Structured vs unstructured data: What’s the difference?

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

1. 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
2. Files derived from applications, such as Apple Numbers and Pages, Google Docs and Sheets, and Microsoft Office Word and Excel files
3. Medical information, such as after-visit summaries, DNA, health records, results from X-rays and scans, and other test results
4. Metadata from files and messages
5. 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.

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:

1. 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.
2. 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.
3. 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:

1. Data mining
2. Data analytics
3. Business intelligence

Additionally, management tools are used to work with structured data, such as:

1. Schema resource and management tools that execute, test, manage, and help make updates to structured data
2. Structured data tools that are used to generate basic structured data
3. 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:

1. Banking information (e.g., financial transaction and account information)
2. Customer relationship management (CRM) data (e.g., customer profiles, lead information, and sales data)
3. Customer reviews
4. Delivery apps (e.g., grocery and restaurants)
5. E-commerce site information (e.g., product descriptions, pricing data, and SKU numbers)
6. Electronic ridesharing systems
7. Medical information (e.g., prescriptions, patient data, test results, and medical history)
8. 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

1. Data derived from devices and apps include mobile activity, social media posts, satellite imagery, surveillance imagery, geospatial data, financial ticker data, and weather information
2. Documents include invoices, records, web history, emails, voice and text messages, videos, and photos
3. 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.

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:

1. Visualization tools that present the results of analytics (e.g., MongoDB Charts)
2. Tools that support fast processing to enable real-time analytics (e.g., Apache Spark)
3. Data cleaning, transformation, and extraction (e.g., MapReduce and Pig)
4. 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

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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