Leveraging Unstructured Data Analysis Methods to Extract More Value from Your Data

January 17, 2018

4 minute read

How to Increase Operational Efficiency with OCR AutomationWhen undertaking any kind of unstructured data management project, an organization might overlook the necessity of cleaning up their data. However, this can lead to more serious issues later on when they realize that their body of data has massive gaps—and they have no idea how to fix them. For example, if your organization wants to undertake an overhaul of its business analytics but you don’t know which content to review, you could be in trouble.

To inspire educated decisions, stay in line with compliance measures, and avoid risk, it’s critical to adopt the right unstructured data analysis methods when beginning any kind of data processing project. To increase the success of your data analysis efforts, we’ve compiled some methods that you can use as a framework.  


Tap into your overlooked data

To make sure your unstructured data processing is as effective as possible, you need to make sure it’s being applied to all your data. However, there are some sources of data that fly under the radar, and that might be overlooked in projects of this type.

One of these mystery content types is something that most of us encounter every day: emails. Although opening a thread of emails is something you can do without a second thought, many systems struggle to analyze the information contained within. The same goes for nested emails, which many organizations still need to process manually. Last in the list of email culprits is attachments, particularly TIFF files, which still need to be opened manually in order to be analyzed.

Many organizations may dismiss the need to look into the content of their emails, but this can be a dangerous mindset if the documents within contain PII or other sensitive information.

Even if these files only make up one percent of your unregulated data, not knowing what they contain is dangerous when it could be highly sensitive information. If any data leaks occur, you could be in trouble—even if you weren’t aware of what was in your content. The incoming entry of GDPR makes this requirement an even more critical one, with organizations who aren’t compliant risking hefty fines and penalties. Mandatory disclosure regulations in Canada and abroad outline the need to keep your data safe at every level, which also means knowing what’s contained within it.


Know what data you have

Between file shares, folders on personal computers, USB drives, and more, it can be difficult to know exactly what kind of data you have and where to find it. It’s also challenging to ascertain what kind of data you don’t have, which can be just as important to determine.

Creating one source of “content truth” allows you to quickly and clearly see what you need. Once your content has been cleaned, you can analyze and classify your data to facilitate improved decision-making.

For example, it’ll be easier to find specific data around a particular client requirement if you know that it can all be found with a simple search. It’s also important to have different departments synced up on content requirements, as you might otherwise see misalignment between them when one needs information from another. Finally, this will ensure that your analytics priorities are aligned at every step of the way and that you can start running the appropriate unstructured data analysis methods for the types of data you have available.  


Clean up your unstructured data

Once you’ve sifted through your data, you can start cleaning it up and turning it into a usable corpus of insight. No matter what motivates you to better manage your unstructured data, one thing is for sure: to make any kind of decision, you need clean content.

If you have data in unstructured formats and don’t have a sort-data function, you can’t possibly know what’s contained within that content. This means that you could be facing compliance issues without even realizing it. In this situation, ignorance isn’t bliss. Vast volumes of unstructured data bring massive amounts of risk, as these documents could be sitting in unsecured areas (like the email attachments mentioned earlier) or contain PII that you don’t even know exists.

If you’re planning on undertaking a data migration, cleaning up your data will help you make sure you’re not bringing the same ROT content with you along the way.

Plus, organizations typically upgrade their systems for a reason—and if your data doesn’t follow suit, you won’t garner as much value from your data.  

Regardless of your motivation, if your data isn’t cleaned up before beginning any kind of data processing project, all of the bad practices that were holding you up before will follow you to the new location. Rather than assuming that your problems will go away along with your new processes, it’s important to ensure that bad policies are removed along with ROT data.


How to get started

Remember that, without proper processing, none of your metadata will necessarily be accurate. By being suspicious of any insight it contains, and cleaning it up before making use of it, you’ll save yourself trouble down the line.

That being said, it’s important to note that you can’t just rely on metadata alone—even once it’s been cleaned up—to get the job done. Although metadata does a good job of representing the data within a specific document, some projects will also require a solution that can look inside each document itself. To achieve peak performance throughout your unstructured data processing project, you need to match your use case to the system you’re selecting.

You wouldn’t take a BMW down a dirt track, just like you wouldn’t drive a four-wheeler quad down the highway. The same analogy applies to your content elevation. If you intend to feed an automated document process, such as extracting numbers from each piece of content, the process is much simpler than if you’re planning to overhaul your information governance systems. Knowing whether you need to find very specific information only or whether semantic intent needs to be added to the mix upfront will save you a lot of difficulties down the road.

Once you’ve completed these steps, your unstructured data management project will be well on its way to success. 

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