Structured data (also known as relational data) refers to data that fits a predefined data model. It can be easily mapped into designated fields. A US ZIP code can be stored as a five digit string (e.g. 90210), a State as a two-character abbreviation (e.g. CA), etc.
Structured data is easily stored and retrieved in a traditional relational database where the management system applies logic to ensure information is in the correct format as it is written to disk.
Unstructured data doesn’t have a predefined data model. Therefore, it’s not as easily categorized into the predefined tables and rows of a relational database. Satellite imagery, audio files, video files, or even emails may have common features, but these rich data types aren’t easily ingested, processed, or analyzed with conventional database related systems.
Learn more about Unstructured data here.
With structured data, every record adheres to a predefined data model; if incoming data fails to meet those definitions, it cannot be saved without correction or truncation. As a result, structured data may often be very text-heavy. This does have the advantage of being extremely easy to parse and search using conventional software.
Unstructured data is more ambiguous. Without a predefined data model, you can store a far broader range of rich data including images, sound, video, and text. As the scope for storage increases, and as data becomes more complex and dynamic, so too does the difficulty with which you can search and analyze that information. Thankfully, there are modern data management platforms, such as MongoDB Atlas, that make it easier to store and process large amounts of unstructured data.
Because of its relative simplicity, structured data is well suited to the relative limitations of relational database systems. Large data estates can be housed in a data warehouse — as long as the information continues to meet the rigid database schema.
Unstructured data can be, and is, stored in a number of places. Specific applications like email servers often create their own data silo of unstructured information. Data warehouses and data lakes have become important for big data analytics, providing a way to increase overall capacity using low-cost commodity storage. For analyzing complex data types, or for advanced data analysis, NoSQL databases offer a way to more efficiently manage and search across disparate data sets.
Structured data is best suited to process-driven applications that rely on specific information presented in a known, consistent format. An inventory control system that maintains stock levels against product SKUs is an ideal example because it operates using concrete information. The logic built on top of the database may be complex, but the records themselves are very simple.
Unstructured data and applications powered by unstructured data tend to be more ambiguous; email clients that store messages of varying lengths that may include attachments. Or presentation software that blends text, graphics, and multimedia content. Potentially high value information is held in these assets, but it cannot be retrieved using regular text queries from a traditional relational database.
The linear, controlled nature of structured data is best suited to statistical-type big data analytics using similarly structured query language (SQL). If you want to know which product line sells best during the summer months or which manufacturing component is likely to fail next, a regular relational database will perform adequately.
Unstructured data can also generate these insights — and a lot more. Going beyond raw statistics, unstructured data can (with the right NoSQL database), can provide more advanced insights, like customer sentiment. It can also provide enough structure so that non-text assets can be queried, allowing you to run facial recognition analysis from photographs for instance.
In the big data analytics environment, this additional layer of information provides much-needed context and insights that are not available from raw statistics and SQL-based, sanitized data sets. Further, the no-loss storage of unstructured data means that details remain intact even as your data needs and strategies change.
Structured data will undoubtedly retain a place in most business operations for the foreseeable future. But as information management continues to evolve, and user data becomes more complex, the additional context provided by unstructured data will prove vital. The ability to store, query, and analyze information from any source opens new opportunities — and this is where future success awaits.
Discover how to unlock the hidden value of Unstructured Data for yourself with the free tier of MongoDB Atlas.