Unstructured data is big. Really big. According to an IDC study, 80 percent of an organization's data will get classified as "unstructured" by 2025. This should come as no surprise considering the number of photos, videos, documents, and emails generated on the web by the minute. However, experts say that only around half of the data companies collect actually gets used and analyzed.

With such huge potential for unstructured data, and technologies like Hadoop to handle it, old school database administrators need to make the transition from the comfortable world of structured data to the chaotic realm of the unstructured.

So, what are the best ways organizations can deal with their unstructured data? We're here to break things down.

Table of Contents

Unstructured Data Processing Methods

Unlike structured data, which comes neatly organized in relational databases, unstructured data does not have a predefined schema and isn’t available in a specified format. How can your organization process this? Let’s discuss it by classifying two different groups of unstructured data.

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Logs

The first group consists of application logs, stored as files that list events such as page visits, button clicks, logins, exceptions, and so forth. You can structure part of these log lines and have it contain the date, log type (info/warning/error), and URL, while the other part may be fully unstructured with any info the app’s developers choose to include. Log data could also include newline characters, which may further complicate processing - determining where one log starts and another one ends.

So, your boss needs analytics on web app logs pronto - which transactions happened, how long they took, and what errors occurred. What tools can you use to provide these numbers? Unfortunately, the solution is to write custom code that checks for sequences (e.g. add an item to cart, checkout, thank you) and extract values using regular expressions. You could use Hive or Pig to deal with the data, but you’d still need to find or write UDFs (user-defined functions). Logs are a critical part of the data processing pipeline, providing keen insights into some of your vital data. Make sure you have the technological capacity - and proper tools - for processing your organization's log data.

Fully Unstructured Data

Data such as social network statuses, emails, documents, images, and videos are fully unstructured data. In actuality, this label may be misleading, because emails and binary file formats have well-defined headers with metadata. However, their content is fully unstructured and may appear in the form of free text or binary bits and bytes, either raw or compressed.

Processing unstructured data means extracting structure from it. Let's look at the example of sentiment analysis, which is also known as opinion mining. It determines judgement, evaluation, or even emotional state by processing unstructured text and analyzing how the words fit together. They are then assigned a polarity that identifies the text to be positive, negative, or neutral.

Biometrics is another field that uses unstructured data, more specifically images. Biometrics works by processing fingerprints and facial images to extract structured attributes. For example, ink smears turn into lines and polygons. From there, a biometric comparison uses the structured attributes rather than the raw data.

Your organization's "fully unstructured data" is another important element of your overall data gains. The right type of processing can "bring order to chaos" - bringing out critical and concrete insights from data that is anything but.

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Structured Unstructured

Actually, unstructured data already exists in structured data. Take BLOBs (binary large objects) - collections of binary data saved in a database entity. BLOBs can store text, documents, videos, images, and other kinds of unstructured binary data.

As for textual data, some features are already available in relational databases: LIKE operators with regular expressions, fulltext search, entity extraction, text classification, and text similarities. Processing XML is also possible - via XQuery support on Oracle and SQL Server, with extra support for JSON on PostgreSQL. SQL Server also has the ability to map file systems to database tables.

"Structured Unstructured" might seem like a bit of a confusing term, but it's really quite important to your data processing. Elements like BLOBs should not get overlooked when it comes to planning out your data processing solutions.

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Utilizing Unstructured Data

There are two major groups of unstructured data for processing: The first is application logs that need handling via custom code and regular expressions; The second is fully unstructured data from which advanced algorithms extract structured attributes.

Considering the huge amount of unstructured data and that only a tiny bit of it is being analyzed, adventuring in this dense information jungle with the right tools and methods could lead to some amazing discoveries: Ascension Seton Healthcare Family, a non-profit health care facility in Texas, uses unstructured clinical information to save costs by predicting potential return patients and intervening with them. Also, the investment firm BNY Mellon integrates customer interactions with its traditional systems to get a better view of customer needs.

How can your organization join the ranks of these other standouts? That's a question that needs to be on the mind of anyone dealing with organizational-level "big data" - and these days, that's just about everyone.

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How Xplenty Can Help Your Organization With Unstructured Data

The odds are that your organization is sitting on a pile of unstructured, important data - information it can use to for critical insight and analysis. So, how can you take advantage of that data?

Turn to Xplenty. Xplenty's high-powered, cloud-based solution provides you the simple, visualized data guidelines for automated date flows - allowing you to transform, normalize, and clean data all while sticking to compliance best practices. With Xplenty, you can integrate, process, and prepare data for analytics on the cloud, giving your organization the important edge it needs.

Ready to see how Xplenty can help your organization with unstructured data? Contact us to schedule a demo!