Structured vs Unstructured Data, Uses in the Business World
From mobile devices to computers and televisions, all major technology products and services that are currently available to consumers around the world today function in accordance with some form of data. To this point, the term data has become an umbrella term in recent years, as data forms the foundation of virtually every internet or mobile service within the business world. This being said, this influx of information has also resulted in the development of new hardware and software programs, in conjunction with the so-called big data revolution. More specifically, this data revolution has allowed software developers to return to ideas and concepts that had been theorized within the fields of artificial intelligence and mathematics many decades ago.
A common example of such ideas are artificial neural networks, as the mathematical concepts that supported and enabled the creation of artificial neural networks in recent years were first theorized in the 1940s, a time when the computing power that we currently have today was not available. Likewise, these neural networks can process massive amounts of data, in the same way that a human brain can process millions of thoughts and emotions over the course of an individual’s lifetime. With all this being said, the two types of data that have led to new advances in the fields of machine learning and artificial intelligence include structured data and unstructured data.
Structured data is defined as a data set that has been formatted to fit within a particular structure as it relates to data storage, as the name suggests. Structured data is perhaps best exemplified by the data that is contained within a relational database, as the information within these databases will be organized into specific categories or fields. A common example of relations databases is the databases that are maintained by large-scale retail corporations, as these databases with be filled with different information pertaining to a specific product, such as the size, color, style, and release date of an athletic shoe, among other pertinent information. Due to the structure of this data, it can easily be fed into an algorithm that has been created to achieve a particular machine learning task, such as object recognition or natural language processing (NLP), in addition to many others.
On the other hand, unstructured data is defined as data that is kept in its native format and left unprocessed until it is being used. This is also known as schema-on-read, and essentially means that the data will be applied to a particular plan or schema as it is retrieved from a storage location, in contrast to a relational database that is instead used to inform or create such a plan or scheme. Moreover, unstructured data is of a qualitative nature when compared to the quantitative nature of structured data. Common examples of unstructured data include audio and video files, as the determining factors in these files will often be the quality of said files.
Pros and cons
As is the case with any comparison that is made between two objects or pieces of information, there are both pros and cons to using structured and unstructured data. Starting with structured data, the primary benefit of using such data is that the organized nature of structured data allows users to manipulate the data to fit within the confines of a particular object or goal. In staying with the example of machine learning, Customer Relationship Management (CRM) is predicated on the application of structured data, as every customer that shops or purchase products or services from a particular company will have their own defined set of desires and needs. However, one con of structured data is the means by which such data must be stored. Subsequently, structured data will often be stored in a data warehouse, where even making small changes can cost huge amounts of time and resources to be expended.
Alternatively, the primary pro of using unstructured data is the level of flexibility that such data offers. As unstructured data is not confined to a specific format, this data can be adapted and implemented in ways that structured data can not. To illustrate this point further, social media messages on a platform such as Twitter can be used to send a wide array of different messages, including business communications, a birthday message, or a shopping list, among a host of others. What’s more, due to the undefined nature of unstructured data, such data can also be accumulated much quicker than structured data, which often involves some form of labeling, be it manual or automated. Nevertheless, one of the main cons of unstructured data is the level of expertise that is required to utilize such data, as well as the specialized tools that are needed to facilitate such utilization.
Irrespective of whether a software developer or business owner chooses to use structured or unstructured data, the possibilities that can be realized through the application of these data types are truly endless. From popular AI assistants that have been created by huge international corporations such as Amazon, Apple, and Microsoft, to self-autonomous vehicles that have been developed by automotive and clean energy company Tesla, new and cutting-edge products are being created through the leveraging of structured data and unstructured on an annual basis. Furthermore, new advancements that have yet to be discovered will surely be on the horizon in the near future.