Today, with millions of new pieces of information being created every second, data serves as the backbone that holds everything together. Whether you are filling a survey online, making an online transaction, or uploading fresh content to social media, data is continually flowing in diverse formats. Depending on the information we are seeking to store, some of it may be well-organized while others may not have a consistent type.
It is essential for developers to have a solid understanding of their practical applications to fully utilize the potential of the data collected and learn how it supports effective data management.
In this blog, we will be going through these different types: structured and unstructured data. Basic customer information, transaction records or any product information that is stored in simple, organized tables is a type of structured data. On the other hand, multimedia content like text, images and videos falls under unstructured data.
Excited already? Let’s take a closer look and uncover all about these different types of data, their use cases and their practical implementations!
What is Structured Data?
A data that is well-organized, has a well-defined format, is said to be structured data. Here, the content is typically stored in tables with rows and columns, where each row represents a record and each column contains various attributes defining the data. This distinction makes the information easy to read, analyze, and retrieve as needed, more on that later.
Structured data is organized in spreadsheets and databases, and therefore one can effortlessly search for a specific record easily. It follows a consistent schema, which means the data has similar structure and formatting. The data can conveniently be saved in columns under predefined categories. Structured data is a preferred choice for building efficient databases and data retrieval systems.
How is Structured Data Stored?
As we discussed above, structured data has a uniform data formatting, which makes it easy to store, retrieve and analyze the information. In real-world applications, this type of data is often stored in data warehouses, relational and non-relational databases, cloud storages, depending on how we are going to utilize the data.
- Relational Databases: With a tabular structure, RDBMS( Relational Database Management Systems) like Oracle, MySQL, Microsoft SQL Server and PostgreSQL are used to store structured data. They incorporate columns and rows to store data, making it more efficient, readable and manageable.
- Cloud-Based Storage Systems: Another great option, cloud-based solutions are a reliable option to store data in cloud containers or buckets. Companies use a variety of cloud computing services like Google Cloud Storage, Microsoft Azure Storage, and Amazon AWS because of its great scalability and cost-effective solutions.
- Data Warehouses: Last but not the least, data warehouses are specialized data management systems that handle large volumes of historical data. These are typically used by analysts to conduct analysis and predict future data. Data warehouse systems like Google BigQuery, Amazon Redshift, Teradata, Apache Hive, and Snowflake gather data from several sources into a central repository.
Applications of Structured Data
You can find structured data almost everywhere, from excel spreadsheets to data repositories and enterprise databases. Due to its robust schema management system, structured data is frequently used in a variety of industries, including finance, healthcare, handling consumer data and behavior analysis, monitoring sales, supply chains, tracking orders and shipments, logistics, and more. Either it can be created manually, or machine-generated.
For instance, the results that are displayed when you conduct a search on a search engine like Google are a type of structured data. Other real-world applications may include the product information you get on e-commerce platforms like Amazon.
Data analysts and data engineers can get useful insights and performance reports with the aid of structured data, enabling them to make data-driven decisions.
What is Unstructured Data?
Unlike structured data, in unstructured data the data does not hold any specific type or format. In this case, the data does not fit efficiently into the predefined categories of a table. Multimedia content such as text, images, videos, audio, and emails comes under unstructured data. Because of this inconsistency in formatting, the process of storing and analyzing the data can be a bit complex.
Despite this, using machine learning and Natural Language Processing (NLP), unstructured data can be facilitated to derive meaningful insights using content recommendation and sentiment analysis techniques.
How is Unstructured Data Stored?
Much like structured data, storing unstructured data requires different approaches depending on the use case and type of data we wish to store. In real-world applications, unstructured data includes text data from user reviews, image and video content from social media feeds, audio files, emails, and more. Here are some ways in which unstructured data is stored for analysis:
- Non-Relational Databases (NoSQL Databases): NoSQL databases like MongoDB, Amazon DynamoDB, Apache Cassandra, and Redis are a popular option to store unstructured data in large-scale applications where data is rapidly evolving. These databases are highly scalable and capable of handling huge volumes of data.
- Data Lakes: There are repositories that are designed for storing and handling large amounts of raw, unprocessed data securely. Data lakes are powerful storage systems that specialize in storing data from various sources, which can further be used to perform exploratory data analysis. Amazon S3, Microsoft Azure Data Lake, Apache Hadoop, Google Cloud Storage, IBM Cloud Object Storage, and Apache Cassandra are some of the popular data lakes available online.
- File Storage: Unstructured data can also be organized hierarchically within folders and directories. Document storage systems, where each file is given a distinct label and can be categorized inside certain folders, are an ideal example of this. Although such solutions are wonderful for creating a clutter-free organization, data retrieval may become less effective with time.
Applications of Unstructured Data
Unstructured data offers a great set of functionalities that can be applied to various applications to gain powerful insights. NLP techniques like sentiment analysis and image recognition require a diverse and extensive set of textual and image data content to get meaningful results. Similarly, to perform text summarisation, we would require a large corpus of documents to train models.
In such a scenario, unstructured data can be quite useful with its labeled data approaches. Unstructured data from social media such as posts, images, tweets, and comments are employed to comprehend current market trends and customer sentiments. This can be helpful to understand customer feedback and their opinions about specific products.
Apart from its applications in understanding human behavior, unstructured data is also applied in fraud detection and security analysis. Data from email conversations and chat logs are analyzed by Machine Learning algorithms to look for fraudulent activities like unauthorized transactions and security breaches. Image recognition systems are used to detect autonomous vehicles and security systems.
Structured vs Unstructured Data
Now that we understand structured, unstructured data and their use cases, you must be wondering, which one of them is better? To understand that, let’s explore their differences:
|Simple and organized.
|Complex and Unorganized.
|Can be displayed in rows and columns.
|Can not be displayed in rows and columns.
|Data is quantitative.
|Data is qualitative.
|Stored in relational database management systems (SQL).
|Stored in non-relational database management systems (NoSQL)
|Can be stored in data warehouses, therefore is highly scalable.
|Can be stored in data lakes, but is difficult to scale.
|Schema on write.
|Schema on read.
|Less management is needed.
|High maintenance is needed.
|Provides structured insights from the data.
|Provides deeper insights into the data.
|Standardized file formats: csv, xml, json, sql, xls, xlsx
|Extensive file formats: .txt, .doc, .docx, .pdf, .html, .jpg, .jpeg, .png, .gif, .mp3, .mp4
|Low storage space is required.
|High storage space is required.
|Structured data is more secure.
|Unstructured data is susceptible to data breaches.
|Structured data is easy to retrieve and use.
|Unstructured data requires complex search and processing before it can be used.
|Includes data from web forms, customer data, server logs, orders and shipments.
|Includes data from text documents, PDFs, social media feeds, multimedia content, and audio files.
Understanding this distinction between structured and unstructured data can help data engineers and analysts make better choices for data management and analysis. Businesses, where data consistency, reliability, and processing are essential, may prefer structured data since it offers greater accessibility and formatting. This comprises transactional systems, customer datasets, inventories and logistics data.
While on the other hand, unstructured data offers immense potential for businesses that want to dive deeper into the data and extract valuable insights to make data-driven decisions. This comprises market trends, social media analysis, tailored commercials, and better marketing strategies.
As we wrap up this blog, I hope you now understand how, in the quickly evolving data-driven industry, selecting the right type of data may subsequently help organizations use their data efficiently and make meaningful decisions.