Structured vs. unstructured data: A complete guide
Data is the lifeblood of business, and it comes in a huge variety of formats — everything from strictly formed relational databases to your last post on Facebook. All of that data, in all different formats, can be sorted into one of two categories: structured or unstructured data.
Structured vs. unstructured data can be understood by considering the who, what, when, where, and the how of the data:
- Who will be using the data?
- What type of data are you collecting?
- When does the data need to be prepared, before storage or when used?
- Where will the data be stored?
- How will the data be stored?
These five questions highlight the fundamentals of both structured and unstructured data, and allow general users to understand how the two differ. They will also help users understand nuances like semi-structured data, and guide us as we navigate the future of data in the cloud.
What is structured data?
Structured data is data that has been predefined and formatted to a set structure before being placed in data storage, which is often referred to as schema-on-write. The best example of structured data is the relational database: the data has been formatted into precisely defined fields, such as credit card numbers or address, in order to be easily queried with SQL.
Pros of structured data
There are three key benefits of structured data:
- Easy use by machine learning algorithms: The largest benefit of structured data is how easily it can be used by machine learning. The specific and organized nature of structured data allows for easy manipulation and querying of that data.
- Easy use by business users: Another benefit of structured data is that it can be used by an average business user with an understanding of the topic to which the data relates. There is no need to have an in-depth understanding of various different types of data or the relationships of that data. It opens up self-service data access to the business user.
- Increased access to more tools: Structured data also has the benefit of having been in use for far longer; historically, it was the only option. Data managers have more product choices when using structured data because there are more tools that have been tried and tested for using and analyzing structured data.
Cons of structured data
The cons of structured data are rooted in a lack of data flexibility. Here are some potential drawbacks to the use of structured data:
- A predefined purpose limits use. While on-write-schema data definition is a large benefit to structured data, it is also true that data with a predefined structure can only be used for its intended purpose. This limits its flexibility and use cases.
- There are limited storage options. Structured data is generally stored in data warehouses. Data warehouses are data storage systems with rigid schemas. Any change in requirements means updating all of that structured data to meet the new needs.This results in massive expenditure of resources and time. Some of the cost can be mitigated by using a cloud-based data warehouse, as this allows for greater scalability and eliminates the maintenance expenses generated by having equipment on-premises.
Examples of structured data
Structured data is everywhere. It’s the basis for inventory control systems and ATMs. It can be human- or machine-generated.
Common examples of machine-generated structured data are weblog statistics and point of sale data, such as barcodes and quantity. And don’t forget spreadsheets — a classic example of human-generated structured data.
What is unstructured data?
Unstructured data is data stored in its native format and not processed until used, which is known as schema-on-read. It comes in a myriad of file formats, including email, social media posts, presentations, chats, IoT sensor data, and satellite imagery.
Pros of unstructured data
As with the pros and cons of structured data, unstructured data also has strengths and weaknesses for specific business needs. Some of its benefits include:
- Freedom of the native format: Because unstructured data is stored in its native format, the data is not defined until it is needed. This leads to a larger pool of use cases, because the purpose of the data is adaptable. It allows for preparation and analysis of only the data needed. The native format also allows for a wider variety of file formats in the database, because the data that can be stored is not restricted to a specific format. That means the company has more data to draw from.
- Faster accumulation rates: Another benefit of unstructured data is in data accumulation rates. There is no need to predefine the data, which means it can be collected quickly and easily.
- Better pricing and scalability: Unstructured data is often stored in cloud data lakes, which allow for massive storage. Cloud data lakes also allow for pay-as-you-use storage pricing, which helps cut costs and allows for easy scalability.
Cons of unstructured data
There are also cons to using unstructured data. The biggest challenge is that it requires both specific expertise and specialized tools in order to be used to its fullest potential.
- Data science expertise: The largest drawback to unstructured data is that data science expertise is required to prepare and analyze the data. A standard business user cannot use unstructured data as-is due to its undefined/non-formatted nature. Using unstructured data requires understanding the topic or area of the data, but also of how the data can be related to make it useful.
- Specialized tools: In addition to the required professional expertise, unstructured data requires specialized tools to manipulate. Standardized tools are intended for use with structured data, which leaves a data manager with limited choices in products — some of which are still in their infancy — for utilizing unstructured data.
Examples of unstructured data
Unstructured data is qualitative rather than quantitative, which means that it is more characteristic and categorical in nature.
It lends itself well to use cases such as determining how effective a marketing campaign is, or to uncovering potential buying trends through social media and review websites. Because it can be used to detect patterns in chats or suspicious email trends, it’s also very useful to organizations in assisting with monitoring for policy compliance.
Structured data vs. unstructured data
The difference between structured data and unstructured data comes down to the types of data that can be used for each, the level of data expertise required to make use of that data, and on-write versus on-read schema.
Structured data is highly specific and is stored in a predefined format, where unstructured data is a compilation of many varied types of data that are stored in their native formats. This means that structured data takes advantage of schema-on-write and unstructured data employs schema-on-read.
Structured data is commonly stored in data warehouses and unstructured data is stored in data lakes. Both have cloud-use potential, but structured data allows for less storage space and unstructured data requires more.
The last difference may hold the most impact. Structured data can be used by the average business user, but unstructured data requires data science expertise in order to gain accurate business intelligence.
What is semi-structured data?
Semi-structured data refers to what would normally be considered unstructured data, but that includes metadata that identifies certain characteristics. The metadata contains enough information to enable the data to be more efficiently cataloged, searched, and analyzed than strictly unstructured data. Think of semi-structured data as in between structured and unstructured data.
A good example of semi-structured data vs. structured data would be a tab delimited file containing customer data versus a database containing CRM tables. On the other hand, semi-structured data has more hierarchy than unstructured data; the tab delimited file is more specific than a list of comments from a customer’s Instagram.
How is structured data different from unstructured data?
What is next for your data?
Regardless of whether you choose to use structured or unstructured data, quality is a must to keep your data as a reliable source of truth. Data quality is best created using established data governance practices and data management techniques.
Choosing an experienced partner can help you to achieve a better quality for all your data. Talend Data Fabric offers a complete suite of tools that help users collect the data they need, ensure data integrity, and create quality without sacrificing efficiency or security. Begin to unlock your data choice’s potential with the right tools — try Talend Data Fabric today.
Ready to get started with Talend?
More related articles
- Data literacy benefits: Understanding the advantages and disadvantages
- Data Literacy Tools: Understanding how they work together
- Developing and applying data literacy in business
- Mastering Data Literacy: Developing Data Literacy Skills
- The ultimate guide to data literacy training
- Data literacy framework: a guide to creating an effective data literacy program
- Data Literacy: What is it? And why is it important?
- Data literacy in education: Understanding the benefits, advantages, and barriers
- What is data masking?
- Building a Data Governance Framework
- Data governance with Snowflake: 3 things you need to know
- Data Governance Tools: The Best Tools to Organize, Access, Protect
- Data governance framework – guide and examples
- Five Pillars for Succeeding in Big Data Governance and Metadata Management with Talend
- What is a data catalog, and do you need one?
- What is data stewardship?
- What is Data Governance and Why Do You Need It?
- What is Data Lineage and How to Get Started?
- What is Metadata?
- What is Data Access and Why is it Important?
- What is Data Obfuscation?