Imagine you’re going on a run. You strap on your Apple Watch, pop in your earphones, and turn on your favorite motivational music as you head out the door.

As you run, the many sensors on your watch are tracking everything: the speed of your run, the movements you make, your precise GPS location, what music you’re listening to, the altitude of your location, your heart rate, your blood oxygen level, how many times you’ve changed songs, and more. That’s all thanks to the device’s built-in wifi, Bluetooth, GPS, accelerometer, gyroscope, heart rate monitor, barometer, altimeter, compass, SpO2 sensor, and VO2Max sensor. 

Every piece of data generated can be analyzed and scrutinized to learn more about you. For example, did you change the speed of your run according to the tempo of your music? That’s a powerful insight into your behavior that you might not even be aware of. 

Insights like these can be used for everything from shaping the ads you’re shown to detecting heart problems early on. This example is just a fraction of the potential for unstructured data.

Unpacking Unstructured Data

Information that either doesn’t have a predefined data model or isn’t organized in a predefined manner is “unstructured” data. It’s always been around, but it’s making up a larger portion of the data we create today. 

In the early days of computing, most databases were relational databases. In simple terms, the databases looked like spreadsheet tables. As a result, they weren’t very flexible when it came to accommodating anything other than structured data.  

Advances in technology have created more forms of data that can’t be placed neatly in strictly relational databases. Long-form text, audio files, image files, and video files are just a few examples of the kinds of unstructured data generated today. While SQL databases are efficient for relational data, NoSQL databases have emerged to serve the needs of unstructured data. This data continues to expand in volume, thanks to the declining costs associated with storing and transmitting it.   

How Machine Learning Supports Unstructured Data Analysis

So, how can you work with unstructured data? By nature, it has historically been quite difficult to analyze. Artificial intelligence (AI) and machine learning (ML) algorithms are making it easier. 

AI and ML algorithms can extract insights from seemingly unrelated sets of information. By harnessing huge amounts of computing power on vast amounts of data, they produce insights that can help drive better business decisions.

Take YouTube, for example. Every single minute, users upload 500 hours of video content to the platform. The amount of video uploaded in one day on YouTube couldn’t be viewed by a single person in their entire lifetime. As a result, YouTube needs a way to filter videos to deliver the best content to its users, with minimal effort on their part. 

YouTube relies heavily on machine learning algorithms to filter videos according to various categories and criteria. In addition, it needs to be able to remove objectionable and explicit content, as well as enforce copyright protections on each of the uploads.

If YouTube had to employ humans for these tasks, it would need millions of employees. Machine learning algorithms make it easier to analyze large volumes of unstructured data like videos. YouTube can quickly understand video content to serve it up to the appropriate users. It’s also able to optimize the performance of ads shown on its videos to drive a high ROI from them. 

More Unstructured Data Use Cases

Big technology firms like Apple and Google (which owns YouTube) aren’t the only ones harnessing swaths of unstructured data to improve products and services. Businesses of all sizes can leverage big data, cheap computing power, and AI/ML algorithms to drive better business outcomes from unstructured data. Here are a few examples.

Public Transport

Unstructured data can be utilized to run public transportation systems more efficiently. Historical ridership data and live data can be analyzed in real time to schedule public transport systems more dynamically. 

Take a subway system, for example. Historical data can help identify rush periods, such as on weekdays before and after regular office hours. Further, unstructured data analysis might determine that demand on weekends is more sporadic and often busier at night. The transportation agency can use this data to design different schedules for different days.

The transportation agency can also leverage real-time data. Swipes from subway passes, tickets generated at various stations, and even CCTV footage from different stations can help determine the passenger volume the system is about to experience. The agency can allocate more trains to busier routes to increase efficiency. A similar data-oriented approach can be taken for other systems, such as bus transportation, taxi systems, and waterways.


Logistics is a complex business. Supply disruptions caused by the recent lockdowns and Suez Canal blockage have showcased how important the supply chain and logistics are to our everyday lives. The logistics industry has a wealth of historical data at its fingertips. Yet, in the past, it’s proven difficult to analyze and make use of this information. 

Now, historical shipment data and live sales data can help support various aspects of the supply chain. Some of the ways in which this unstructured data can help the logistics industry include:

  • Planning vehicle transportation requirements
  • Estimating required inventory levels at warehouses and distribution centers
  • Predicting weather patterns to estimate delivery dates
  • Assigning the most efficient layout for warehouses, according to sales data
  • Helping to improve order picking efficiency 
  • Reducing the distance associates have to walk inside the warehouse by calculating the shortest paths to pick orders
  • Selecting the most efficient transportation modes between air, road, rail, and water, according to costs, customer requirements, and other external parameters

End-User Experience Management

Technology applications can also benefit from the analysis of vast amounts of unstructured data. 

Users interacting with digital applications can be tracked using different methodologies. Every click, mouse movement, and other user action can be logged and analyzed. This data can be paired with performance data from devices used to access the application, forging a new discipline called end-user experience management.

The insights gained from these different sources of data can help improve the current services of the digital platform. They can inform the development of new products and extensions for current applications, driving improved user experience, the acquisition of new customers, and the retention of existing customers for longer periods.

Tapping the Potential of Unstructured Data

Unstructured data is experiencing unprecedented growth. And it needs to be stored somewhere. Flash storage delivers the performance and efficiency needed to power modern storage solutions and address the requirements of modern data. 

Connected devices, ubiquitous internet, and AI/ML are all coming together to make gathering and analyzing unstructured data a profitable endeavor. From text mining and call center data to advanced analytics and business intelligence, the possibilities are powerful. But, the right underlying infrastructure is critical to take on this data and carry out these endeavors.

Unified fast file and object (UFFO) storage can handle the complexities of modern unstructured data. It strikes a great balance between speed, performance, and cost for large-scale analytics applications. 

When it comes to UFFO, Pure Storage® FlashBlade® is the industry’s most advanced all-flash storage solution for consolidating fast file and object data. This fast, scalable storage solution is ideal for pairing with an agile, scale-out architecture. 

Take a free test drive to see the difference today—and to see what unstructured data can do for your business.