Structured Data vs Unstructured Data: What’s the Difference and Why Does It Matter?

Today’s businesses run on data. In fact, the most successful enterprises have found efficient ways to use data to gain deep insight into their operations and processes.

The challenge is storing, managing and securing data that is growing exponentially and putting strains on the traditional storage systems.

The first step to managing data is to understand the different types of data and why those differences matter. The two main types of data are structured and unstructured. Both types can help your business in many ways, even though there are vast differences in the way they’re organized and managed.

What is structured data?

Structured data is information that can be neatly organized into a set structure, such as a spreadsheet with rows and columns. Think of the information we are most used to working with on a computer: customer or patient names and addresses, phone numbers, credit card numbers and expiration dates, Social Security numbers, financial transactions and product names and SKU numbers. These are all good examples of structured data.

Structured data is easily searchable and highly organized, and machines can process it easily. Users can enter data, search through databases, and modify and use it how they want, typically using a relational database management system or through structured query language (SQL), which is a programming language designed specifically for managing structured data.

Benefits of structured data

Structured data is easy to categorize and organize. It is simple to store and access and simpler to analyze – which helps lead to valuable insights. When structured data is used online, it makes important information and websites easier to find. It can also:

• Aid in the analysis of content

• Accelerate online searches

• Enhance search engine optimization (SEO) through the use of text string and attribute indexing

• Provide users with novel ways to display content

• Simplify data mining and the extraction of valuable insights from data

• Make data updates and deletions quite simple because of how well organized it is

• Simplify business Intelligence operations such as data warehousing.

• Scale easily and be simple to secure

Characteristics of structured data

Structured data:

• Conforms to a specific data model and its structure is easily identified

• Is typically stored in rows and columns 

• Has known definitions, formats, and meaning

• Resides in fields that are typically static and fixed within files or records

• Is easy to group together based on similar classes or relationships – and all data in a group will have identical attributes

• Makes access and querying easy, which means other applications can easily use the data

• Has elements that are addressable, making analysis and processing very efficient

• Is always very specific and stored in a predefined format

• Is more commonly stored in data warehouses rather than data lakes

Examples of structured data

The most common examples of structured data would be a relational database, such as those used in placing retail product orders, making hotel reservations, or setting up a checking or savings account. The relational databases and the structured data are typically consumed by applications such as ERP, CRM, MDM, EMI, etc.

Any use case that involves spreadsheets is a use case for structured data. For example:

• Inventory control systems

• Point-of-sale and retail transaction data

• Online reservation systems for hotels, airlines, concerts, or other events. 

• General accounting practices

• Online banking

• Customer Relationship Management (CRM)

• Enterprise Resource Planning (ERP)

• Enterprise Patient Master Index (EPMI, EMI)

Pros and cons of structured data

The primary advantages of structured data are:

• It’s human-readable - humans can easily read and visualize the structured data that is neatly organized into rows and columns.

• It’s easy for machines to process, manipulate, and query. Structured data is easily understood by machines, and its organization and specifically makes it ideal for machine learning datasets.

• It doesn’t require specialized training to use. Enterprise employees, for instance, are familiar with this type of data and database structure, and don’t need to understand the foundational relationships to use it and benefit from it.

• It’s been around the longest with many available tools —long before computers made it standard—so developers have created many tools and platforms for storing, using, managing, and analyzing structured data.

• It’s easy to secure - database vendors have developed inbuilt controls to secure the structured data against cyber threats both intentional and unintentional.

There are also some drawbacks to using structured data, which include:

• An overall lack of data flexibility. Due to its specific, predefined nature, structured data can typically only be used for its original intended purpose. It’s hard to take a specific database, for instance, and use it for anything else.

• It requires storage within rigid schemas. Most structured data is stored in data warehouses, which means it’s hard to make changes to the data and scalability isn’t simple.

• Cannot increase storage capacity. Relational databases cannot easily grow their storage capacity to store growing structured data as it hurts the query and in-turn application performance.

What is unstructured data?

Once you understand what structured data is, it’s pretty simple to grasp the concept of unstructured data—it’s basically everything else. That includes all data that doesn’t fit neatly into a row and column format, such as audio recordings, video footage, images, social media postings, email content, customer service chat transcripts, machine sensor data and much more. In fact, Gartner estimates that unstructured data makes up about 80% of all enterprise data, and some other estimates are even higher.

Benefits of unstructured data

The challenge of extracting its value is worth it. The reason Big Data analytics is such a buzzword is that the exciting possibilities posed by delving into vast stores of unstructured data. Through advanced data analytics and data mining, enterprises can process their unstructured data to identify customer purchasing behavior, for instance, according to season or time of day. Or analyze drivers’ travel patterns on highways across the city to identify where, when, and why bottlenecks are occurring. Or process social media posts to gain an understanding of the ways customers perceive a brand or how they feel about a specific product. Or perform predictive analytics on machine data, etc.

The insights from analytics have the potential to revolutionize an organization’s operations and services. With a deep level of insights into the data, enterprises can gain a seriously competitive edge, identify opportunities for new revenue streams, and boost customer service like never before, reduce maintenance costs and downtime to name a few.

Characteristics of unstructured data

As its name suggests, unstructured data has no predefined data model and traditional data tools developed for structured data can’t process or analyze it.

Instead of being stored in relational databases in data warehouses, unstructured data is often stored in its raw forms in personal thumb drives, local servers, data lakes, etc. It takes specialized, advanced tools and solutions to analyze this type of data and extract value in the form of actionable insights into every aspect of enterprises, machines, processes, etc.

Examples of unstructured data

Unstructured data can be generated by humans or machines. Human-generated information can include audio files, videos such as YouTube content & surveillance, photos, healthcare imaging and text messages. Machine-generated data can include sensor data from turbines, aircraft engines, IOT, appliances, system logs, traffic or weather, for instance, or satellite imagery, digital surveillance files, or atmospheric data.

Unstructured data is used in cases where you want to perform predictive analytics, detect anomalies in machine data or user/customer behavior, determine qualitative characteristics, such as public opinion or product effectiveness. Other use cases include:

• Prevent, detect or recover from cyber attacks by analyzing data for anomaly behavior

• Perform predictive analytics on machine data to reduce maintenance costs and downtime

• Analyze audio/video customer interaction transcripts to improve support and customer satisfaction

• Analyze application ingested data to improve performance

• Measuring the effectiveness of a marketing campaign

• Identifying potential buying trends by analyzing social media posts and review sites

• Detecting employee satisfaction through text mining of chats or emails

• Enabling chatbots through text analysis, to get customers to the right resources

• Natural language processing to determine customer sentiments about a product or brand

Pros and cons of unstructured data

Of course, there are advantages and disadvantages to dealing with unstructured data. Besides having the potential to deliver deep, game-changing insights into processes and customer habits, the advantages of unstructured data include:

• More flexibility in how it’s used. Unstructured data is stored in its native format and doesn’t need to be defined until it is in use. That means it can adapt to all kinds of use cases.

• It’s easy to collect. Unstructured data doesn’t need to be predefined. It can come into the organization in its raw form quickly and simply, to be handled later.

• Storage is massively scalable. Data lakes can scale easily as volumes of data grow.

Disadvantages include:

• Need for specialized data science skills. Unstructured data can’t be analyzed or processed by just any employee. Its undefined nature and wide range of formats requires an understanding of the data itself as well as how it relates to other data.

• It requires specialized tools. The analysis of unstructured data is still fairly new, so the available tools and platforms to organize, manage, and analyze that information are still being refined and perfected.

• Not easy to scale storage. Storing and managing such unstructured data requires files and objects storage, along with a variety of business intelligence and analytics applications. Traditional storage cannot store and scale with massively growing unstructured data.