What’s the difference between structured, semi-structured, and unstructured data?
In today’s digital age, data has become the lifeblood of modern business. Access to quality data is driving innovation, improving insight, and enhancing decision-making within the most agile organizations.
As more global enterprises harvest the vast amounts of data available, it’s crucial to understand the different types of data and the documents that store it.
Jun 21, 2023 by Craig Woolard
Structured, semi-structured, and unstructured data each possess distinct characteristics that impact how businesses operate. Each type can also impact decision-making differently, so having a deeper understanding of the nuances and implications is critical for modern business leaders to grasp.
This blog will explore the distinctions between structured, semi-structured, and unstructured data. We will explore each data type’s characteristics, challenges, and unique opportunities for businesses operating in the data-driven era. Finally, we will discuss how enterprises can use modern AI-driven intelligent document processing (IDP) to navigate the growth of unstructured data and “unlock” the intelligence “stuck” in documents.
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Structured data vs. semi-structured data vs. unstructured data
In the world of big data, information can be grouped into two distinct categories: “structured” and “less structured.” Structured data is information that follows predefined “rules” or “guidelines,” such as data points organized into a database. “Less structured” information is essentially everything else.
“Semi-structured” and “unstructured” data is information that can be grouped into the category of “less structured.” When information is locked away in documents, for example, these terms can often be confusing because all documents (structured, semi-structured, and unstructured) are generally considered “unstructured data.” Having said that, structured data (as in data in a database) is not considered “unstructured” data.
Unstructured data requires processing with AI before you can store or query its contents in structured formats like a database table or JSON. However, the key understanding to have is that “data,” no matter where it is stored, is your organization’s most valuable asset. There is valuable information in customer emails, social media posts, chat transcripts, competitor websites, invoices, contracts, and many other types of business documents. But the value within remains unobtainable if you can’t unlock the intelligence trapped in document-centric processes.
Critical data points in business documents—whether “stuck” in structured, semi-structured, or unstructured documents—are the valuable intelligence your organization must “unlock.” Eliminating the “friction” in the way of potential business intelligence is the key to unlocking enhanced employee and stakeholder decision-making. In the next section, we’ll take a closer look at the differences between structured, semi-structured, and unstructured data types and investigate some common business use cases for each one.
The goal of intelligent document processing is to convert structured, semi-structured, and unstructured documents into structured data. Therefore, we will begin with the most accessible data type: structured data. This will deepen your understanding of the unique challenges and opportunities for each data type and the documents that store them.
What is structured data?
For all practical purposes, everything people try to extract from documents is “structured data.”
Structured data is any information organized into a format that’s easy for traditional machines to find. Documents with structured data typically follow a “fixed” layout and usually contain fields with text, values, and other organized data—such as fixed forms, spreadsheets, or databases.
Examples of documents with structured data
Structured data typically includes text and other values stored in tables or databases. For a comprehensive list of the most common examples of structured data in business documents, check out the table below:
|Relational database tables
|Tables with predefined columns and rows representing attributes or fields containing individual data, such as text, numerical values, or code.
|Spreadsheets (e.g., Excel)
|Machine-printed text and numerical values are organized into cells, rows, and columns for easy sorting, filtering, and analysis.
|Structured Query Language (SQL) tables are used in relational databases to organize and store structured data, enabling efficient querying and manipulation.
|ERP system data
|Structured data is captured and stored in Enterprise Resource Planning (ERP) systems and covers various business processes such as sales, inventory, finance, and human resources.
|Inventory management systems
|Data captured in Inventory Management Systems include product codes, quantities, prices, and physical warehouse locations that are structured and organized for efficient tracking and management of inventory.
|Financial transaction records
|Structured financial data that includes dates, amounts, account numbers, and transaction types, which are essential for financial analysis and reporting.
Advantages of structured data
The biggest advantage of documents with structured layouts is that the information is already designed and optimized for quick and easy processing by computer systems. This also makes the data easily searchable with traditional “rules-based” automation tools.
Because the data is highly organized and “structured,” it’s easier for legacy automation software like robotic process automation (RPA) to find critical data points. This also makes it easier for widely accessible but outdated technologies, such as legacy Optical Character Recognition (OCR), to scan and capture the data faster than manual human effort.
Simply put, structured documents require less advanced technology, which can benefit organizations that are reliant on older heritage document processes and legacy data systems.
However, these are not the only organizations that reap the benefits. Because structured documents are easier to process, nearly every industry can benefit from the value they store.
Here are some more strategic advantages of structured data:
- Structured data enables efficient data retrieval and querying.
- Structured data is easy to store, organize, and access for further processing.
- Structured data facilitates consistency and accuracy to ensure good data quality.
What are the limits of structured data?
Researchers at the IDC discovered that more than half of the documents enterprises process have structured layouts. However, most organizations cannot rely on structured data alone.
For example, what about handwriting? Fixed forms and other structured documents typically have additional fields reserved for signatures and handwritten checkmarks.
These documents may be easier for traditional automation tools to process, but they provide little flexibility. Plus, if they do contain unstructured elements, such as handwriting, then traditional “rules-based” technologies will struggle, which leads to critical information being missing.
When dealing with modern omnichannel data sources that lack a well-defined structure, such as social media, email, or even handwritten content, it’s crucial to understand the limits of traditional rules-based extraction techniques.
Understanding these limitations can help you make a more informed decision about the strategies that unlock the business value in structured and less structured documents.
What is semi-structured data?
Semi-structured data is information that does not exist in a structured or fixed format per se, such as a database or spreadsheet, but may have some attributes that make it easy to find. Some examples include XML documents, JSON files, and NoSQL databases. It’s also worth mentioning that there are no documents that contain “semi-structured data,” but there are plenty of examples of semi-structured documents.
Examples of documents with semi-structured data
When we talk about “semi-structured” in the context of intelligent document processing, we are referring to documents where the same pieces of information are present across a variable layout.
Documents with semi-structured data conform to a template, but the information layout is flexible and likely varies from document to document. In the context of IDP, we are not talking about documents that contain XML or JSON data. We are talking about business documents made up of plain text, tables, and other elements that are based on evolving templates.
Since semi-structured documents do not have “fixed” or standardized layouts, organizations handling them may need help predicting where the information of interest is located. Examples of semi-structured documents include invoices, purchase orders, bill-of-materials (BOM), receipts, and loan applications.
Check out the table below for a comprehensive list of semi-structured data examples:
|Flexible, schema-less data storage. Accommodates semi-structured or unstructured data. NoSQL databases, such as MongoDB, Cassandra, and CouchDB, store data in a flexible, schema-less format. This means that the data in NoSQL databases can have varying structures or fields across different documents or records. Semi-structured data in NoSQL databases can include diverse datasets such as user profiles, product catalogs, sensor data, social media feeds, and unstructured text documents.
|XML (eXtensible Markup Language) is a markup language used for storing and transporting structured data. It uses tags to define elements and attributes to provide additional information about those elements. XML is versatile and allows for custom data structures. Semi-structured data in XML can include documents such as invoices, scientific data, and electronic health records. It is widely used for data interchange, configuration files, and data representation in various domains.
|HTML (Hypertext Markup Language) is the standard markup language for creating web pages. HTML documents consist of tags and attributes that structure and present content on the web. HTML is primarily used for defining the structure and layout of web pages, the content within HTML documents can vary in structure, formatting, and data representation. Examples of semi-structured data in HTML include web scraping results, online articles, blog posts, and forum threads.
Advantages of semi-structured data
Semi-structured data bridges the gap between structured and unstructured data types. Understanding the distinct advantages can help business leaders appreciate the value of incorporating semi-structured data into their document processing workflows.
Here are some of the key benefits of semi-structured data:
1. Data analysis
Semi-structured data often contains more contextual information than traditional structured data. Examples of this include metadata or tags that provide additional context that can improve the accuracy and relevance of data analysis.
Semi-structured data is more flexible in data storage and data management scenarios compared to rigidly structured documents. Since semi-structured documents do not follow a strict, predefined template, incorporating new data types into existing databases or data pipelines is easier.
Semi-structured data is more scalable than structured data. Semi-structured data can be stored and processed using distributed computing systems in a variety of locations, such as existing on-prem databases, data lakes, and cloud storage. This flexibility and scalability enable greater agility to handle massive amounts of data.
Semi-structured data easily integrates with other types of data, such as unstructured data, making it faster to combine and compare data from multiple sources.
Challenges of semi-structured data
While semi-structured data offers significant advantages, it also presents unique challenges that business leaders must consider when working with diverse data types. Understanding these challenges is essential to effectively managing and leveraging the potential of this data type.
Here are the key challenges with semi-structured data:
1. Data extraction and integration
Semi-structured data can be more challenging to extract and integrate than structured data. It often requires specialized tools such as intelligent document processing (IDP) with custom Context-aware OCR data extraction data extraction processes to capture the relevant information from various formats and sources. Because semi-structured documents have varying templates, their improved flexibility can make data integration more complex. Additional efforts and sophisticated AI tools are needed to harmonize and align the different data elements.
2. Data quality and consistency
Ensuring data quality is much more demanding in semi-structured data environments. The lack of strict data models and schemas can lead to inconsistencies, duplications, and discrepancies that compromise the quality of the data. Cleaning and standardizing semi-structured data requires either extra attention from humans or sophisticated AI technology to address variations in data formats, missing fields, and inconsistent datasets.
What is unstructured data?
Unstructured data is information that is not organized into any particular format and may be completely “free-form.” Examples of unstructured data might include photos, videos, emails, books, social media posts, health records, and legal contracts.
Examples of documents with unstructured data
Unstructured documents are “unfixed” and do not follow a templated design, a fixed layout, or “rules.”
Gartner defines unstructured data as machine-printed or handwritten content lacking predefined rules or guidelines that computers traditionally use to identify. Unstructured data could be free-form text, such as the body of an email, or non-textual, such as a photo containing handwriting—but it could also exist in a non-relational database—such as NoSQL.
The table below lists some of the most common examples of unstructured data in business documents:
|Handwritten text or documents that lack a predefined structure. Unstructured data includes personal notes, letters, signatures, and any other text produced by hand. Examples: handwritten letters, personal diaries, meeting minutes, and signatures.
|Images, photographs, or graphics that contain handwriting, machine-printed text or important symbols. These data points are unstructured as they don’t adhere to a predefined format. Examples: logos, illustrations, and digital photos of handwritten notes or scanned images of business documents.
|Unstructured textual data such as articles, books, emails, and reports. This data lacks a predefined structure and can vary in length, language, and formatting. Examples: PDF news articles, research papers saved as a Word document, and email messages.
|Unstructured notes, memos, or annotations created by individuals. They can contain text, diagrams, sketches, or any other form of personal record-keeping. Examples: personal journals, meeting notes, brainstorming sessions.
|Transcriptions of spoken language, such as interview recordings or speech-to-text conversions. These data points capture spoken words, including dialogues, speeches, and conversations. Examples: interview transcripts, meeting minutes, voice recordings, and podcast episodes.
|Unstructured email messages and their attachments. Emails can contain text, images, and various file attachments. They often exhibit varying structures and content types. Examples: personal emails, business correspondence, newsletters.
|Social Media Posts
|Unstructured data shared on social media platforms, encompassing text, images, videos, or a combination thereof. It includes user-generated content, hashtags, and engagement metrics. Examples: tweets, Facebook posts, Instagram stories.
|Websites contain both structured and unstructured data. While the overall structure, using HTML and CSS, is semi-structured, the unstructured data within websites includes text content, tables, images, videos, and other media elements. Examples: web pages, blog sites, and online forums. Unstructured data extracted from websites using web scraping techniques includes product listings, customer reviews, and news headlines.
|Unstructured data captured by sensors or IoT devices, including raw measurements, signals, or streams of data. It often represents real-world phenomena and lacks a predefined structure. Examples: temperature readings, accelerometer data, air quality measurements.
Advantages of unstructured data
Understanding the distinct advantages of unstructured data can help business leaders “unlock” the business value stuck in unconventional documents.
Here are some of the key advantages you get when you leverage unstructured data:
1. Unlocked business value
You have a lot of unstructured data. Unstructured data is a business’s most valuable asset. It is the raw conceptual material and intellectual property that are a goldmine of untapped information for advanced analytics. The research estimates that unstructured data accounts for a whopping 80-90% of all new enterprise data. Yet only 18% of businesses are actually able to take advantage of it. The other 82% need help unlocking the business intelligence in this valuable resource.
2. Intent and sentiment analysis
Unstructured data opens the door to advanced analytics techniques such as sentiment and intent analysis. For example, customers use email for all kinds of requests, and sometimes they have more than one request. It’s interesting to know that every 24 hours, there are more than 3 billion business emails sent and received worldwide. Intent classification detects customer intent in emails, and then it can automatically categorize emails based on intent to help automate email processing and triaging.
Sentiment analysis provides insights into customer opinions and emotions, allowing organizations to gauge brand sentiment, improve customer satisfaction, and address issues promptly.
3. A big competitive advantage
By leveraging advanced analytics techniques, businesses are able to uncover patterns, trends, and relationships that might go unnoticed. These invaluable insights enable organizations to make data-driven decisions that give them a competitive edge.
Challenges with unstructured data
Your unstructured data offers the most significant competitive advantages of all the data types. But it also presents the widest set of challenges to enterprise organizations. Understanding the challenges is essential to unlocking the business value stuck inside unstructured documents.
Here are the key challenges involved with unlocking unstructured data:
1. Data extraction
Since the information in unstructured documents does not follow a predictable pattern, their contents are entirely “hidden” from traditional data extraction methods.
2. Handwriting recognition
Standalone Optical Character Recognition (OCR) is a ‘rules-based” technology that recognizes machine-printed letters, numbers, symbols, and even some handwriting. However, traditional OCR technology still struggles to accurately process handwriting. If your OCR cannot “see” unstructured handwritten signatures, tables, and other nuances accurately, someone must manually review the documents to extract the information. This stops your automation.
3. Data accuracy and quality
OCR best suits high-quality scanned images with higher contrasts between texts and backgrounds. But if the text is splotchy or the scan is low-quality, OCR’s accuracy drops dramatically. Even with the best scanners and the best document quality, you only get 60% accuracy with traditional OCR at best. For enterprises looking to improve data processing speed, accuracy, and agility in unstable digital markets, rules-based approaches simply don’t cut it.
4. Data privacy and security
Unstructured data is the lifeblood of your business. It should never appear on public platforms. Proprietary data, such as chat transcripts, emails, multimedia content, patent applications, legal contracts, and even programming languages, can contain sensitive information with valuable intellectual property. Protecting data from unauthorized access, implementing proper encryption methods, and complying with relevant data protection regulations are essential.
5. Scalable storage concerns
Unstructured data, typically large in volume and diverse in formats, presents challenges in storing, processing, and retrieving the data efficiently. Traditional storage systems and analytical tools may need help to handle the scale and complexity of unstructured data, requiring businesses to invest in scalable storage solutions and advanced data processing technologies.
6. Integration challenges
Unstructured data often need more standardized formats and schemas, which may make integration with existing systems more challenging. Integrating unstructured data with structured or semi-structured data sources requires careful data mapping, transformation, and consolidation techniques to ensure seamless data flow and compatibility across systems.
Structured data facilitates efficient analysis and supports business intelligence by enabling organizations to derive crucial insights quickly.
Semi-structured data requires a more nuanced approach since businesses need to extract relevant information from various formats and sources before they can uncover potential insights.
Unstructured data presents both a challenge and an opportunity. Unlocking its potential provides the business intelligence that will give agile enterprises a real competitive advantage.
But how can enterprises truly know whether or not data has potential value if there’s too much “friction” to unlock it? The answer is Intelligent Document Processing (IDP).
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