What Is Structured Data?
Data that is the easiest to search and organize, because it is usually contained in rows and columns and its elements can be mapped into fixed pre-defined fields, is known as structured data. Think about what data you might store in an Excel spreadsheet and you have an example of structured data.
Structured data can follow a data model a database designer creates – think of sales records by region, by product or by customer. In structured data, entities can be grouped together to form relations (‘customers’ that are also ‘satisfied with the service). This makes structured data easy to store, analyze and search; and until recently it was the only data easily usable for businesses.
Today, most estimate structured data accounts for less than 20 percent of all data. Often structured data is managed using Structured Query Language (SQL)—a programming software language developed by IBM in the 1970s for relational databases.
Structured data can be created by machines and humans. Examples of structured data include financial data such as accounting transactions, address details, demographic information, star ratings by customers, machines logs, location data from smart phones and smart devices, etc.
What you need to know about structured data
- Structured data is quantitative data that consists of numbers and values.
- Structured data is used in machine learning and drives machine learning algorithms.
- Structured data is less flexible and schema-dependent.
- Structured data is stored in tabular formats like excel sheets or SQL databases.
- Structured data has a predefined data model.
- It is formatted to asset data structure before being placed in data storage (e.g schema-on-write).
- Structured data is sourced from online forms, GPS sensors, network logs, Web server logs, OLTP systems, and the like.
- Structured data is stored in data warehouses, which makes it highly scalable.
- Structured data requires less storage space.
- Structured data is easy to search and analyze.
What Is Unstructured Data?
A much bigger percentage of all the data is our world is unstructured data. Unstructured data is data that cannot be contained in a row-column database and doesn’t have an associated data model. Think of the text of an email message. The lack of structure made unstructured data more difficult to search, manage and analyse, which is why companies have widely discarded unstructured data, until the recent proliferation of artificial intelligence and machine learning algorithms made it easier to process.
Other examples of unstructured data include photos, video and audio files, text files, social media content, satellite imagery, presentations, PDFs, open-ended survey responses, websites and call center transcripts/recordings.
Instead of spreadsheets or relational databases, unstructured data is usually stored in data lakes, NoSQL databases, applications and data warehouses. The wealth of information in unstructured data is now accessible and can be automatically processed with artificial intelligence algorithms today. This technology has elevated unstructured data to an extremely valuable resource for organizations.
What you need to know about unstructured data
- Unstructured data is qualitative data that consists of audio, video, sensors, descriptions and more.
- Unstructured data is used in natural language processing and text mining.
- There is an absence of schema, so it is more flexible.
- Stored as audio files, video files or NoSQL databases.
- Unstructured data does not have a pre-defined data model.
- Unstructured data is stored in its native format and not processed until it is used (e.g schema-on-read).
- Unstructured data is sourced from email messages, word-processing documents, pdf files and so on.
- Unstructured data is stored in data lakes which make it difficult to scale.
- Unstructured data requires more storage space.
- Unstructured data requires more work to process and understand.
Difference Between Structured And Unstructured Data In Tabular Form
|BASIS OF COMPARISON||STRUCTURED DATA||UNSTRUCTURED DATA|
|Description||Structured data is quantitative data that consists of numbers and values.||Unstructured data is qualitative data that consists of audio, video, sensors, descriptions and more.|
|Application||Structured data is used in machine learning and drives machine learning algorithms.||Unstructured data is used in natural language processing and text mining.|
|Flexibility||Structured data is less flexible and schema-dependent.||There is an absence of schema, so it is more flexible.|
|Storage Format||Structured data is stored in tabular formats like excel sheets or SQL databases.||Stored as audio files, video files or NoSQL databases.|
|Data Model||Structured data has a predefined data model.||Unstructured data does not have a pre-defined data model.|
|Data Storage||It is formatted to asset data structure before being placed in data storage (e.g schema-on-write).||Unstructured data is stored in its native format and not processed until it is used (e.g schema-on-read).|
|Sourcing||Structured data is sourced from online forms, GPS sensors, network logs, Web server logs, OLTP systems, and the like.||Unstructured data is sourced from email messages, word-processing documents, pdf files and so on.|
|Scalability||Structured data is stored in data warehouses, which makes it highly scalable.||Unstructured data is stored in data lakes which make it difficult to scale.|
|Storage Space||Structured data requires less storage space.||Unstructured data requires more storage space.|
|Search||Structured data is easy to search and analyze.||Unstructured data requires more work to search, process and understand.|