Every year, hundreds of billions of data is generated in various industries. Every little piece of information, whether it be documents, audio, photos, or user data, is stored in some specific format. This data can be broadly categorised into two types: structured data and unstructured data. 

In large organizations, where you need to deal with huge volumes of data regularly, categorizing information as structured and unstructured data helps businesses handle data efficiently and make profitable business decisions. Without proper classification, data retrieval can become a nightmare. In this blog, you will learn the difference between structured and unstructured data and understand when and where to store each type.

Key Differences Between Structured and Unstructured Data

• Structure
  Structured data is organized into categorized data types within tables. Data retrieval and analysis is easier with structured data. On the other hand, unstructured data can not fit properly into tables or databases and is relatively harder to access.
• Schema
  Structured data follows a predefined schema with data distributed properly across rows and columns. Unstructured data does not maintain a consistent schema and can exist in various formats like images, audio, and documents.
• Handling
  Structured data is easy to maintain and can be analyzed using structured query language (SQL) within relational databases. Unstructured data requires specialized tools like natural language processing (NLP) to retrieve information from them. 
• Storage
  Structured data is stored in systematic, consistent tables inside relational databases. Large organisations use data warehouses to store tons of structured data. Unstructured data is usually in large volumes without any predefined structure. It is stored in data lakes or non-relational cloud databases like Google Cloud Storage and Amazon S3.
• Example
  Structured data can include product inventory, bank transactions, employee records, patient data, etc. Unstructured data can include data like YouTube videos, PDF documents, social media posts, chat databases, images, and more.

What Is Structured Data?

Structured data comes with a predefined format, i.e., the data is arranged in tables with rows and columns before being loaded into data warehouses. This structure helps with easy retrieval and accessibility of data. Various kinds of data, including sales, financing, customer information, product inventory, etc., are stored in separate tables. These groups of tables together form a relational database. Structured query language (SQL) is used to retrieve data from these databases.  

How is structured data used?

Data is collected, sorted, and analyzed to extract meaningful insights from raw values into useful visualizations like graphs, pie charts, KPIs, trends, and dashboards. Structured data can be used for various purposes in different industries, including:

• Data analysis: SQL, Python libraries like Numpy and Pandas, and Excel can all be used  to manipulate and analyze structured data to identify any patterns and outliers in the dataset.
• Data processing: Structured data is stored in relational databases like MySQL using a consistent table schema. Although the data is stored in an organized manner, there may be outliers present. Data manipulation with extract, transform, and load can clean the datasets. Once cleaned, this data can be easily retrieved using SQL queries to generate reports.   
• Dashboards and Reports: Business intelligence and visualization softwares like Tableau, PowerBI, Apache Superset, and Google Looker Studio are used to create advanced dashboards. Dashboards are a consolidated view of different charts and KPIs put together to find meaningful insights from raw data. Periodic (weekly, monthly) reports are created using structured data to analyze the performance of active campaigns and on-going market trends.
• Business decisions:  Forecasting is done through historical data trends to estimate product demands and decide financial budgets.
• Data management: Integrated systems such as student attendance tracker, healthcare ERP (Enterprise Resource Planning) systems, CRM (Customer Relationship Management) systems are used to keep track of employees, customer trends, market demands, and inventory status.
• ML Algorithms: Structured data is often fed to machine learning models to train and improve their algorithms because of their consistent schema. Datasets or tables are labeled data and are easier to clean, process, and retrieve. This minimizes both the processing time and resources needed to build advanced machine learning models.

What are the pros and cons of structured data?

Pros of structured data: 

• Structured data can be easily stored in neat rows and columns using relational database management systems like MySQL, PostgreSQL, and MS SQL Server. 
• The datasets (tables) are easy to manipulate and analyze with tools like SQL and Python data analysis libraries such as numpy and pandas.
• Structured data works efficiently with machine learning models.
• It is user-friendly and easy to understand.
• It is easily accessible using simple tools like Excel Spreadsheets.
• Structured data is highly accurate and consistent.
• This type of data can be used to forecast trends and patterns based on previously obtained historic data, making it useful for business decision making as well.
• The consistent data format makes data manipulation faster by sorting, filtering and aggregating data. This in turn makes data analysis faster.
• Structured data typically requires less storage, resources and computational costs to be stored in data warehouses.

Cons of structured data:

• Structured data tables can only contain textual or numeric values, which restricts its usage to images, audio, and video files.
• It comes with a predefined schema which makes it less flexible as compared to unstructured data.
• For huge datasets, relational databases can be costly to employ.
• It is difficult to store complex, inconsistent data in structured databases.
• There are limited options for automating data handling systems.
• Although structured data is useful for basic relationship management, it does not allow you to display complex database connections such as many-to-many relationships. 

What is unstructured data?

Unstructured data does not follow any predefined format or structure. This type of data cannot be stored properly in tables, and hence are stored as-it-is, in their raw forms. For example, photos can be kept in cloud storage systems such as Google Drive or Amazon S3, documents in digital file repositories, mp3 songs in audio libraries, mp4 files in online media streaming servers, emails in email servers, and so on. Unstructured data is retrieved and analyzed using machine learning methods and natural language processing (NLP).

How is unstructured data used?

• Data gathering: Unstructured data typically include multimedia content that is collected from various sources such as customer reviews, social media posts, music and video streaming platforms, emails, images, and online document repositories. As unstructured data doesn’t have a predefined schema, the collected data can be stored in  its raw form.
• Data processing:  Unstructured data is stored in NoSQL databases like Amazon DynamoDB, MongoDB, AWS S3, or Google Cloud Storage, as these cloud services are able to store large volumes of raw data. Big data processing tools like Apache Spark and Hadoop can handle unstructured data efficiently.
• Data analysis: Depending on the format of raw data, techniques like NLP (Natural Language Processing), predictive analytics,  and advanced machine learning algorithms are used to extract valuable insights from unstructured data. 
• Dashboards and reports: After processing, unstructured data can be represented in tools like Tableau and PowerBI to drive business decisions.
• Data management: Unstructured data is handled in large data lakes. Cloud storage solutions like Google Drive, OneDrive, and Dropbox are capable of handling multimedia content. Content management systems (CMS) helps individuals to maintain their digital content like blogs and website multimedia. 

What are the pros and cons of unstructured data?

Pros of unstructured data: 

• Unstructured data is faster in data retrieval and querying since it is not bound to any schema or structure. 
• It is flexible to the applications in use. This means that the raw data can be obtained in many file formats as required at the time.
• Unstructured data is generally more scalable.
• Data management is affordable due to the various pricing tiers offered by cloud repositories like Amazon AWS. These platforms offer flexible pricing options with rates increasing as the volume of big data increases.
• Unstructured data can be stored directly without being preprocessed.
• Unstructured data is schema-independent. This is also known as schema-on-read, where the schema is generated after a query execution takes place.
• Large amounts of multimedia content can be stored efficiently in data lakes without compromising on data retrieval or querying time.
• Unstructured data is used to train advanced machine learning models like generative AI, recommendation systems, NLP, computer vision, and speech recognition. 

Cons of unstructured data: 

• Unstructured data requires specialized tools to manipulate and extract insights from it. This limits its accessibility to people with little or no analytical skills.
• It is not efficient to perform real-time analysis on unstructured data, as raw data is stored in various formats which makes data processing laggy.
• Data is very inconsistent and harder to work when working with multiple datasets.
• Data storage for large datasets can be quite expensive.
• Unstructured data can be difficult to maintain and handle in organizations, when you have to deal with millions of data. This can lead to performance issues like slowing down of dashboards and reports, which are generally integrated with cloud data storages.
• Unstructured data is complex to analyze as data processing steps can be time-consuming.  

Summary of Differences: Structured Data vs. Unstructured Data

Following is a quick summary of structured vs unstructured data: