Over the last decade, unstructured data has exploded—and so have the use cases for this treasure trove of information. It’s growing and it’s complex. According to Statista and data from IDC, the total volume of data created, captured, copied, and consumed worldwide by 2024 will cross 149 zettabytes every year—and much of it will be unstructured.

Every organization stands to benefit from unstructured data—to power modern apps, for next-gen insights, and to make business breakthroughs. But first, they need a way to get a handle on it. And when it comes to modern unstructured data, many of the traditional storage architectures, technologies, best practices, and principles of structured data won’t apply. This is new territory.

What Is Unstructured Data?

Unlike structured data, such as Excel files or SQL databases, unstructured data is data that doesn’t fit neatly into formatted tables. It is generally in the form of files and objects. This includes:

  • Internet of things (IoT) data, like sensor data, ticker info, and more
  • Device and network data, such as telemetry and location data
  • Text and documents that require context to process and extract data from, such as notes from a customer service rep in a call center
  • Visual data, such as images and video
  • Audio data
  • Rich data, such as weather data and spatial analysis data
  • Data generated by social media activity, including user activity, sentiment analysis of comments, ad clicks, and demographics

Why Unstructured Data Is Exploding

Humans and machines generate data every minute. Billions of people around the world interact with various digital devices every day. Each device—and every activity carried out on that device—generates copious amounts of data. Every swipe, keystroke, and click is a data point. This amalgamation of data, across billions of people around the globe, amounts to zettabytes (1021 bytes) of information every year.

This is modern data, and it’s projected that it will account for at least 80% of all data—including Enterprise Data—by 2025.

If you’re not already doing the “human housekeeping” required to manage the growing volume of unstructured data—such as creating a taxonomy for every type and format coming in—its sheer scale will increasingly be a bottleneck you can’t work around.

Challenges with Analyzing Unstructured Data

That said, although unstructured data can provide significant insight with huge transformative potential, accessing and leveraging it proves the saying, “No pain, no gain.” Some common challenges include finding relevance from data, discerning the quality from the quantity, and identifying causal relationships between unstructured data.

The nature of unstructured data makes it difficult to know what’s relevant. Collecting and storing huge amounts of data without discretion means a lot of irrelevant information gets caught up in the mix and must be eliminated. Modern machine learning techniques are much more effective in gaining insights from unstructured data, but those models are still incapable of finding causal relationships. This not only affects the output of unstructured data analysis but also could lead to business decisions being made based on unproven trends or faulty insights.

Challenges Storing Unstructured Data

One final piece of the “structured vs. unstructured” data conversation is the issue of storage. Generally speaking, you’re going to be up against the volume challenges mentioned above, which will require a scale-out architecture to seamlessly scale alongside your data’s growth. But there’s also the challenge of variety.

Data can be stored in blocks, files, and objects:

  • Block storage. Here, files are broken down into blocks and placed in the storage medium. A simple unique identifier assigned to each block allows the data to be reassembled when it’s time to retrieve it (no matter where the individual blocks get stored. The advantage of a system like this is that data can be chopped and spread across different environments. The disadvantage is the high cost and inability to handle metadata. Block storage is more likely to be used for structured data, such as relational databases for ledgers or transactions.

Unstructured data, however, is primarily stored as file storage and object storage:

  • File storage. In this case, data is stored in files that are located within folders and subfolders. Computers find the data using specific paths to the files. While this is a fast option for reading and retrieving data, you can’t scale your storage without adding systems. Increasing capacity alone won’t suffice.
  • Object storage. Lastly, object storage also divides up data into small chunks and spreads it around the hardware. But the difference, in this case, is that there is no hierarchy (like file storage) or interconnections (like block storage). Each chunk of data acts as a discrete unit. As a result, it can be implemented with simple APIs and scaled easily. The drawback is that objects can’t be modified once they’re written.

The Potential for Unstructured Data

All of this data holds the keys to understanding and shaping the customer journey. Usage behavior can be studied to create better products, understand users more deeply, better identify their interests, and recommend products with greater accuracy. But you’ll need modern solutions underpinning your efforts.

The good news: It’s possible to combine both the unstructured storage approaches and consolidate different data types, as in the case of matching the fast access of file storage with the scalability of object storage. Unified fast file and object (UFFO) storage provides exactly that, and it’s an ideal solution for storing vast quantities of unstructured data. FlashBlade®, the advanced UFFO storage solution from Pure Storage®, offers the speed associated with flash storage technology, as well as the ability to scale any architecture in an agile fashion.