Unstructured data represents one of today’s most significant business challenges. Unlike defined data – the sort of information you’d find in spreadsheets or clearly broken down survey responses – unstructured data may be textual, video, or audio, and its production is on the rise.
In fact, by some estimates, as much as 80-90% of new data is unstructured, and that presents real challenges from a data management standpoint.
How can businesses make meaning out of unstructured data and generally manage all of the information they’re generating in a productive way? It’s a challenging process, but it is possible with the right array of tools.
The first step towards making use of unstructured data is to find a way to centralize this information, and this is a top priority for many businesses today. In fact, 56% of businesses say that getting their unstructured data into the cloud is a top priority.
Migration is not quite management, but it’s the first step towards organizing and evaluating unstructured data, and that’s important. The biggest barrier to this task, however, is a lack of IT capacity and budget – but businesses that can successfully budget for these expenses may find profitable insights in that data, more than making up for those initial costs.
Find Out What’s Inside
The next aspect to addressing unstructured data is extracting more concrete information from it, and this may be the most complicated element. How do you quantify unstructured data? There are a number of approaches, but AI is one of the most important tools because by using innovations like natural language processing (NLP), the system can identify frequently used terms, evaluate tone, and much more.
Once businesses can see “inside” their unstructured data, there’s a lot to explore. Better data management can drive business growth, and can help provide direction for a number of operational changes. For example, businesses have used information derived from unstructured data to improve safety, advance healthcare outcomes, and automate business facilities based on worker insights – but let’s take a closer look.
One type of unstructured data common in the healthcare industry is imaging, whether that’s a CT, MRI, or an X-ray. Radiologists can be slow in evaluating non-emergent imaging, and the human eye is only so sensitive. When imaging is paired with AI technology, however, facilities can turn over imaging results more quickly and with greater accuracy.
Another area of healthcare that is well-positioned to benefit from better unstructured data management and analysis is drug development. This may seem like a fairly structured area, but given what we know about unstructured data production, pharmaceutical research generates a lot more of it than you might realize. The industry also struggles with prioritizing and organizing such data, which has a negative impact on collaboration and product development in the pharmaceutical industry.
As noted regarding the pharmaceutical industry, collaboration is a critical business function and the inability to collaborate can be a serious hindrance to progress – and this is true across industries. With unstructured data, though, you can’t just send along a data set and, depending on how you aim to evaluate or manipulate the information at hand, businesses often need to be able to exchange, comment on, and modify large files across teams and locations. So, what’s the best way to tackle this task?
If you need to send large files fast while continuing to collaborate, one option to consider is using a cloud-based file storage system. These platforms essentially prevent the need to regularly transfer files by storing them in a shared repository featuring access and privacy controls and ensuring users always have the most recent iteration of the document when collaborating on a document.
Storing and transferring files may not fundamentally reveal much about the content of unstructured data, but as we’ve seen, just centralizing these files remains a serious issue for many businesses. Until basic tasks like migration cease to be top priorities for major corporations, we can’t underestimate the importance of centralized transfer and storage tools.
Experts continue to raise concerns about how and if businesses are making use of unstructured data, but in addition to actively sharing that information, until access to machine learning protocols is more widespread, the ability to effectively utilize this information and derive insights will be compromised. While large retail and finance organizations currently lead in this regard, small businesses are in a challenging position because NLP and other AI tools can still be expensive, especially when they need to be modified to suit unique industry terms or functions. Without the ability to access the insights contained in unstructured data, however, businesses can’t compete in the modern marketplace.
It takes a variety of tools to properly navigate unstructured data, and what tools you’ll need depend heavily on the types of unstructured data that dominate a business’s approach.
At the end of the day, though, the most important thing is that your company makes an active effort to engage the information in your unstructured data while it’s still relevant. So much of what you need to know is in there, just waiting to be unpacked.