According to IDC, as much as 80% of all data could be classified as unstructured data. While estimates vary, the majority agree that at least half of the data in most organizations is unstructured in nature. However, this rather murky term tends to create more confusion than clarity for IT staff. So, what is unstructured data?

Four Common Questions About Unstructured Data

What Is Unstructured Data, Really?

Unstructured data is any data that exists outside of structured databases and doesn’t follow a clear data model. Data models allow you to consistently use the data in a given set because that data adheres to strict rules. A good example of structured data with a proper data model would be 3D models.

In a 3D model, each corner is located at a precise point in space, identified by its X, Y, and Z values. These must be numeric values. By connecting each of the points, you get a 3D shape. This data is considered structured because all of those data points follow the same rules and format.

Numbers are extremely easy to structure, which makes them easy to analyze and process for insights. Your Excel files full of figures and facts about your business feed structured data into your BI tools. However, as you can imagine, most of your data isn’t numerical in nature. Fortunately, Aparavi can help you give some structure to your files as we’ll see.

What Are Some Common Types of Unstructured Data?

Most of the data that we use on a daily basis is probably unstructured data. For example, text files or emails are considered unstructured. There are no strict rules for the data in a text file. You could have a single smiley face or a 40-page thesis.

Even though languages have structure inherently, when we use them to make data files they are notoriously difficult to structure. There’s no rule that says how many letters a word should have, and English is full of irregular verbs and awkward spellings. Video and audio are common examples of unstructured data as these also have too many variables to fit into strict models.

That doesn’t mean all unstructured files are completely disorganized. Humans apply some structure to everything they create, whether that’s in the form of headings like in this article or segments of a video. The trouble comes when we ask machines to analyze loose structures when they would rather have a rigid data model.

Can You Analyze Unstructured Data?

The answer is, yes — so don’t delete your unstructured files just yet. In fact, there’s a lot of insights to be gained from unstructured data, if you are using the correct technology. In fact, if you don’t analyze your unstructured data (which you’ll recall makes up 50-80% of enterprise data), you are probably missing out on valuable business intelligence.

Natural language processing is a form of artificial intelligence that can analyze text files and emails to attempt to make some sense of this unstructured data. This technology even has applications for audio and video.

Furthermore, unstructured data often has metadata that can be structured and used to better understand the file. Your Word docs have a record of who created and edited the document, how many words it has, how many pages it would be on paper, and its total size, just to name a few data points. Videos include bitrate, duration, and resolution along with several other variables.

With a powerful search tool like Aparavi, you can identify text within your files and even scour for specific files that meet your very precise requirements. You can also apply classification policies to your files to make it easier to find them later or facilitate future analysis. Aparavi’s search is much smarter than your default OS search function, and it can explore your entire enterprise data system across all storage locations, including core, multi-cloud and endpoint devices on your network.

How Can You Give Structure to Unstructured Data?

Good data management practices can help you handle unstructured data more efficiently. Start by making sure that when files are created they’re being saved with as much metadata as possible. The more metadata you add, the more structure these files will have in the future.

Consider how you are structuring your file system as well. Good folder organization can likewise facilitate search and analysis. If you’re using lots of acronyms and abbreviations, keep a list somewhere of what these mean and be sure everyone in your organization uses them consistently, otherwise, there’s bound to be chaos. Teach your employees the right way to handle files instead of letting everyone run wild.

Finally, consider using AI tools to derive better understandings from your unstructured data. For example, you can use text mining software to structure the text in your documents and proceed to analyze it. Of course, before you start feeding files into your analytics, you’re going to want to make sure you’ve got all of the right data files, and that you are not analyzing “junk data.” Use Aparavi to make sure you never miss a file.

Give Your Data More Structure Today

Aparavi is an intelligent data management platform that finds files and helps you categorize them for future use. Before you start any sort of analysis, you need to be sure you have the right data. Aparavi can automatically find the files you might have missed with a manual search. Call Aparavi or visit our website to Get a Data Audit.