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Structured vs. unstructured data: Business benefits

Structured vs. unstructured data: Business benefits

February 15, 2023
February 15, 2023
Structured vs. unstructured data: Business benefits
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Understanding the difference between structured and unstructured data is a critical first step to creating a comprehensive plan for fully utilizing your company's data.‍

Data is more important today than ever before. Failure to correctly store business data can lead to critical failures in everything from decision-making to resource allocation.

This article aims to explain the difference between structured and unstructured data. By the end, you'll understand the pros and cons of both and different use cases to help you get the most out of your business data.

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What is structured data?

Structured data refers to business data that’s organized into specific formats based on the needs of the business. Usually, structured data is organized into tables and stored in data warehouses. Because the data is specifically formatted, it's easier for the average business user to understand and utilize it for specific purposes.

Data is written into a database using schema-on-write, which means the data is preformatted before going in. This method makes it easier to manage smaller amounts of prestructured data, such as credit card information, medical insurance data and financial transactions. 

examples of structured data

The downside with structured data is that it’s limited in what it can be used for. Changing structured data can be difficult and expensive as the entire dataset needs to be altered to keep everything consistent.

Pros of using structured data

  • Easy for the average user to utilize and understand
  • Easy for machine learning algorithms to utilize
  • A greater number of analytics tools can use the data
  • Requires less storage space

Cons of using structured data

  • Limited to specific uses
  • More limited storage options
  • Difficult and expensive to make changes

Tools for working with structured data

Structured data is well-defined and organized, which lends itself to an array of tools that can be used for working with it. Because structured data has been around longer, these tools are well-developed and tested in the field.

structured data tools

These tools range from database management systems to analytics and business intelligence tools to help teams better utilize data. 

Some of the most popular tools for the management of structured data include:

  • MySQL: Embedding data in mass-deployed software.
  • OLAP (Online Analytical Processing): Data analysis.
  • SQLite: Relational database.
  • Oracle database: Advanced database management system.

Use cases for utilizing structured data

Structured data has been used historically longer than most other data types and has a greater number of use cases. However, each data set has to be preformatted for the intended use.

This rigidity is one of the major downsides to structured data. It's very specific to each company's needs. That said, there's a range of use cases across many industries including:

  • Ecommerce: Product IDs, pricing data and customer account data
  • Healthcare: Patient forms, medical insurance data and medical billing data
  • Banking: Customer account data and financial transactions
  • Customer relationship management (CRM) software: Names, phone numbers and addresses
  • Travel industry: Reservation data, ticket pricing information and dates

What is unstructured data?

Unstructured data is an amalgamation of data formats typically stored in data lakes. It covers everything from social media posts to videos and text files. 

One of the key advantages of unstructured data is that it helps provide qualitative information useful to businesses for understanding trends and changes.

The primary drawback of working with unstructured data is the added complexity requiring specialized skills, tools and understanding to analyze and use the information. This complexity typically means working with a data specialist who can query and analyze the information.

In contrast to structured data, its unstructured counterpart utilizes a schema-on-read data analysis strategy. This method means that the data is organized as it gets pulled out of the storage location rather than before going in.

examples of unstructured data

There are a few advantages to this, including the ability to create multiple views of the same data and the ease of storing information and adding data sources.

Pros of using unstructured data

  • Easier to store due to being in native format
  • Collecting and storing are faster
  • Cheaper to store unstructured data using data lakes
  • Provides more granular information

Cons of using unstructured data

  • More complicated to work with
  • Requires highly specialized tools for organizing
  • Expertise needed

Tools for working with unstructured data

Unlike structured data, which has a robust number of reliably tested tools for working with, unstructured data offers fewer options. There's also the added component that these tools require specialized data experts to pull and analyze the information meaningfully.

With that said, more tools for working with unstructured data are being released, including AI-powered options that can better manipulate data. More established products are also improving, making it easier for data managers to work with unstructured data.

unstructured data tools

Some of the most popular tools for working with unstructured data include:

  • NoSQL (not only Structured Query Language) Database Management System (non-tabular database)
  • MongoDB
  • Apache Hadoop
  • Microsoft Azure
  • Amazon DynamoDB

Use cases for utilizing unstructured data

A key advantage to unstructured data is that it's highly versatile and can be used for understanding qualitative aspects of a business that may be more difficult with its structured counterpart.

Because unstructured data is stored in native formats and can be collected quickly, there are many more uses for businesses than structured data.

Common use cases for unstructured data include:

  • Ecommerce: Identify spending patterns and customer behavior
  • Healthcare: Determine treatment recommendations and forecast changes in a patient
  • Finance: Track markets and perform risk analysis

Privacy law issues that come with unstructured data

It's critical to note that unstructured data can present challenges concerning compliance with global privacy laws such as the General Data Protection Regulation (GDPR).

A key stipulation of these privacy laws is data management and record keeping, which can be difficult to follow when working with large amounts of unstructured data. Failure to follow these laws can lead to major fines as high as 10 million Euros or 2 percent of annual turnover.

One solution to this is the utilization of distributed databases, with over 78 percent of IT managers using this option to manage sensitive data better and more easily comply with country-specific privacy regulations.

Structured vs. unstructured data: Key differences to understand

The key differences between structured data and unstructured data can easily be broken down into a few different points.

Data formats

Structured data comes in a limited number of formats compared with its unstructured counterpart. Typically, structured data consists of numbers and text with standardized formats that are more easily readable by users. 

Unstructured data, on the other hand, offers a wide range of format options that can be collected quickly and easily into a storage location. These formats include unique data such as:

  • Social media posts
  • PDF documents
  • Video (WMV, MP4 and MOV)
  • Images (JPG, PNG and GIF)
  • Aaudio (MP3, WAV and MPEG)

Data models

Structured data is highly dependent on rigid data models. This rigidity has the advantage of making this type of data more easily searchable and simple to analyze. However, it also means that it must follow predefined data models with little room for flexibility. 

Unstructured data is the opposite and offers greater flexibility regarding data modeling. Because data is stored in native formats, data managers have more options for using the data. The downside is that the flexible structure makes the information more subjective and therefore more difficult to work with.

Data storage

Another major difference when looking at structured vs. unstructured data is how the information is stored. With structured data, the end location is typically in a data warehouse. These are large databases with a rigidly defined structure that are optimized to save space.

Unstructured data, on the other hand, is typically stored in data lakes. These are large storage repositories that make it easy to store large amounts of data in native formats. Data lakes are more easily scalable and allow storing larger volumes of data such as videos and images.

structured vs unstructured data

Databases

Structured data is stored in relational databases, known as RDBMS, and set up in tables. Each of these tables typically has many rows that store records and columns that indicate the specific data type for that column. These rows and columns make up the schema of the database.

To work with and make changes to this structured data, Structured Query Language (SQL) is used. The syntax of SQL is basic and similar to English, making it easy to work with when reading and writing data and making changes.

With unstructured data, the information is stored on non-relational databases called NoSQL databases. These utilize various data models to store and process large volumes of data. With NoSQL databases, there are few, if any, relations to ensure faster queries.

Ease of use

As noted previously, structured data is much easier to analyze and manipulate as everything stored is already formatted. This makes it simple for both human users and machine learning. Another benefit that makes structured data easier to use is the large number of tools available for manipulation and analysis because the data type has been around longer.

With unstructured data, fewer tools are available and the knowledge needed to work with and analyze the data is much more specific.

Data types

Structured data is quantitative, meaning that it typically contains numbers and text that are precisely defined. Users can analyze this data in a few different ways including classification, regression, clustering, combinatorial and geometric, among other methods.

In contrast, unstructured data is commonly qualitative and often contains information that's less precise and more subjective. Examples of this can include written reviews left on a store's website or a person's social media posts. 

Unstructured data can also be analyzed using a few different methods such as data mining, data stacking, natural language processing and others.

What is semi-structured data?

Semi-structured data represents a bridge between structured and unstructured data. It’s easier to store but not quite as well defined as structured data, which makes it more flexible. 

semi-structured data examples

Using markers and tags to group datasets makes it easier to catalog and search than unstructured data. These markers come in the form of metadata that's used to identify specific characteristics providing a hybrid structure.

While semi-structured data may seem like the perfect balance between structured and unstructured data, businesses must combine all three to remain competitive.

Examples of semi-structured data 

One of the most prominent examples of semi-structured data used daily is the metadata surrounding web articles (titles, image alt-text and snippets) which machine learning algorithms can use to understand the purpose of online articles.

Other common examples of semi-structured data include:

  • Markup languages (XML and JSON) 
  • Emails
  • Sensory data (weather, traffic and satellite imagery)
  • Electronic Data Interchange

How Fivetran can help manage data types

Irrespective of what type of data you're working with, Fivetran can help you centralize and scale your data management.

One of the key benefits of using Fivetran is the ability to scale up through cloud data warehousing, allowing for faster and more reliable insights.

fivetran advantages

You can more easily manage the data transformation process when turning unstructured data into structured and semi-structured data formats. More importantly, Fivetran utilizes the Extract, Load, Transform (ELT) data integration process for near real-time reporting and more accurate decision-making.

Fivetran connects with dozens of data sources including Google Drive, Hubspot, Salesforce, Amazon S3 and many others. Numerous enterprise-level brands like Morgan Stanley, Lufthansa Airways and Asics have used the data pipeline to manage different data categories better.

For a more streamlined data management process handling structured, unstructured and semi-structured data, Fivetran offers a reliable and secure solution.

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