Considering structured vs unstructured data at a glance, structured data is highly organized and formatted, making it easy to search and analyze, and unstructured data has no pre-defined format or organization, making it more difficult to search and analyze.
Unlike many instances of x vs y, structured vs unstructured data is not a choice; the data simply is one or the other.
In addition, the pros and cons of structured vs unstructured data do not inform choices about which kind of data a user wants to have. Rather, they help users understand the benefits and limitations of each data type to help them use it optimally.
What is structured data?
Structured data is highly organized, usually quantitative data that follows a specified format that fits into the rows and columns of a database or spreadsheet. However, it can also be stored in data warehouses.
Structured data is the most widely used by business users and individuals, because it is easy to manage and search using human-generated queries. Automated analysis methods and machine learning (ML) algorithms can also be used to search structured data.
Examples of structured data
- Business information, such as customer relationship management (CRM) data, customer transaction records, databases, financial records, inventory records, point of sale system data, pricing details, product catalogs, reservation system data, and web form data
- Files derived from applications, such as Apple Numbers and Pages, Google Docs and Sheets, and Microsoft Office Word and Excel files
- Medical information, such as after-visit summaries, DNA, health records, results from X-rays and scans, and other test results
- Metadata from files and messages
- Text and numbers, such as addresses, banking records, contact records, dates, order information, and times
Pros and cons of structured data
When considering structured vs. unstructured data, it is important to understand the pros and cons of structured data.
| Pros of structured data | Cons of structured data |
| -Accessible to users of all skill levels, including entry-level, who understand the subject matter related to the data -Can be easily fed into machine learning models without requiring any artificial intelligence (AI) or machine learning (ML) expertise -Data can be manipulated with a variety of tools, from basic spreadsheets to structured query language (SQL) or business intelligence (BI) tools -Easy to store, access, manage, manipulate, and query -High-quality, consistent, and usable information -Improves the flow of business processes and decision-making -Maintained in stable, centralized repositories -Many tools are available to measure and analyze it -Quantitative data; can be used to forecast trends and strategic impact -Quick and efficient access, filtering, and analysis -Rich suite of tools (e.g., storage, manipulation, and visualization) available for all use cases and needs -Scales algorithmically, making it easy to add storage and processing power as data volumes increase -Standardized and organized formatting that can be used across different systems and applications | -A data definition language (DDL) command is required to create, insert, and select to sort, manage, and retrieve data -Can take more time than expected to load -Data is schema-dependent; difficult to scale for large databases -Data often requires complex transformations before it can enter a data store -Data warehouses used for storage are complex systems requiring significant resources to operate and maintain -Demands the use of pre-defined categories -Difficult to identify hidden problems in the source system -Fixed, pre-defined structure is difficult to change -Hard to determine which query would result in a specific outcome -Initial task of categorizing, tagging, and arranging data can be time-intensive -Overlaps between datasets, redundant data, and stale or low-quality data are common -Requires users to create schema data definitions in advance -Schemas can be inflexible and rigid -Specialized knowledge and skills are required to build and maintain data stores -Unable to capture the nuances of human language, images, or other complex information |
Structured data tools
Storage is a vital tool, and the type that can be used will vary based on whether the data is structured or unstructured. The main types of storage for structured data are:
- Spreadsheets
The most basic type of storage for structured data is spreadsheets. These are used to connect, search, manipulate, and manage data. Examples of spreadsheet solutions are Microsoft Excel, Apple Numbers, and Google Sheets. - Relational database management systems (RDBMS)
A relational database collects structured data and organizes it according to pre-defined relationships. Data is stored in one or more tables (or “relations”) of columns and rows. Relationships are the connections between different tables.
Examples of relational database management systems are IBM DB2, Microsoft SQL Server, My SQL PostgreSQL, and Oracle Database. - Data warehouses
A data warehouse is a type of relational database management system that is specifically designed for business intelligence (BI) and in-depth data analytics. Examples of data warehouse solutions are Amazon Redshift, Azure Synapse Analytics (previously Microsoft Azure SQL Data Warehouse), Google BigQuery, and Snowflake.
Tools are used to retrieve data from storage, conduct analysis, and provide reports. Functions of these tools include:
- Data mining
- Data analytics
- Business intelligence
Additionally, management tools are used to work with structured data, such as:
- Schema resource and management tools that execute, test, manage, and help make updates to structured data
- Structured data tools that are used to generate basic structured data
- Schema validation tools are used to debug a variety of structured data types and ensure that structured data accurately expresses the intent
Structured data use cases
Structured data use cases abound. Examples include:
- Banking information (e.g., financial transaction and account information)
- Customer relationship management (CRM) data (e.g., customer profiles, lead information, and sales data)
- Customer reviews
- Delivery apps (e.g., grocery and restaurants)
- E-commerce site information (e.g., product descriptions, pricing data, and SKU numbers)
- Electronic ridesharing systems
- Medical information (e.g., prescriptions, patient data, test results, and medical history)
- Reservation systems (e.g., hotels and airlines)
What is unstructured data?
Unstructured data is raw qualitative information that does not follow specified formats. It represents the bulk of data that exists.
Unstructured data is represented in images, videos, text messages, and audio recordings. Since it does not have standardized formatting, it is stored in non-relational or NoSQL databases and data lakes.
While it can be unwieldy, unstructured data is a gold mine of insights.
Unstructured data can be aggregated and analyzed to provide rich, complex insights, such as predictions of future behavior or outcomes and users’ sentiments.
Examples of unstructured data
- Data derived from devices and apps include mobile activity, social media posts, satellite imagery, surveillance imagery, geospatial data, financial ticker data, and weather information
- Documents include invoices, records, web history, emails, voice and text messages, videos, and photos
- File types include audio files, image files, text, and video files
Pros and cons of unstructured data
It is important to understand the pros and cons of unstructured data when considering structured vs. unstructured data.
| Pros of unstructured data | Cons of unstructured data |
| -Can be stored on shared or hybrid cloud servers with minimal expenditure for data management -Customizable to meet the needs of specific use cases -Does not require pre-processing for storage -Free-flowing -Offers qualitative insights into user behavior and sentiments -Provide a broader, more diverse view of information -Represents an infinite variety of data types -Schema independent (i.e., schema on read), which means that minor alterations to the database do not impact cost, time, or resources -Stored in native format until needed -Vast volumes of information available for analysis | -Can be difficult to understand -Data lakes can turn into data swamps, storing vast amounts of information that is not of value to the organization -Difficult to organize -Large volumes of data can be expensive to store -Limited tools available for manipulating it -Limited visibility into the data that is stored and its value -Proficiency in data science and machine learning is required to use (i.e., prepare, analyze, and integrate) it -Relies heavily on open-source solutions, which can have security vulnerabilities -Requires advanced analytics with complex algorithms to analyze and extract insights -Takes time to prepare for queries |
Unstructured data tools
Storage differs for structured vs unstructured data. Unstructured data is stored in non-relational databases (e.g., NoSQL or Hadoop).
With unstructured data, data warehouses are replaced with data lakes. A data lake is a centralized repository that can store unstructured data in its raw form without requiring schema or transformation. Examples of data lake solutions are Amazon Web Services’ AWS data lake solution, Cloudera SDX’s Data Lake Service, Databricks Lake House Platform, Google Cloud’s data lake, and Microsoft Data Lake.
Examples of tools used to help analyze and deliver insights from unstructured data include:
- Visualization tools that present the results of analytics (e.g., MongoDB Charts)
- Tools that support fast processing to enable real-time analytics (e.g., Apache Spark)
- Data cleaning, transformation, and extraction (e.g., MapReduce and Pig)
- Self-service business intelligence tools (e.g., Domo, Microsoft Power BI, and Tableau)
Unstructured data use cases
Chatbot optimization
Data from customer interactions can be analyzed and converted into directions to help chatbots respond to requests more effectively.
Classify images and sounds
Image and sound classification are performed using deep learning. An example is using the sounds of motors to train a model to detect one that is on the verge of failure to allow for proactive maintenance. Image classification has a number of use cases in radiology, marketing, and competitive research among other fields.
Converting unstructured data into structured data
Text analytics that leverage natural language processing (NLP) and machine learning can be used to add structure to unstructured data.
Customer insights
Using data mining on unstructured data sets, such as click data, chats, and emails collected from an online retailer’s website, provides insights into customer buying habits and timing, purchase patterns, and sentiment toward a specific product.
Predictive maintenance
Unstructured sensor data from industrial machinery can be used for predictive analytics. The results can allow organizations to take proactive maintenance measures to address issues before they occur to avoid costly breakdowns.
Structured vs unstructured data
| Structured data | Unstructured data |
| Structured data analysis methods include: -Classification and arrangement of data into similar classes based on common features -Data clustering: Organizing the data into defined groups based on different attributes -Investigation or regression analysis of the relationships and dependencies between variables | Unstructured data analysis methods include: -Data mining to detect anomalies and connections in large data volumes to predict outcomes -Data stacking: Investigation of large volumes of data -Splitting data volumes into smaller items and stacking the variables with related values into one group |
| Roles that handle structured data include: -Business analysts -Marketing analysts -Software engineers | Roles that handle unstructured data include: -Data analysts -Data scientists -Engineers |
| Sources of structured data include: -Financial transactions -Relational databases -Sensor data -Spreadsheets -System logs | Sources of unstructured data include: -Audio files -Emails -Social media posts -Surveys and interviews -Videos |
| Structured data characteristics: -Quantitative information (i.e., information is countable) -Comes in the form of numbers, text, and values -Common formats include XML and CSV -Preformatted -Organized -Pre-defined, not flexible data models | Unstructured data characteristics: -Qualitative data (i.e., information is subjective) -Comes in the form of text files, audio files, and video files (can be numerical, alphabetical, Boolean, or a mix of these) -Common formats include WMV, MPW, MP3, and WAV -Unorganized -Not pre-defined, flexible data models |
| Structured data storage: -Typically resides in a relational database or data warehouse -Storage tools are established and well-understood -Stored in tables with rows and columns -Labels specify the data types -Data stored in similar, defined formats (e.g., text and numbers) -Models describe the relationship between data elements | Unstructured data storage: -Typically resides in a non-relational database or data lake -Storage tools are new, and fewer people understand how to use them -Stored in multiple data models without tables (e.g., a document, wide-column, graph, and key-volume database) -Can have different data types in the same collection -Data in raw formats (e.g., text, video, audio, or image) -No set data model, but it can have a structure |
| Using structured data makes it easy to: -Search -Analyze and extract insights | Using unstructured data requires: -Complex search tools and skills -Advanced analytics tools to extract insights, such as AI, natural language processing, and machine learning |
Gaining the most from both structured and unstructured data
When unstructured data began to explode, organizations knew they had something valuable on their hands, but lacked the tools to use it. Technology has leveled the playing field, making the structured vs unstructured data access and use challenges a moot point.
When considering structured vs unstructured data, the focus should not be on one versus the other, but on ensuring the principles laid out in the CIA Triad (i.e., ensuring the confidentiality, integrity, and availability of data) are followed. This ensures that organizations extract the maximum value from data and have the systems in place to protect it.
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