In Machine Learning, Visual Search, Data Labeling

What is Unstructured Data?

By Jeff Toffoli

Unstructured data is taking over the world

“90 Percent of the Big Data We Generate Is an Unstructured Mess” - PC Mag

“80 to 90 percent of data generated and collected by organizations, is unstructured” - MongoDB

“Big Data and unstructured data often go together. Unstructured data comprises the vast majority of data found in an organization.” - Merrill Lynch

“Volumes are growing rapidly — many times faster than the rate of growth for structured databases. The global datasphere will grow to 163 zettabytes by 2025 and the majority of that will be unstructured.” - IDC and Seagate

Most data is not organized in a pre-defined way. We call this data unstructured, because it lacks a useful organizational structure. Without a useful organizational structure, your data is probably not going to do you much good. You can think of unstructured data as a house without an address: the house might be nice, but no one is going to use it if they don’t know where it is.



Where is all of this unstructured data coming from?

Broadly speaking, there are two different sources of unstructured data: There is unstructured data generated by people, and unstructured data that is generated by machines.

Digital photos, audio, and video files are some of the most common types of unstructured data that we create in our daily lives. This data is sometimes private, it’s sometimes shared on social media channels, photo sharing sites and YouTube. It's sometimes created by professional media and entertainment organizations. Many business documents are also unstructured, such as records and invoices.

There is also a huge amount of unstructured data that is machine generated. Scientific data, digital surveillance, satellite imagery, geo-spatial data, weather data and various types of sensor data are all generated automatically by machines.

The AI solution

Unstructured data comes in many formats, and it’s a real challenge for conventional software to ingest, process, and analyze. This lack of organization results in irregularities and ambiguities that have made this kind of data useless for companies using conventional approaches to data analysis. Lack of consistent internal structure doesn’t conform to what typical data mining systems can work with.

With the help of AI and machine learning, new tools are emerging that can search through vast quantities of unstructured data to uncover beneficial and actionable business intelligence. AI-powered technology like Clarifai’s Spacetime visual search functions at near real-time speed and custom training can automatically identify the patterns and insights they uncover in unstructured data. In effect, these AI systems can help you transform unstructured data into structured data.


Transforming unstructured data into structured data has many advantages

Structure makes data easier to parse and analyze. A clear pattern or pathway for locating data makes data easy to access.

Once records are held in separate tables based on their categories, it is straightforward to insert, delete or update records that are subjected to the latest business requirements. Any number of new or existing tables or columns of data can be inserted or modified depending on the conditions provided.

Using join queries and conditional statements one can combine all, or any number of related tables in order to fetch the required data. Resulting data can be modified based on the values from any column, on any number of columns, which permits the user to effortlessly recover the relevant data as the result. It allows one to pick on the desired columns to be incorporated in the outcome so that only appropriate data will be displayed. Data can be deduplicated (de-duped), and noisy, irrelevant data can be eliminated.

Structured databases can grow and be modified over time. Changes can be made to a database configuration as well, which can be applied without difficulty devoid of crashing the data or the other parts of the database.

Increased security is also possible once data is structured. It is possible to tag some data categories as confidential and others not. When a data analyst tries to login with a username and password, boundaries can be set for their level of access, by providing admission only to the categories that they are allowed to work on, depending on their access level.

A quick note about semi-structured data

It is probably important to point out that there is a lot of data out there that comes with some organizing properties, even though it may not be fully classified and structured in all of the ways that you want. Oftentimes, data contains internal tags and markings that allow for grouping and hierarchies. Native metadata allows for basic classification and keyword searches. It is common for semi-structured data to come in the form of image and video metadata, email, XML, JSON, or NoSQL.


With recent advances in machine learning and AI, the wealth of information hiding away in unstructured data stores can now be used to guide business decisions, and create a whole new generation of products and services. Companies can tap into value-laden data like customer interactions, rich media, and social network conversations.

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