Structured vs. Unstructured Data: Key Differences Explained

Structured data consists of clearly defined data types with patterns that make them easily searchable, while unstructured data—“everything else”—is composed of data that is usually not as easily searchable, including formats like audio, video, and social media postings.

Structured data analytics is a mature process and technology, whereas unstructured data analytics is a nascent industry with a lot of new investment in research and development.

The structured data versus unstructured data issue within corporations is deciding if they should invest in analytics for unstructured data and determining if it is possible to aggregate the two into better business intelligence.

For more information, also see: Data Management Platforms

Key Differences Between Structured and Unstructured Data (Chart)

Structured Data Unstructured Data
Organized information Diverse structure for information
Quantitative Qualitative
Requires less storage Requires more storage
Not flexible Flexible
ID codes for databases Videos and images

What is Structured Data?

Structured data usually resides in relational databases (RDBs). Fields store length-delimited data like phone numbers, Social Security numbers, or ZIP codes, and records even contain text strings of variable length like names, making it a simple matter to search.

Data may be human- or machine-generated, as long as the data is created within an RDB structure. This format is eminently searchable, both with human-generated queries and via algorithms using types of data and field names, such as alphabetical or numeric, currency, or date.

Common relational database applications with structured data include airline reservation systems, inventory control, sales transactions, and ATM activity. Structured Query Language (SQL) enables queries on this type of structured data within relational databases.

Some relational databases store or point to unstructured data, such as customer relationship management (CRM) applications. The integration can be awkward at best since memo fields do not lend themselves to traditional database queries. Still, most of the CRM data is structured.

For more information, also see: Top Data Warehouse Tools

Benefits of Using Structured Data

Easy to Use

Business users who understand what the subject matter of the data is and how it is related to their infrastructure can easily understand how to structure their data. Tools such as Excel or Google Sheets make structured data easy, or more advanced users can lean further into SQL or business intelligence tools.

Convenient Storage

Because structured data is organized, it is commonly stored in data centers for easy access of the data. The data warehouses hold their own space for businesses that choose to use it.

Instant Usability

Structured data is organized, making it easy for a company to find exactly what they are looking for. With this method, a company can begin using the data instantly.

Disadvantages of Structured Data

Limitations on Use

Due to the organization style of structured data, it is more difficult to have flexibility or varied use cases.

Limited Storage

Structured data is stored in specific spaces of data warehouses. While accessing the data is easy, scalability can be difficult. Changes within data warehouses can become hard to manage. Using cloud data centers help with the storage problems.

High Overhead

Data centers or other storage for structured data can become expensive and be part of the structured data ordeal. Again, cloud data centers are recommended, but the storage can still require significant work to keep the data maintained properly.

7 Structured Data Examples

  • ZIP codes
  • Phone numbers
  • Email addresses
  • ATM activity
  • Inventory control
  • Student fee payment databases
  • Airline reservation and ticketing

10 Common Structured Data Tools

  • Google’s Structured Data Testing Tool
  • Yandex Structured Data Validator
  • Markle’s Schema Markup Generator
  • SEO SiteCheckup
  • Bing Markup Validator
  • Google Email Markup Tester
  • RDF Translator
  • JSON-LD Playground
  • Schema Markup Generator
  • Microdata Tool

For more information, also see: What is Big Data Analysis

What is Unstructured Data?

Unstructured data is essentially everything else. Unstructured data has an internal structure but is not structured via predefined data models or schema. It may be textual or non-textual and human- or machine-generated. It may also be stored within a non-relational database like NoSQL.

Typical human-generated unstructured data includes:

  • Text Files: Word processing, spreadsheets, presentations, emails, and logs.
  • Email: Message field
  • Social Media: Data from Facebook, Twitter, and LinkedIn.
  • Websites: YouTube, Instagram, and photo sharing sites.
  • Mobile Data: Text messages and locations.
  • Communications: Chat, IM, phone recordings, and collaboration software.
  • Media: MP3, digital photos, and audio and video files.
  • Business Applications: Microsoft Office documents and productivity applications.

Typical machine-generated unstructured data includes:

  • Satellite Imagery: Weather data, landforms, and military movements.
  • Scientific Data: Oil and gas exploration, space exploration, seismic imagery, and atmospheric data.
  • Digital Surveillance: Surveillance photos and video.
  • Sensor Data: Traffic, weather, and oceanographic sensors.

Benefits of Using Unstructured Data

Limitless Use

Use cases for unstructured data are significantly larger than structured data due to its flexibility. From social media posts to scientific data, unstructured data gives companies the flexibility to use the data how they want.

Greater Insights

When a company has more unstructured data than structured data, there is more data to work with. Unstructured data may be difficult to analyze, but through processing, a company can benefit from the data.

Low Overhead

Because of the ability to store unstructured data at data lakes, a business can save money with how they choose to store the data.

Disadvantages of Unstructured Data

Hard to Analyze

If a company uses unstructured data, it is more difficult to take the raw data and analyze it despite its flexibility.

Data Analytic Tools

Unstructured data cannot be managed by business tools. Its inconsistent nature makes it more difficult than structured data.

Numerous Formats

Unstructured data comes in many different forms, such as medical records, social media posts, and emails. This information may be challenging with analysis.

12 Unstructured Data Examples

  • Text files
  • Email
  • Social media
  • Website
  • Mobile data
  • Communications
  • Media
  • Business applications
  • Satellite imagery
  • Scientific data
  • Digital surveillance
  • Sensor data

10 Common Unstructured Data Tools

  • MonkeyLearn
  • Microsoft Excel
  • Google Sheets
  • RapidMinder
  • Power BI
  • Tableau
  • MongoDB Charts
  • Apache Hadoop
  • Apache Spark

For more information, also see: Top Data Analytics Tools 

Semi-Structured Data’s Role

Semi-structured data maintains internal tags and markings that identify separate data elements, which enables data analysts to determine information grouping and hierarchies. Both documents and databases can be semi-structured. This type of data only represents about 5–10% of the data pie, but has critical business usage cases when used in combination with structured and unstructured data.

Email is a common example of a semi-structured data type. Although more advanced analysis tools are necessary for thread tracking, near-dedupe, and concept searching, email’s native metadata enables classification and keyword searching without any additional tools.

Email is a huge use case, but most semi-structured development centers on easing data transport issues. Sharing sensor data is a growing use case, as are web-based data sharing and transport, including electronic data interchange (EDI), many social media platforms, document markup languages, and NoSQL databases.

How Do Companies Use Structured and Unstructured Data?

New tools are available to analyze unstructured data, particularly given specific use case parameters. Most of these tools are based on machine learning. Structured data analytics can use machine learning as well, but the massive volume and many different types of unstructured data requires it.

A few years ago, analysts using keywords and key phrases could search unstructured data and get a decent idea of what the data involved. E-discovery was and is a prime example of this approach. However, unstructured data has grown so dramatically that users need to employ analytics that not only work at compute speeds but also automatically learn from their activity and user decisions.

Natural language processing (NLP), pattern sensing and classification, and text-mining algorithms are all common examples, as are document relevance analytics, sentiment analysis, and filter-driven web harvesting.

Unstructured data analytics with machine-learning intelligence allows organizations to:

Analyze Digital Communications for Compliance

Failed compliance can cost companies millions of dollars in fees, litigation, and lost business. Pattern recognition and email threading analysis software search massive amounts of email and chat data for potential noncompliance.

A recent example in this area is Volkswagen, which might have avoided huge fines and reputational hits by using analytics to monitor communications for suspicious messages.

Track High-Volume Customer Conversations in Social Media

Text analytics and sentiment analysis lets analysts review positive and negative results of marketing campaigns, or even identify online threats. This level of analytics is far more sophisticated than simple keyword search, which can only report basics, like how often posters mention the company name during a new campaign.

New analytics also include context:

  • Was the mention positive or negative?
  • Were posters reacting to each other?
  • What was the tone of reactions to executive announcements?

The automotive industry, for example, is heavily involved in analyzing social media, since car buyers often turn to other posters to guide their car buying experience. Analysts use a combination of text mining and sentiment analysis to track auto-related user posts on Twitter and Facebook.

Gain New Marketing Intelligence

Machine learning analytics tools quickly work on massive amounts of documents to analyze customer behavior.

A major magazine publisher applied text mining to hundreds of thousands of articles, analyzing each separate publication by the popularity of major subtopics. Then, it extended analytics across all of its content properties to see which overall topics got the most attention by customer demographic.

The analytics ran across hundreds of thousands of pieces of content across all publications, and cross-referenced hot topic results by segments. The result was a rich education on which topics were most interesting to distinct customers, and which marketing messages resonated most strongly with them.

For more information, also see: The Data Analytics Job Market 

Bottom-Line: Structured and Unstructured Data

Aside from being stored in a relational database versus stored outside of one, the biggest difference between structured and unstructured data is the ease of analysis. Mature analytics tools exist for structured data, but analytics tools for mining unstructured data are nascent and developing.

The “versus” in unstructured data versus structured data does not denote conflict between the two. Customers select one or the other not based on their data structure, but on the applications that use them: relational databases for structured data and most any other type of application for unstructured data.

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