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 and 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.
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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:
- Easily used 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.
- Easily used 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, as historically it was the only option. This means that there are more tools that have been tried and tested in using and analyzing structured data. Data managers have more product choices when using structured data.
Cons of structured data
The cons of structured data are centered in a lack of data flexibility. Here are some potential drawbacks to structured data’s use:
- A predefined purpose limits use: While on-write-schema data definition is a large benefit to structured data, it is also true that ata with a predefined structure can only be used for its intended purpose. This imits its flexibility and use cases.
- 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.
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Examples of structured data
Structured data is an old, familiar friend. 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. Plus, anyone who deals with data knows about 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 it is 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 there are 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 data scientists to prepare and analyze 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 by 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.
- Data lake storage: 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. It requires specific expertise and specialized tools in order to be used to its fullest potential.
- Requires 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 it is, due to its undefined/non-formatted nature. Using unstructured data requires understanding the topic or area of the data, but also of understanding how the data can be related to make it useful.
- Specialized tools: In addition to the required expertise, unstructured data requires specialized tools to manipulate. Standard data tools are intended for use with structured data, which leaves a data manager with limited choices in products for unstructured data, some of which are still in their infancy.
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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 determining how effective a marketing campaign is, or to uncovering potential buying trends through social media and review websites. It can also be very useful to the enterprise by assisting with monitoring for policy compliance, as it can be used to detect patterns in chats or suspicious email trends.
Structured data vs. unstructured data
Structured data vs. unstructured data comes down to data types that can be used, the level of data expertise required to use it, and on-write versus on-read schema.
Requires data science expertise
Only select data types
Many varied types conglomerated
Commonly stored in data warehouses
Commonly stored in data lakes
Structured data is highly specific and is stored in a predefined format, where unstructured data is a conglomeration 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 could potentially have 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.
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What is semi-structured data?
Semi-structured data refers to what would normally be considered unstructured data, but that also has 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 the go-between of 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 side of the coin, semi-structured has more hierarchy than unstructured data; the tab delimited file is more specific than a list of comments from a customer’s instagram.
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What is next for your data?
Regardless of whether you choose to use structured or unstructured data, data integrity is a must to keep your data as a source of truth. Data integrity is best created using established data governance practices, and using established 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. Begin to unlock your data choice’s potential with the right tools — try Talend Data Fabric today.