to automatically transform client requests into tickets for your support team: there’s no faster way to respond to your clients’ needs.

In the same way, you can follow up with the online content that mentions your brand’s name, allowing you to track your presence on all social media platforms in real time with no extra effort. 

And it pays off: companies offering a superior customer experience can charge up to 16% more for their products or services.

4. Safer Information Environment

The more data gets overlooked, the more future security threats your business may face. Unstructured files often contain sensitive data – but, at the same time, can often remain overlooked and unprotected. Visibility across your data infrastructure is key to preventing breaches – a critical priority, given that the average cost of a breach reached USD 4.45 million in 2023.

How can you protect something if you don’t know it exists? Complete information transparency with the help of unstructured data governance and regular backups significantly reduce the risk of cyberattacks.

5. Clear Market Vision

Just as you can monitor your own data on social media, you can monitor that of your competitors. Every company leaves a trace on the Internet; you only have to follow it. Social media and blog posts, press releases, and customer reviews – you will quickly analyze all this using AI and machine learning technologies.

By analyzing others, you will better understand your place in the market and discover areas for improvement. What is trending now? And what may come next? Information is one of the key factors for your market growth.

6. Timely Regulatory Compliance

What if some of your business documentation doesn’t meet the necessary legal regulations? The risk is much higher if you don’t manage your company’s data properly. But if your unstructured information is always on display, you’ll be aware of areas that need your attention the most.

In fact, 44% of organizations highlight compliance with industry and internal policies as one of the top drivers for improving unstructured data management.

How to Handle Unstructured Data

Now that you understand the importance of managing unstructured data, you may be wondering how to make it all happen. Below, we’ve put together a six-step guide that shows you the unstructured data management process from start to finish. 

1. Define Your Goal and Required Type of Content

Gathering and structuring information is not just some fancy trend – it should support a clear business goal and align with your broader data engineering strategy. First, ask yourself what your aims are in getting your unstructured data in order.

Here are some ideas:

• Analytics of customer preferences;
• Social media presence and PR;
• Gaps in organization processes;
• Wear-and-tear analytics of the production equipment;
• The team’s productivity, etc.

You probably don’t need to collect all the data right away, but instead, focus on a certain type that fits your current needs. Start there and gradually move your focus from one issue to another.

2. Consider Your Business Needs and Capabilities 

Now let’s look broadly at your business. Before diving into any solution, it’s often helpful to get a second opinion – this is where data engineering consulting can bring real value by aligning technical decisions with long-term business goals.

Here are a few key questions to consider:

1. What security level is required for this type of data? Several industries, such as finance and health care, need extra security protection.
2. What is your current budget for data management? Will it change in the future?
3. Are there crucial business strategy points to keep in mind? For example, prospective startups should be ready for intensive documentation growth in their organization. 

3. Take Care of Your Storage Space

Your answers to the questions above will help you decide on the storage space to use. The most popular unstructured data storage tools follow:

• A public cloud is a go-to option for many companies. It has the easiest accessibility and scalability: when your data grows, you can buy more space in no time. The most popular cloud-based services are Google Cloud, Azure, and Amazon, and with the right cloud development approach, you can customize storage, security, and access to fit your business needs. 
• On-premises hardware is a common choice for those concerned about data security issues. A company buys a server and stores critically important files in-house. But you should also consider that maintaining technical support for this will usually require a bigger budget.
• The hybrid approach includes using both on-site and cloud-based servers for different purposes. It’s the most obvious option for enterprises storing a lot of data, some of which requires advanced security layers. 

To make your storage user-friendly, provide it with metadata and a search feature with filters. 

4. Clean Up Collected Information

Unstructured data usually doesn’t follow any regular order and may contain mistakes or different character encoding types that can create challenges for AI algorithms. Hashtags, misspellings, HTML tags – all of it should ideally be cleaned up before analysis starts. On top of that, data silos make things worse by scattering this messy data across multiple systems or teams, limiting visibility and slowing down preparation.

Pay attention to these points:

• Spelling. And it’s not only about wrong spelling. Social media, for example, is full of slang terms and nonstandard word forms (like “tnx” or “luv”). The same goes for merged words (“sunnyweather”) and letter transposition (“tahnk you so mcuh”).
• Abbreviations. These can be formal (names of organizations) and informal (social media reductions). Depending on the type of content, you should consider if there are several meanings for one abbreviation in the context.
• HTML tags. These do little except create extra noise in the text, and bring no value to the overall meaning for anyone other than a web browser.
• Long paragraphs. If you focus on business reviews, longer paragraphs usually concern detailed customer opinions. To distinguish valuable insights, you should first break a paragraph into several small ones.

5. Analyze Data With AI/ML Technology 

Generally speaking, you can manage unstructured data with AI in two ways: by using ready-made solutions available on the market or creating one with a development team, depending on your initial goals. Sometimes, it’s better to have a custom tool if your business needs differ from the standard market demands.

All solutions currently available on the market are based on machine learning (ML) technology, which can be used to classify unstructured data sets by their topic, general message, point of view, purpose, and more. But the capabilities of machine learning are much broader – we’ll tell you about the possible applications we’ve encountered through our experience.

6. Use the Info to Your Advantage

Unstructured data analysis can often bring unexpected results that hint at the weak spots in your organization. This is your golden opportunity – and we suggest you start using these insights to fill in existing gaps as soon as possible.

Using ML solutions to analyze unstructured data, CHI Software clients have reduced equipment operating costs, automated business processes, and expanded target audience segmentation to offer a personalized experience to the customers.

Top Tools to Manage Unstructured Data

Will a ready-made solution be enough to meet your company’s needs? Let’s review some popular unstructured data management solutions in order to help you make a decision.

AI & Natural Language Processing

IBM Watson

A comprehensive platform for text analytics, sentiment detection, and visual insight generation.

Key features:

• AutoAI to automate data preparation and modeling;
• Deep text analytics for unstructured content;
• Interactive dashboards to track insights;
• Natural language processing for emotional tone detection.

Best for: Enterprises with complex NLP/AI needs and in-house data teams.

Azure AI Services

Microsoft’s flexible set of APIs for speech, vision, language, and search. Great for real-time interaction and enterprise-level deployments.

Key features:

• Speech and language APIs to extract intent and sentiment;
• Vision APIs for image and video recognition;
• Decision APIs to personalize content and detect anomalies;
• Integration via REST APIs;
• Scalable cloud infrastructure for enterprise use.

Best for:
Mid-to-large businesses using the Microsoft ecosystem or needing modular cloud AI.

Amazon Comprehend

AWS-powered NLP service that automatically identifies key phrases, entities, sentiment, and language from text.

Key features:

• Entity recognition and categorization;
• Sentiment analysis with custom classification;
• Syntax analysis and language detection;
• Topic modeling across large datasets;
• Integration with other AWS tools (S3, Redshift, etc.).

Best for:
Companies with infrastructure on AWS seeking cloud-native NLP tools.

How to manage unstructured data becomes a core concern when working with these tools, from raw text and customer reviews to logs, documents, and multimedia. That’s why you need cleanup, parsing, and transformation solutions.

Data Cleanup & Transformation

OpenRefine

An open-source tool ideal for exploring, cleaning, and transforming messy datasets, especially useful for inconsistent tags, spellings, or formats.

Key features:

• Clustering to detect duplicates and variations;
• Expression language (GREL) for advanced transformations;
• Undo/redo history for safe experimentation;
• Import/export support for CSV, JSON, TSV, and more;
• Extension support for reconciliation with external sources.

Best for:
Small teams and analysts needing hands-on data cleaning without coding.

Document Parsing & Metadata Extraction

Apache Tika

A robust content analysis toolkit that extracts text and metadata from a wide variety of file formats – great for document-heavy environments.

Key features:

• Support for 1,000+ different document types (PDF, DOCX, HTML, etc.);
• Built-in language detection;
• Metadata parsing for indexing and classification;
• OCR integration for image-based documents;
• Easy integration with content management systems and pipelines.

Best for:
Legal, insurance, and academic teams working with large volumes of documents.

Deep Text Analysis

Provalis WordStat

A high-end module for content analysis, often used in research and policy environments, where interpretability matters.

Key features:

• Processing speed: up to 25 million words per minute;
• Explorer mode for topic detection and keyword mapping;
• Import support for Word, Excel, XML, PDF, et cetera;
• Relationship mapping between words, phrases, and concepts;
• Built-in dictionaries and thesaurus for automatic tagging.

Best for:
Researchers, think tanks, and institutions handling qualitative text analysis.

Our Experience with Unstructured Data Solutions

Every business has unique needs and characteristics, requiring custom tools for dealing with unstructured data. From our experience, we can tell you that the final set of features may vary greatly. Here are several examples.

Automated Reporting Software

One of our clients is a large consulting firm delivering market intelligence to enterprise customers. With dozens of analysts preparing reports daily, the process was slow, inconsistent, and heavily manual. Disconnected systems and messy inputs are clear examples of unstructured data management challenges.

Our team applied big data development expertise to build a centralized platform that automates data collection, validation, and visualization. As a result, reporting became faster, more accurate, and accessible to everyone who needed it.

For more exciting details about this project, read our original case study in full.

Image Recognition Service

Our client, a Japanese preschool photography service, needed a mobile-first solution to simplify how educators capture and report children’s daily activities. The project aimed to make communication with parents more transparent through real-time photo reporting, powered by facial recognition software.

The CHI Software team designed an image recognition app that runs entirely on mobile devices, overcoming performance limits without needing to utilize a complex back-end infrastructure. We addressed key accuracy and speed requirements while solving the challenges of storing and managing unstructured data like photos and face embeddings on-device without compromising security or user experience.

Discover how we use computer vision to simplify and speed up routine tasks.

Auto Insurance Solution

Another of our clients is a taxi service trying to save time identifying the reasons for vehicle deterioration, which significantly impacts their maintenance, repair and insurance expenses. This issue required real-time data collection of different parameters, including behavioral and sensor-based information, raising concerns around unstructured data privacy.

Our team created a solution that analyzes the vehicle’s vibration level, speed, driving and aggressiveness scores, and more. It gives a complete overview of the trip and allows our client to notice and predict operational “blind spots” and plan expenses beforehand.

Check out this case study in full via the link!

Final Thoughts on Unstructured Data

Unstructured data is here to stay, and it will only extend in the future. These are the key takeaways from this article:

• Unstructured data is comprised of all your business documents, social media posts, and customer feedback in voice and text forms, and there’s hardly a limit to how big its volume can grow;
• 99 percent of businesses face challenges in unstructured data management;
• You can’t manage unstructured content without knowing why you need to do it in the first place;
• Your data management budget directly impacts the amount of data you’ll be able to collect and process;
• All unstructured data management tools are based on machine learning and artificial intelligence technologies;
• With unstructured data, you potentially can improve your internal working processes and your relations with clients, regardless of the niche or industry you work in.

FAQs

• How do I know if challenges of managing unstructured data are slowing down my team?

  Look for signs of friction:
  
  - Employees spend too much time searching for documents, emails, or customer feedback;
  - You often hear “I didn’t know we already had this info”;
  - Decision-making relies more on guesswork than actual input.
  
  If projects are delayed due to missing or messy information, chances are that unstructured data is getting in the way.

• What types of unstructured data should I prioritize first?

  Start with what directly affects your operations or customers, which could be:
  
  - Customer support chat logs or call transcripts;
  - Social media mentions and product reviews;
  - Internal documentation in scattered folders.
  
  Ask yourself: “What are people constantly asking for and struggling to find?” That should be your starting point.

• Do I need a custom solution for working with unstructured data, or will off-the-shelf tools work?

  It depends on two things: complexity and scale.
  
  - If you deal with diverse formats and want integration with existing systems, a custom solution makes sense.
  - If your needs are more standardized (e.g., basic NLP or metadata extraction), off-the-shelf platforms like Azure or AWS may be enough for your business.
  
  A hybrid approach often works best: start with ready-made tools, then extend where needed.

• Is AI really necessary to manage unstructured data, or is it optional?

  AI isn’t mandatory, but it makes unstructured data manageable at scale. Manual methods can work if your data volume is low and your team is highly specialized.
  
  But as soon as the number of documents, images, or messages enters into the thousands, AI becomes the only viable way to analyze and extract insights without drowning in the noise.

• How long does it take to see results from unstructured data governance?

  It depends on the scope, but the following improvements can show within just a few weeks:
  
  - Faster internal search and document access;
  - Better responses in customer support;
  - Fewer duplicated efforts across teams.
  
  More advanced outcomes of successful unstructured data management, like trend forecasting or automated workflows, usually take a few months and are normally rolled out gradually, in stages.