Key Takeaways
- Structured data is stored in a schema, a plan of how the data will be stored and used by software that manipulates structured data.
- Unstructured data is not defined and stored in its native format.
- Microsoft Word saves its file with a .doc extension, and an adobe file is saved with a .pdf extension, the native format for these software tools.
To get the most out of an organization’s data, Information Technology (IT) managers and executives need to understand the types of data used by an organization. IT managers that understand the types of structured vs. unstructured data available will help them make better decisions when aggregate data is used from both data categories.
In this article...
What are structured and unstructured data types?
Structured data is created from a pre-defined format when a user has some idea of what data columns will be included in the structured data. Structured data is stored in a tabular form with specific columns that can be text, numeric, or date columns. Each of these columns can be formatted to accept data in particular formats.
Relational Database Management Systems (RDBMS) such as Microsoft (MS) SQL Server or an Oracle Database are popular software tools for structured data for large organizations. SQLite or MySQL are RDBMS tools that small businesses can use.
ALSO READ: How Businesses use Structured vs. Unstructured Data
Unstructured data types are word documents, emails, adobe PDF files, social media posts, and video or audio files. Any data that is not considered structured data can fall into the category of unstructured data, such as presentations, chats, sensor data, and satellite imagery.
Unstructured data is stored in its native format. All unstructured data is saved in its native format by the software that created the file, and 80-90 percent of all business data is unstructured.
Structured vs. unstructured data advantages and disadvantages
Regardless of the type of data businesses use, IT managers need to know the strength and weaknesses of the data types to exploit the advantages of structured and unstructured data.
The advantages and disadvantages of structured data
The advantage of structured data is it’s generated by a variety of business applications that are used daily in a business environment. Entry-level users can use basic software tools like MS Access, Excel, and more experienced users can use MS SQL Server or business intelligence (BI) tools to manipulate data.
Structured data has a wider variety of RDBMS software and analytical tools available to support it since it has been around for decades. Artificial Intelligence (AI) tools can quickly generate queries due to how structured data is stored.
Structured data is not flexible and can only be used for its intended purpose, which is a significant disadvantage. Another pain point of structured data is the complex alteration it must go through before a flexible data store can use it. As a business grows, the number of databases and tables proliferates, lending itself to overlapping datasets and redundant data with complex relationships between tables.
The advantages and disadvantages of unstructured data
Since unstructured data is in its native format, no processing is required before using it. As a result, unstructured data is flexible and can be used for different purposes.
Another advantage of unstructured data is the low overhead associated with storing and processing the data. When appropriately used, unstructured data can provide better insight into a business’s overall progress that can become a competitive advantage.
A disadvantage of unstructured data is that it requires advanced analytics to derive meaningful information for a business. In addition, retrieving, processing, and analyzing unstructured data requires advanced tools and data science skills to generate insightful information.
Quick summary:
- Structured data has been around for a long time and is easy to use.
- Getting relevant information from unstructured data requires an experienced data scientist familiar with the latest AI tools.
ALSO READ: 5 Best Data Storage Solutions for BI
Similarities and differences between structured and unstructured data
All data belongs to a business and can add value to a company using quantitative or qualitative data. Each data type can represent a comprehensive business overview from an employee, supplier, and customer perspective.
Similarities between the two data types
The similarity between the structured vs. unstructured data is they belong to a business. The proprietary business data needs to be securely protected by the owning organization with the proper cybersecurity controls in place.
Both data types can be used to improve the business through continuous process improvement (CPI) practices, making the data valuable to businesses.
Differences between the two data types
The main difference between structured and unstructured data is that structured data uses a defined format, and unstructured is saved in its native format. Structured data is quantitative and is used to show monetary gains and losses for organizations, which is numbers-based, countable, or measurable.
Unstructured data is qualitative and generally descriptive or interpretation-based, so it can tell the why, how, and what happened in certain business situations.
Another difference between the two data types is that structured data is easier to search by a person or a created algorithm. However, to exploit and retrieve meaningful data from unstructured data, businesses will require a person with data scientist skills that can use advanced analytical techniques like data mining and data stacking.
Additionally, structured data is stored in a data warehouse, while unstructured data is stored in a data lake, which has more storage capacity for all data types.
Quick summary:
- Structured and unstructured data add value to a business but are used differently.
- Combining quantitative and qualitative data allows management to make decisions beyond raw Return on Investment (ROI) gains.
- A third category of data that may be stored in a data lake is called semi-structured data. Semi-structured data does not have a fixed schema but uses tags and business metadata to help define its semi-structured data. HTML code and XML documents are examples of semi-structured data.
Software tools used to manipulate structured vs. unstructured data
Relational database management tools have been around as long as structured data. Businesses can use analytical tools to manipulate and analyze structured and unstructured data in today’s environment.
Artificial Intelligence and its associated cohorts under AI are Machine Learning (ML) and Natural Language Processing (NLP), which play a crucial role in extracting insightful data from structured and unstructured data.
Software tools used for structured data
Any RDBMS software like Microsoft SQL Server can manipulate structured data. Zoho Analytics can connect to an organization’s structured and unstructured data and blend the two data types to provide meaningful information to executives and IT managers. Zoho Analytics also offers the following features:
- Dashboards – visual analysis information with drag-and-drop options.
- Zai AI – Zia intelligent assistant that uses ML and NLP to generate responses.
- Library of mathematical and statistical functions – uses a user-friendly formula engine to help extract business metrics.
Software tools used for unstructured data
Over eighty percent of business data is unstructured. Since unstructured data is stored in multiple formats, a business can use an Extract, Transform, and Load (ETL) software tool to extract structured and unstructured data onto a data lake platform. ETL software can also store data in a data warehouse, a centralized database, or an analytical database for faster queries. Hadoop is one of the many software tools that use ELT to transform and load large amounts of data to a designated repository for further analysis.
Quick summary:
- Voluminous amounts of structured, semi-structured, and unstructured data are known as big data.
- Big data software tools such as Sisense have built-in ETL tools, and Google Cloud Platform uses its embedded set of big data tools to analyze data in data lakes or warehouses.
Characteristics of Structured vs. Unstructured Data
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Selecting the best tools for structured and unstructured data
Emails, reports, and surveys are as important as structured data in an RDBMS system. The benefit of equating unstructured data as valuable as structured data is realizing the value of unstructured data, especially with it being eighty percent of all business data. With a better understanding of all data types in a business, executives and IT managers can formulate a plan to eliminate any data silos. When deciding on a data storage solution, management needs to consider these key features:
- Space and scalability – as a business identifies more unstructured data, additional space may be needed, so scalability is necessary.
- Accessibility – a data storage solution must be easily accessible and able to interface with any other analytical tools used in your business environment.
- Management – understand how the selected data storage vendor will manage your organization’s data and what self-service options are available for a user.
- Security – ensure the selected data storage vendor uses updated security features, including end-to-end encryption.
To help IT managers get started with research for a data storage solution, here are some of the best data storage vendors identified in 2022.
1 Quickbase
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