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Unstructured Data 101 - Definition, Examples, Benefits & Challenges

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Over the past few years, data has exploded. To put things into perspective, it is projected that by 2025, data will grow to over 180 zettabytes globally.

So, what do these numbers tell us? Data is a valuable resource that businesses are harnessing to drive critical decisions, innovations, and product experiences.

A majority of the growth is in the form of unstructured data. In this guide, we will discuss everything there’s to know about unstructured data, formats, benefits, challenges, and the ways to deal with it.

What is Unstructured Data?

As opposed to structured data, unstructured data is irregular and unorganized. Structured data follows a pre-defined data model which is akin to a spreadsheet where each column has labels, such as Unique ID, Username, Password, etc.

Unstructured data exists in its native or raw form. It may be found residing in data lakes or file systems. Examples of unstructured data may include emails, presentations, spreadsheets, surveillance footages, survey reports, videos, images, text files, and machine-generated formats, to name a few.

Although there are a number of challenges associated with unstructured data, with “zero visibility” topping the list. However, there are also some beneficial aspects that add to its strength. For instance, since unstructured data exists in a non-predefined or native format, it is easier and faster for organizations to collect it and store it. In fact, organizations can easily dump it in data lakes so they can later extract it and refine it to derive valuable insights.

Examples of Unstructured Data

As mentioned earlier, unstructured data exists in its raw or native form. Some part of unstructured data is human-generated, while the other half exists in machine-generated format.

Let’s take a look at some of the common examples of unstructured data:

Computer-Aided Designs:

These types of formats are the result of 3D design software like CAD or Microsoft Visio. Some notable examples include model, stl, iges, art, 3dxml, and psmodel, to name a few.

Mails:

As the name suggests, these file formats are generated by email exchange services like Microsoft Exchange or Microsoft Outlook. Some examples include eml, msg, emlx, dbx, and wab.

Crypto Keys and Certificates:

These file formats represent file types that contain public keys, such as crt, pem, pkipath, etc.

Videos:

These file formats are generated upon rendering, creating, or downloading videos. Common file formats include mpeg, mpg, h263, h264, 3gp, wmv, etc.

Spreadsheets:

These formats are generated by spreadsheet applications like Microsoft Excel, Apple Numbers, or Quattro Pro. Common spreadsheets formats include xls, xlsx, numbers, cal, and ots, to name a few.

Presentations:

These formats are generated by presentation software like Apple KeyNote or Microsoft PowerPoint. Examples include ppt, keynote, gslides, or ppz.

Binary Files:

These files represent the operating system library and other executable files, such as gsf, hex, exe, or bpk.

Source Codes:

These file formats are the result of compilers and other software development applications. Source codes’ examples include a2w, amw, androidproj, awd, axb, bufferedimage, or buildpath.

Markup Texts:

These formats include HTML and other markdowns. Examples include html, xhtml, markdown, etc.

Desktop Publishing:

These formats are generated by publishing tools like Adobe PDF, Adobe InDesign, etc. The examples include pdf, pub, xfdf, ave, etc.

Images:

These formats result from imaging applications. Top examples include jpeg, png, bmp, tiff, etc.

Audios:

Common audio formats include mp3, mp4a, wma, ram, aac, etc.

Text Tables:

This file format is created when tabular files are imported or exported by spreadsheet applications. Examples include csv or tsv.

Database Files:

These files are associated with different databases, such as OpenOffice Base or Microsoft Access. Examples include 4db, adt, box, kexic, contact, pdb, and more.

Word Processing:

These files are created by word processors, such as Apple Pages or Microsoft Word. Examples include doc, docx, otm, wps, etc.

Medical:

These are machine-generated files, such as MRI or Ultrasound equipment. Examples include dicom and hl7.

Plain Text:

Examples include text or txt.

Machine-Readable Data:

These are structured file formats (Big Data) used by data processing systems to export data. Common examples include avro, parquet, xml, dtd, or xsd.

Compressed Data:

As the name suggests, these file types are used to indicate compressed or archived data. Popular examples include 7z, zip, rar, rar5, etc.

What is Unstructured Data Used For?

It is believed that around 80% to 90% of global data exists in the form of unstructured data, including rich media, social media, and surveys, to name a few. Recently, technological advancements in areas like Artificial Intelligence, Machine Learning, and Natural Language Processing have helped organizations to get a clear picture of their myriad unstructured data to drive their Business Intelligence and Analytics.

Here are some of the meaningful purposes that unstructured data can serve to help organizations succeed, grow, and scale.

Optimized Customer Experience

Unstructured data comprises customers’ emails, customer support queries, reviews, live chat histories, and more. By gaining insights into customers’ behavior and preferences, organizations can better enhance and optimize their customers’ experience.

By linking their chat history, phone calls, or customer support queries, CS teams can transform the communications into tickets, and respond to their customers accurately, and in a timely fashion.

By harnessing automation and unstructured data analytics, teams can ensure that customers are getting the right type of support that they expect.

Enhanced Marketing Intelligence

Data transparency is imperative to bring about significant improvements in marketing strategies and execution. By allowing AI or ML-driven tools to analyze Big Data or unstructured data, such as online reviews, customers’ rants on different platforms, survey reports, analytics teams can better assess trends in the market, how the current products and offerings are performing, and what the competition is navigating the trend.

By analyzing these different aspects, marketing intelligence teams can better assess where they are currently standing, what strategies they need to overcome the competition, and how they can better serve their customers.

Top Challenges with Unstructured Data

As unstructured data proliferates at an accelerating pace, it tends to bring on many challenges

Lack of Visibility

The growing volume of unstructured data and the resulting data silos further create security and privacy risks that may lead to imminent cyber threats. As organizations can’t protect any data unless they know its location, severity, and sensitivity, this leads to security risks that put not only the unregistered data at risk but also the data that is registered or indexed.

Take, for instance, the excessive privilege threats. When organizations deal with large volumes of data, they tend to lose sight of the data they owned, the personnel having access to the data, and the existing security protocols applicable or applied for data protection. As a result, organizations open their systems and resources to threats like privilege abuse, data leaks, and unintended security breaches.

Changing Privacy Laws

Initially, data protection and privacy laws revolved around governing government entities, the healthcare sector, or financial institutions. But in recent years, privacy laws have tightened their grip, especially when it comes to governing private sector organizations as they collect large volumes of data for consumer analytics and other business-critical purposes.

Over the years, data protection and privacy regulations have improved and become harsher significantly, imposing heavy fines and strict penalties for violations. There are now more regulations concerning data retention, data minimization, and governance. The longer an organization retains the data, including the sensitive data, than it should be, the more likely it is for them to receive fines.

How to Deal With Unstructured Data

Leaving unstructured data as is can be detrimental to an organization as they may face sky-high storage and manpower expenses, heavy fines from regulatory authorities, or loss of customer trust. Here are some effective ways organizations can manage unstructured data for security and privacy compliance.

Identify Data Sources

Lack of visibility is the topmost concern of every organization with unstructured data. Therefore, it is imperative to start by locating all the resources, systems, and applications across legacy, multi-cloud networks or data lakes where data could be located.

To be able to discover and catalog data assets faster and accurately, ensure that the data asset discovery tool offers seamless integration with myriad systems, networks, and applications. The tool should be able to discover data assets (including shadow data assets) across cloud-native (data lakes & multi-cloud) and on-prem environments. Tools with the added functionality of discovering advanced metadata can enable organizations to gain better insights into the sensitivity level or governance status of those assets so that effective measures can be taken accordingly, such as encrypting any data asset that may contain sensitive information.

Discover & Classify Data

Classification is an integral part of the entire data discovery and management process. Data classification enables organizations to have a better look and understanding of the priority of the data, its sensitivity, risk level, and privacy use-cases.

To ensure the effective and efficient classification of unstructured data thoroughly define the categories of data that you need to identify using rich classifiers, such as NER, Luhn, Naive Bayes, and contextual classification, to name a few.

With robotic automation powered by AI, ML, and NLP technologies, organizations can ensure the highly-accurate classification of a multitude of data, including Big Data formats like AVRO and Parquet.

Apply Relevant Labeling

Security-Based Labeling

Using tools like Azure and Microsoft Information Protection (MIP) categorize unstructured data according to its sensitivity label, such as Public, Confidential, Shared, etc. Security-based labeling enables teams to determine the level of security that should be provided to the specified category of data.

Privacy-Based Labeling

The second-most important labeling is the privacy-based labeling that defines privacy metadata against unstructured data for determining the purpose of processing, retention period, special data category, etc.

Final Thoughts

Unstructured data isn’t going anywhere anytime soon. It exists and it will eventually grow more and become even more challenging. With advanced technologies and robotic automation, organizations can automate and streamline their unstructured and structured data discovery, classification, and cataloging to define their privacy use-case, establish security controls, and meet compliance.

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