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All the data that we see around us can be categorized into two types, i.e., structured and unstructured data. The former type of data exists in some fixed format of some record. Therefore, it is quite well-structured and easy to search. Thinking of customer contact information first name, last name, and phone number stored in a database with each field labeled would be an example. All of which is, however, unstructured data, that is multimedia files, emails, or texts that may contain a lot of useful information but are harder to search and use.
Growing reliance on data to fuel operations and inform decision-making in enterprises makes this not a choice between structured versus unstructured data; rather, it is the problem of how to collect, store, and process both. This article examines the contrasts between structured and unstructured data and how companies can leverage each for gain.
The very definition of structured data is derived directly from the name, which is a specific format, organization, and easy readability or processing by machines. The structure is usually already predefined and has the same pattern used for all instances of the data. Contrary to this is the unstructured data, which will have no specific formal structure at all, hence more difficult in label-reaching and searching.
Structured data is information that’s highly organized and readable by machine learning algorithms. This makes it easier to search, manipulate, and analyze. You’ll typically find structured data in database tables, rows, and columns. Each field contains a specific type of data corresponding to its category and value.
Imagine a spreadsheet of information with column headings that are defined. In such a format, search algorithms may read and know the data contained therein. The structured data may be names, addresses, and dates defined to be easily recognizable and clear fields. Since every record contains a search key and every field in those records is clearly defined, you may search those records and data fields with standard database searches or analytics programs.
Because structured data is so organized, people and automated tools can easily scan, organize, and analyze huge amounts of it.
It can be generated in several ways and sources. It may come from enterprise software such as CRM systems, accounting programs, other applications involving the critical business operations of an enterprise, online-based sources, social media platforms, web-based surveys, and so on. It can also be originated from manual human input.
The business intelligence tools that are developed based on artificial intelligence and natural language processing derive structured data further from unstructured data.
Unstructured data refers to a form of information that doesn't have an inherent structure or organization. Objects refer generically to pieces of unstructured data; the term "objects" applies because they contain no record keys to identify them. Such data contained within unstructured objects has to be tagged with a "tag" or identifier for each separate object to enable the search and location of this data.
This data includes videos, emails, images, and HTML content. Such data counts for about 80 to 90 percent of all data being created around the world, while it is considerably less valuable compared to structured data as such data is much harder to process and extract insights from.
Unstructured data is developed from a wide variety of sources. An unstructured data object might be free-form text not broken down into a fixed record format containing individual data fields. This can also come in the form of a photo, video, engineering CAD drawing, a social media text stream, an HTML document, or any form of data that is not captured as a fixed record, or field-defined data format.
In some cases, unstructured data may live inside structured data records. For example, consider the form that generates a list of questions from which one selects an answer from a pick list; yet one is also allowed to key in free-form comment replies based upon the pick list are structured data. However, comment fields offer unstructured data.
Semi-structured data falls somewhere between structured and unstructured data because it has some level of organization but does not appear to be fully organized in a fixed record format that exists within a traditional system or database.
For instance, you could impose some structure on a file that is intrinsically not structured by using metadata to indicate who wrote the document and when, as well as keywords for the subject matter allowing it to be searched. In otherwise unstructured HTML files, H1 tags denote titles, and H2 identifies subsections, making those searchable too.
Semi-structured data comes in many formats from a wide variety of sources. IoT sensors generate vast amounts of data that might be used to optimize order fulfillment in a shipping warehouse or to monitor the health and functionality of equipment in a manufacturing environment. Much of it can be made much more useful by adding tags that will allow it to be searched.
There is also semi-structured data from markup languages, such as HTML and XML. Searching through tagged semi-structured data is not as efficient as it is through structured data. However, tagging can be useful in identifying and retrieving documents and other unstructured data objects in site- and technology-level searches by sites and technologies, like AI.
Today's businesses face a strange, almost paradoxical situation: while approximately 80 percent of their day-to-day IT processing is based on traditional structured data, 80 percent of new incoming data is unstructured. This reality calls for hybrid views for companies from a data management standpoint when including methods for the management and processing of both types.
The best and fastest route to universal data management is through tagging all unstructured data objects so they may be semi-structured, essentially making them individually identifiable and searchable.
Organizations also need to include data tagging that could connect or integrate both structured and unstructured data into a composite and hybrid data record. For instance, connecting a picture of a lawnmower with a fixed record description of the item in a sales catalog record file, or appending a company employee ID badge to that employee's fixed record description in a human resources system.
With most businesses seeking to transfer more work into analytics and AI systems, the need to process both structured, unstructured, and unstructured-but-tagged data intensifies.
Today's firms need to employ a hybrid data management approach if they are to effectively manage, store, curate, and consume the large stores of data available to them. A hybrid capability is an ability that uses the combinations of structured and unstructured data in daily IT operations; having data mining options that can identify and link these kinds of varied types of data together.
