In this blog, we’ll go over what unstructured data is, why it’s so hard to work with, and then show you ways that AI (and M-Files specifically) can help your company harness this data. Let’s jump in!

What is Unstructured Data? 

Unstructured data can be most easily defined by first looking at its opposite: structured data. Structured data is data that can be easily mapped, entered into a database or spreadsheet, sorted, and searched. It easily fits into data storage software (in row-and-column form, for instance). Structured data is easy for machines to work with.

Unstructured data is … everything else. In fact, reports have shown that unstructured data makes up 80% or more of many companies’ datasets. Unstructured data doesn’t fit easily into spreadsheets or databases. You can think of unstructured data as data that humans, not machines, work with.

Here are some examples:

• Email messages
• Document or presentation files (PDFs, PowerPoints, etc.)
• Social media data
• Text messages
• Phone recording
• Digital media (photos, audio recordings, videos)
• Surveillance data
• Geo-spatial or weather data
• Internet of Things data (sensor data, ticker data)
• Analytics data

Many of these data sources can be invaluable for your business—if you can just find a way to access and record them.

Challenges of Working with Unstructured Data 

As you can probably guess, making unstructured data useful poses many challenges. Here are some difficulties businesses face when using unstructured data:

• Daunting scale: Many sets of unstructured data—such as email correspondence, temperature readings, and large video files—are gigantic and require vast storage space. Raw datasets, in particular, can be absolutely massive.
• Legacy architecture: For many companies, valuable unstructured data is tied up in legacy systems that may be difficult to use and siloed off from the main data flow of the organization.
• Data Silos: Unstructured data is unlikely to be consolidated in one place. In fact, it may have no connection to the company’s other data storage at all. For example, email conversations are often completely unconnected from a business’s ERP or other data storage software systems.
• Lack of categorization or “rules”: Many companies do not have a set policy for what to do with unstructured data. If the data is stored at all, it often isn’t categorized or routinely sent to the right place for analysis and use.
• Manual processes: When unstructured data is used, it usually requires extensive manual processes to be analyzed and used properly. This takes up valuable employee time—and will likely decrease employee satisfaction if the processes are monotonous and frustrating.

So, what are some ways to overcome these challenges? Thankfully, the advent of AI has opened up exciting new possibilities in the realm of unstructured data analysis. But before we get too far, there are a few steps that any company must take to prepare for using AI in their data systems.

How to Prepare Your Unstructured Business Data for AI 

While each company’s situation is different, there are some general principles to consider when preparing your unstructured business data for AI. In the first place, you’ll want to consolidate your data as much as possible. This may only be possible through a software solution such as a document management system. The more things are in one place, the easier it will be for AI to analyze that data.

You’ll also want to consider moving as much unstructured data as you can to cloud storage. One of the biggest hurdles to storing unstructured data is space limitations, so it makes sense to store this data on the cloud, where there is much more storage available.

This last step is the hardest part: where possible, you should attempt to translate unstructured data into structured data. Are there concrete numbers, ordinal values (“yes” or “no”), or other structured datasets hiding within your unstructured data? If so, it’s worth extracting that information, as structured data will always be easier to analyze than unstructured data.

AI to the Rescue 

Now let’s turn to AI and how it can help your business gain insights from unstructured data. One of the best strategies to harness the power of unstructured data through AI is by implementing a document management system (sometimes also called an information management system). These systems take files—unstructured data—and use AI to classify documents, assign metadata to them, and provide helpful summaries of the content. Most systems also have some sort of an AI chatbot that can search through documents and provide answers to “natural language” questions from employees.

Some products, like M-Files, take things even further by integrating with other software solutions within your business. For example, M-Files for Outlook Pro can save entire email chains as searchable data—and can even automatically assign metadata like the sender or recipient. New AI features include pattern recognition algorithms that recognize certain people or objects in digital images or videos as well as speech-to-text transcription that can transform a Zoom meeting or a video into text.

Some Challenges to Keep in Mind

While AI has tremendous potential to revolutionize unstructured data analysis, there are a few caveats we need to go over. In the first place, AI doesn’t automatically understand your business context. Standard GenAI like ChatGPT are trained on the textual data, and these systems are great at summarizing documents or coming up with text that conforms to a specific genre. However, they aren’t good at classifying data. AI systems can “hallucinate,” and they lack contextual data, both of which mean that you need to find a software that’s specific for your industry (or at least business type) and that’s trained on your specific business data.

Secondly, AI features in business software systems have the same problems that AI everywhere faces: hallucination and bias being two of the primary issues. AI can sometimes hallucinate, or “make stuff up,” providing blatantly untrue answers. One way to combat this tendency is to invest in an AI system that cites its sources, providing links to the actual documents that it’s pulling information from. AI is also susceptible to certain biases. There have been instances of racial bias in AI systems that analyze images, for example. AI is not a perfect solution and should be used with caution and a careful eye for detail.

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