Structured vs. Unstructured Data: What's the Difference?

Data science brings the world together and concentrates randomly distributed information into small units.

With all the buzz about big data, structured vs unstructured data, and how companies use it, you may ask, “What types of data are we referring to?”

The first thing to understand is that not all data is created equal. This means the data generated from social media apps is completely different from the data generated by point-of-sales or supply chain systems.

Some data is structured, but most of it is unstructured. In the backend, a database management software  (DBMS) is a query management system that authenticates the user's access to this data and the ability to store, manage and retrieve it through user queries.

To clarify, let's break down the unique differences between structured and unstructured data.

In addition to being sourced, collected, and scaled in different ways, structured and unstructured data will reside in entirely separate databases.

What is structured data?

Structured data is most often categorized as quantitative data, and it's the type of data most of us are used to working with. Think of data that fits neatly within fixed fields and columns in relational databases and spreadsheets.

Examples of structured data include names, dates, addresses, credit card numbers, stock information, geolocation, and more.

Structured data is highly organized and easily understood by machine language. Those working within relational databases can quickly input, search, and manipulate structured data using a relational database management system (RDBMS). This is the most attractive feature of structured data. 

The programming language for managing structured data is called structured query language, also known as SQL. IBM developed this language in the early 1970s, and it is particularly useful for handling relationships in databases.

Examples of structured data 

If this sounds confusing, here is an example of a DDL (data definition language) command executed to tabulate structured data. The data is stored in an SQL table, each row and column contributing to a specific data type.

From the top-down, we can see that UserID 1 refers to the customer Alice, who had two Order IDs of ‘1234’ and ‘5678’. Next, Alice had two ProductIDs of '765’ and ‘987’. Finally, we can see Alice purchased two packages of potatoes and one package of dried spaghetti.

Structured data is also used in airline reservation systems, electronic ridesharing systems, food, and delivery apps, and search engine optimization (SEO data). In each of these cases, data is stored in relational databases and can be stored, retrieved, or managed in large forms.

Structured data revolutionized paper-based systems that companies relied on for business intelligence decades ago. While structured data is still useful, more companies are looking to deconstruct unstructured data for future opportunities.

                                                                      Source: Fivetran

Examples of structured data 
Structured data is used in multiple consumer-oriented business databases or ERPs, such as :

• E-commerce: Review data, pricing data, and SKU number of commodities
• Healthcare: hospital administration, pharmacy, and patient data and medical history of patients.
• Banking:  Financial transaction details like name of beneficiary, account details, sender or receiver information and bank details.
• Customer relationship management (CRM) software: lead acquisition data, source, activity and so on of leads in the CRM database.
• Travel industry: Passenger data, flight information, and travel transactions.

Structured vs. unstructured data

Unstructured data is the polar opposite of structured data. Here is a lowdown on notable differences between the two.

Structured data is preformatted clean data neatly arranged into memory blocks. Its format is predefined in rows and columns and stored in relational database systems (RDBMS) or Microsoft Excel. The data is known as "schema on write, " representing data for a large database schema or blueprint. It is highly scalable and secure and requires less management. 

Unstructured data is highly complex, qualitative, and unorganized. It is also referred to as big data, which does not conform to any one particular standard. This data can be numerical, alphabetical, boolean, or a mix of all of them. It is stored using a NoSQL database. It cannot be stored in a relational database or RDBMS since data strings have mixed data types that cannot fit into either a row or a column. Common types of unstructured data are clickstream data, social media data, text, and multimedia.

Benefits of structured data

 It is easy to store, retrieve, and manage structured data as it has an organized backend mechanism. Using structured data in business can result in the following benefits. 

• Structured data can be easily fed into machine learning models as input datasets without any trimming.
• Working with structured data does not require AI or ML expertise. Anyone with good product information and basic data science knowledge can do so.
• Structured data is stored evenly in data warehouses or spreadsheets. Its specific and organized nature makes it easy to manipulate and query.
• Structured data predates unstructured data, so more analytics tools are available to measure and analyze it.
• The data is of higher quality, consistency, and usability than unstructured data. 
• There are fallback mechanisms to adapt if the user encounters an error while managing structured data.
• It is also known as quantitative data, as businesses use their metrics to forecast business trends and strategic impact.
• It is maintained in a stable, centralized repository that improves the flow of business processes and decision-making to optimize ROI.

Challenges of structured data

Most structured data issues highlight its inflexibility and rigidity in scaling larger database schemas. Structured data is "schema on write" or "heavily dependent on schema" for operations. Common challenges of structured data are listed as under:

• As structured data is schema dependent, it is a little difficult to scale it for large databases.
• The time needed to load structured data is sometimes underestimated. Identifying hidden problems in the source system and updating, retrieving, and restoring it can eat into your cloud storage. 
• Doesn't cope well with the changing business scenario. It is hard to determine which query would result in a specific business outcome. The nature of queries and transactions change as a business shifts its consumer focus.
• Structured data is manually entered into the database management system. The user has to type in a DDL (data definition language) command like Create, Insert, and Select to sort, manage and retrieve data from the system.

Structured data tools

Apart from using a structured query language(SQL) or Microsoft Excel to manage structured data manipulations, there are a few more tool extensions you can use.

• PL SQL: Procedural Query Language or PL SQL is an existing version of SQL that deals with work transactions. The common transactional queries are "commit" or "rollback."
• Postgre SQL: Postgre SQL is an open-source relational database management system that handles large data volumes. It also supports SQL and JSON querying along with high-level languages.
• SQLite: It is a high-tier, self-contained, and serverless database that software developers use to extract structured data for business app integrations, 
  
• My SQL is a standard integrated data environment that uses user authentication to enter data records through queries in a mass-deployed database. 
• OLAP: It encompasses a broader category of database management comprising data mining, report mining, and business intelligence.
  
  
  

What is unstructured data?

Unstructured databis often categorized as qualitative and cannot be processed and analyzed using conventional data tools and methods. It is also known as "schema independent" or "schema on read" data. 

Examples of unstructured data include text, video files, audio files, mobile activity, social media posts, satellite imagery, surveillance imagery – the list goes on and on.

Unstructured data is difficult to deconstruct because it has no predefined data model, meaning it cannot be organized in relational databases. Instead, non-relational or NoSQL databases are the best fit for managing unstructured data.

Another way to manage unstructured data is to have it flow into a data lake or pool, allowing it to be in its raw, unstructured format.

Finding the insight buried within unstructured data isn’t an easy task. It requires advanced analytics and high technical expertise to make a difference. Data analysis can be an expensive shift for many companies.

Examples of unstructured data

Those able to harness unstructured data, however, are at a competitive advantage. While structured data gives us a bird's eye view of customers, unstructured or big data can give us nitty-gritty information about consumers' everyday actions.

For example, data mining techniques applied to unstructured data from a retail website can help companies learn customer buying habits and timing, purchase patterns, sentiment toward a specific product, and much more.

Unstructured data is also key for predictive analytics software. For example, sensor data attached to industrial machinery can alert manufacturers of strange activity ahead of time. With this information, a repair can be made before the machine suffers a costly breakdown.

More examples of unstructured data:
Unstructured data is any event or alert sent and received by any user within an organization with no proper file formatting or direct business co-dependency. 

• Rich media:  Social media, entertainment, surveillance, satellite information, geospatial data, weather forecasting, podcasts
• Documents: Invoices, records, web history, emails, productivity applications
•  Media and entertainment data, surveillance data, geospatial data, audio, weather data
• Internet of things: sensor data, ticker data
• Analytics: Machine learning, artificial intelligence (AI)

Benefits of unstructured data

Unstructured data, also known as big data nowadays, is free-flowing and native to each specific company. It is schema independent and is known as "schema on read." Customizing this data per your business strategies can give you a competitive edge over competitors still stuck in traditional decision-making. And here is why.

• Unstructured data is easily available and has enough insights businesses can collect to learn about their product response.
• Unstructured data is schema-independent. Hence minor alterations to the database do not impact cost, time, or resources. 
• Unstructured data can be stored on shared or hybrid cloud servers with minimal expenditure on database management. 
• Unstructured data is in its native format, so data scientists or engineers do not define it until needed. It opens the expandability of file formats, as it is available in different formats like .mp3, .opus, .pdf, .png, and so on. 
• Data lakes come with "pay-as-you-use" pricing, which helps businesses cut their costs and resource consumption.

Challenges of unstructured data

Unstructured data is the most trending method of data collection and manipulation today. Many businesses are switching to more "customer-centric" business models and banking on consumer data.  However, working on unstructured data results in the following challenges.

• Unstructured data is not the easiest to understand. Users require a proficient background in data science and machine learning to prepare, analyze and integrate it with machine learning algorithms.
• Unstructured data rests on less authentic and encrypted shared servers, which are more prone to ransomware and cyber attacks.
• Currently, there aren't many tools that can manipulate unstructured data apart from cloud commodity servers and open-source NoSQL DBMS.

Unstructured data tools

Apart from using a NoSQL to manage unstructured data manipulations, there are a few more tools you can use.

• Hadoop: A distributed computing framework for processing large amounts of unstructured data.
• Apache Spark: A fast and general-purpose cluster computing framework for processing structured and unstructured data.
• Natural Language Processing (NLP) tools: For extracting information from unstructured text data.
• Machine learning libraries: For building models to analyze and predict patterns in unstructured data.

More data types

Apart from the above data types, semi-structured data and metadata are crucial in handling the increasing complexity and diversity of modern data sources.

What is semi-structured data?

Semi-structured data is a type of structured data that lies midway between structured and unstructured data. It doesn't have a specific relational or tabular data model but includes tags and semantic markers that scale data into records and fields in a dataset.

Common examples of semi-structured data are JSON and XML. Semi-structured data is more complex than structured data but less complex than unstructured data. It's also relatively easier to store than unstructured data, bridging the gap between the two data types. 

An XML sitemap contains page information for a website. It embeds URLs, domain scores, do-follow pages, and meta tags.

What is metadata?

Metadata is often used in big data analytics and is a master dataset that describes other data types. It has preset fields that contain additional information about a specific dataset.

Metadata has a defined structure identified by a metadata markup schema that includes metadata models and standards. It contains valuable details to help users better analyze data and make informed decisions.

For example, an online article can display metadata such as a headline, a snippet, a featured image, an image alt-text, a slug, and other related information. This information helps differentiate one piece of content from several other similar pieces of content on the web. Therefore, metadata is a handy set of data that acts as the brain for all data types.

Database management tools

Database management tools provide the infrastructure to store, manage, and analyze data effectively, ensuring efficient data management and valuable insights. Utilizing the right database management tool will allow companies to:

• Reduce operational costs
• Track current metrics and create new ones
• Understand its customers on a far deeper level
• Unveil smarter and more targeted marketing campaigns
• Find new product opportunities and offerings

Like data, like decisions

The volume of big data continues to rise, but the importance of big data storage will soon cease to exist.

Whether data is structured or unstructured, having the most accurate and relevant data sources will be key for companies looking to gain an advantage over their competitors.

The more varieties of data created by data scientists, the more new and advanced algorithms will be created, which will ease the line of GDPR compliance.

Data is seeping into every major industry in the world. Brands are moving away from non-essential marketing gimmicks to data-driven consumer marketing. The information data provides us is being learned and analyzed in tandem with artificial intelligence and network computing to create robust, hyperconnected solutions. 

At the end of the day, it’s up to the consumer to determine how comfortable they are with the ways their data is used.

New to big data analytics but want to learn more? Learn how to gain real-time insights from your data with the right big data analytics software.

This article was originally published in 2021. It has been updated with new information.