Structured vs. Unstructured Data: The Key Differences
The two main types of data are structured and unstructured. Structured data is typically alphanumeric, easy to categorize based on shared traits of data points and suitable to store in a predefined data model, such as a database. Unstructured data is usually something other than an alphanumeric representation; it doesn’t fit neatly into a defined, uniform framework; and is often stored in its native format.
The term data embraces nearly anything that can be expressed or represented in digital form. To some degree, the type of data doesn’t matter as much as the context in which it is viewed and its relationships with other bits of data. Context and relationships help turn raw data into useful information.
The ability to manipulate data depends largely on how we classify data elements so that we can see how they relate to broader data sets and the commonalities they have. Data classification enables clusters of data to be searched to discover specific instances that match search criteria.
To achieve all of that requires building uniform structures to hold or define data in a consistent manner — hence the term structured data. But dealing with data isn’t always straightforward, as anyone who has tried to make sense of a massive amount of data will attest. Not all data lends itself to being defined and classified, and that can cause issues.
Today, data is usually much more than an alphanumeric representation of something. It can be video, audio or document files; social media jottings; or email content. These unstructured data types don’t fit neatly into a defined framework and are rapidly becoming the dominant types of data in the enterprise.
To further complicate matters, it’s sometimes necessary to combine structured and unstructured data to derive the desired information. This hybrid approach represents a third general data type — called semistructured data — which is possibly the fastest growing of all three categories.
Structured data is data that, because of what it represents and its format, can be categorized, defined and stored in a consistent structure, such as a database management system (DBMS). The individual data elements of structured data can be constructed in a way that will conform to standard constraints, such as the following: Made up of text, numeric or alphanumeric data. The number of characters constituting the element. The nature of the data elements that enables them to be logically grouped based on their similar or like values. Placing data into the structured environments is sometimes referred to as schema-on-write, meaning data is newly written to or moved to specifically defined slots in a data repository. The definitions are typically narrow to ensure that only like data will be written to the appropriate place in the schema. For example, a ZIP code field may be defined as being numeric and five characters long, thus preventing any data that doesn’t meet those two criteria from being written to that structure. Additional safeguards against spurious data can be put in place by applying filters that further specify an appropriate entry, such as barring a range of ZIP codes that begin with “900.” Another way to ensure that entries conform to a single format is to provide a template for entries. The template effectively forces each entry to appear in a uniform format that facilitates searches. For example, a template for Social Security numbers may force entries to be made in this format: ###-##-####. The ability to group data in this manner makes searching for specific instances of data easy because of the uniformity with which the data is stored. A database of addresses may have the following standard defined fields: first and last name To find and extract only the residents of Oregon from the database, you could search the ZIP code field for all entries that begin with “97.
