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What Is Unstructured Data?
When it comes to data analysis, there are several types of data that are processed by researchers: structured data, semi-structured data, and unstructured data.
Structured data is easier to collect, digest, and analyze, as the data has clearly defined fields, and the order and format of those fields are always the same. This makes structured data the easiest to work with, as researchers know exactly where to find specific information within the dataset.
Semi-structured data is data that is organized in a specific way but does not follow a strict format. The data may have specific fields that need to be filled out, but the order and format of those fields can vary. This makes semi-structured data easier to work with than unstructured data, as there is some structure to the data that can help guide you in finding specific information.
However, it is typically unstructured data that tends to be the goldmine of pertinent market research information. It also accounts for about 80% of the data processed by an organization daily.
How Unstructured Data Works
Unstructured data is commonly known as the “everything else” data since it is data that is not organized in any specific way. It is data that has an internal structure but is difficult to search and often is not structured via pre-defined data models or schema.
There is no specific format that the data needs to follow and no specific fields that need to be filled out. This makes unstructured data difficult to work with, as there is no easy way to find specific information within the data set. Unstructured data can be both human-generated or AI-generated; and textual or non-textual.
Big data—or extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations — often contain large amounts of unstructured data, such as data relating to human behavior or interactions. Other common types of unstructured data include:
Human-generated Unstructured Data
- Text files — Word processing, spreadsheets, presentations, email, logs.
- Email — Email has some internal structure thanks to its metadata, and we sometimes refer to it as semi-structured. However, its message field is unstructured and traditional analytics tools cannot parse it.
- Social Media — Data from Facebook, Twitter, LinkedIn.
- Website — YouTube, Instagram, photo sharing sites.
- Mobile data — Text messages, locations.
- Communications — Chat, IM, phone recordings, collaboration software.
- Media — MP3, digital photos, audio and video files.
- Business applications — MS Office documents, productivity applications.
AI-generated Unstructured Data
- Satellite imagery — Weather data, landforms, military movements.
- Scientific data — Oil and gas exploration, space exploration, seismic imagery, atmospheric data.
- Digital surveillance — Surveillance photos and video.
- Sensor data — Traffic, weather, oceanographic sensors.
Unstructured vs. Semi-Structured vs. Structured Data
Unstructured data is one of three classifications of data, the other two being semi-structured data and structured data.
Datamation—a media company spotlighting data topics—defines semi-structured data as “data that maintains internal tags and markings that identify separate data elements, which enables information grouping and hierarchies. Both documents and databases can be semi-structured. This type of data only represents about 5-10% of the structured/semi-structured/unstructured data pie but has critical business usage cases.”
One example of semi-structured data is email. Despite that, you would need intelligence tools to thoroughly track threads, analyze keywords trends, etc. email provides the capability of searching messages based on content or keywords.
Structured data, which is typically stored in a relational database management system (RDBMS), allows for data to be stored, analyzed, and filtered based on data tag specifications. Datamation emphasizes “this format is eminently searchable both with human-generated queries and via algorithms using a type of data and field names, such as alphabetical or numeric, currency or date.”
The main difference between these three types of data is their ease of searchability. Structured data, being stored in a database, provides easy search access to specific data fields, while unstructured data is more difficult to populate, yet it can provide more insight. Because of this, unstructured data search tools are still in the early development stages.
For example, say there is a software database, like a CRM. Within that database is a customer named John Doe. It has his address, age, total spend, emails between him and his sales rep, and his picture. John’s address, age, and total spend are structured data, the email threads which can be searched through via keyword are semi-structured data, and his picture is unstructured data. It could be argued that a photo of John Doe holds more information about him than his age, address, etc.
When is Unstructured Data Used?
Unstructured data affects every level and department within an organization. Most of them use it in some way or another daily. From the engineering and production departments drawing product diagrams to the marketing department engaging with audiences on social media, every area of an enterprise interacts with unstructured data.
Companies that use unstructured data effectively in their organization are ones that guide and manage it to complete critical company objectives or collect data for product innovation. For example, when a company wants to know what customers think about its new product, it might send out a survey with questions that are open-ended and not easily answerable with a simple rating system.
Why is Unstructured Data Important?
Unstructured data—from documents, social media feeds, digital pictures and videos, audio transmissions, and other unstructured content from the web—can provide substantial insight for decision-making and campaign objectives. Unstructured data is often difficult to process, but it can provide valuable insights into how your business is performing.
If companies fail to manage and utilize unstructured data, they run the risk of overlooking key market research that allows them to remain competitive. By analyzing customer feedback, for example, you may be able to identify new opportunities or trends in your industry. Alternatively, you may find that certain products or services aren’t selling well and need to be reworked or discontinued.
Structured data is easily trackable and searchable because it is easily stored in AI databases. Unstructured data is everything else—photos, videos, diagrams, social media engagement, etc. —that is not so easily stored and searched.
Every area of an organization utilizes and interacts with unstructured data daily, from engineering to marketing to accounting. It can even help CEOs make high-stakes decisions regarding the direction of a company.
About 80% of the data used by organizations is unstructured. Companies that effectively guide and manage unstructured data can use it to drive their bottom line. Therefore, the importance of unstructured data is limitless, which is why effectively managing it with AI will be a focus in the near future.