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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.
Data is a valuable resource that businesses are harnessing to drive critical decisions and product experiences. With the advent of GenAI, its significance has increased even further. LLMs now leverage data to revitalize shelved ideas, introduce groundbreaking innovations, and enhance business processes.
However, the majority of the data is unstructured. In this guide, we will discuss everything there is to know about unstructured data, including formats, benefits, challenges, and best practices.
Unstructured data is irregular and unorganized, as opposed to structured data. Structured data follows a pre-defined data model, similar to a spreadsheet, where each column has labels, such as Unique ID, Username, Password, etc.
Unstructured data exists in its native or raw form and may reside in data lakes or file systems. Examples of unstructured data may include emails, presentations, spreadsheets, surveillance footage, survey reports, videos, images, text files, and machine-generated formats.
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 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.
As mentioned earlier, unstructured data exists in its raw or native form. Some part of the unstructured data is human-generated, while the other half exists in a machine-generated format.
Let’s take a look at some of the common examples of unstructured data:
These formats are the result of 3D design software like CAD or Microsoft Visio. Some notable examples include model, stl, iges, art, 3dxml, and psmodel.
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.
These file formats represent file types that contain public keys, such as crt, pem, pkipath, etc.
These file formats are generated upon rendering, creating, or downloading videos. Common file formats include mpeg, mpg, h263, h264, 3gp, wmv, etc.
These formats are generated by spreadsheet applications like Microsoft Excel, Apple Numbers, or Quattro Pro. Common spreadsheet formats include xls, xlsx, numbers, cal, and ots.
These formats are generated by presentation software like Apple KeyNote or Microsoft PowerPoint. Examples include ppt, keynote, gslides, or ppz.
These files represent the operating system library and other executable files, such as gsf, hex, exe, or bpk.
These file formats are the result of compilers and other software development applications. Examples of source codes include a2w, amw, androidproj, awd, axb, bufferedimage, or buildpath.
These formats include HTML and other markdowns. Examples include HTML, XHTML, and markdown.
These formats are generated by publishing tools like Adobe PDF and Adobe InDesign. Examples include PDF, pub, xfdf, and ave.
These formats result from imaging applications. Top examples include jpeg, png, bmp, tiff, etc.
Common audio formats include mp3, mp4a, wma, ram, aac, etc.
This file format is created when tabular files are imported or exported by spreadsheet applications. Examples include csv or tsv.
These files are associated with different databases, such as OpenOffice Base or Microsoft Access. Examples include 4db, adt, box, kexic, contact, pdb, and more.
These files are created by word processors, such as Apple Pages or Microsoft Word. Examples include doc, docx, otm, wps, etc.
These are machine-generated files, such as MRI or Ultrasound equipment. Examples include dicom and hl7.
Examples include text or txt.
These are structured file formats (Big Data) used by data processing systems to export data. Common examples include avro, parquet, xml, dtd, or xsd.
As the name suggests, these file types are used to indicate compressed or archived data. Popular examples include 7z, zip, rar, rar5, etc.
Here’s how unstructured data differs from structured and semi-structured data:
In an organization context, structured data’s biggest advantage is the fact that it’s the easiest to search and organize. All elements are neatly contained in rows and columns in pre-fixed fields.
An Excel spreadsheet is a classic example of structured data. It can be categorized and organized in any way the designer chooses or wants such as records of sales by region, by number of customers, by profit, or any other metric.
Since data is neatly categorized, it is just as easy to group various elements of data together and gain insights related to their relation with one another.
In simplest terms, data that cannot be contained in the aforementioned row-column is unstructured data. Think of photos, audio and video files, PPT presentations, open-ended survey responses, satellite imagery, and text files. These are all examples of unstructured data since they are wildly difficult to search, analyze, and catalog.
Until recently, most organizations would discard unstructured data. However, the leaps made in artificial intelligence and machine learning have made it easier to process large swaths of unstructured data and gain vital insights from it.
This form of data has elements of both structured and unstructured data but doesn’t conform rigidly to either category. This mix of elements allows for some organization and categorization but there remains a great degree of fluidity within the data.
Emails are a perfect example of semi-structured data. While the content within is usually unstructured, there are elements such as the email address of the sender and recipient, time sent, device used to send the email, and etc that are all structured forms of data.
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. Recently, technological advancements in areas like Artificial Intelligence, Machine Learning, and Natural Language Processing have helped organizations 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.
Unstructured data is leveraged for various purposes in GenAI applications, LLMs, and multimodal systems. For instance, it can be used to train AI models, enabling them to learn patterns and representations.
It enables the models to develop increased contextual understanding, as most unstructured data contains sentiments, tones, and implicit relationships. Unstructured data from specific domains, such as healthcare, accounting, and finance, or business intelligence, helps improve domain-specific knowledge for increased accuracy and reliability.
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 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 support they expect.
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, and survey reports, analytics teams can better assess market trends, how the current products and offerings are performing, and how the competition is navigating the trend.
By analyzing these different aspects, marketing intelligence teams can better assess their current standing, what strategies they need to overcome the competition, and how they can better serve their customers.
There are two ways most organizations prefer handling and storing all their unstructured data: a NoSQL database and a data lake.
Short for “Not Only SQL”, NoSQL has emerged as one of the preferred methods for storing unstructured data as it can not only handle relational databases but also offers support for more complex data structures.
Most unstructured data stored via NoSQL is done through the following:
As opposed to data warehouses, data lakes have almost a non-existent structure, thus making them ideal for unstructured data storage. However, to keep it efficient a rigorous data governance mechanism is in place to avoid slowing down any analytics requests.
This includes:
As unstructured data proliferates at an accelerating pace, it tends to bring on many challenges.
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 own, 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.
Unstructured data can contain personal information (PI), personally identifiable information (PII), and other sensitive information. There is always a risk of exposing this data accidentally. If GenAI models learn from any sensitive information, it remains with them forever, compromising data privacy. Enterprise GenAI apps also often use diverse and ever-changing proprietary unstructured data, raising security, privacy, and governance concerns.
Over the years, data protection and privacy regulations have improved and become significantly harsher, imposing heavy fines and strict penalties for violations. However, with the advent of GenAI, there are now more stringent laws concerning Artificial Intelligence, such as the EU AI Act or the US’s AI Executive Order. Along with these regulations, there are now complex AI regulatory and industry frameworks that businesses must comply with for the safe and responsible use of AI. After all, GenAI uses large volumes of unstructured data, which can contain sensitive information and be a privacy minefield.
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.
Every organization with unstructured data is concerned about a lack of visibility. 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 more 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.
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.
Using tools like Azure and Microsoft Information Protection (MIP), teams can 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.
The second-most important labeling is privacy-based labeling, which defines privacy metadata against unstructured data to determine the purpose of processing, retention period, special data category, etc.
Scan your environment for all the unstructured data that can be used for GenAI projects and catalog it to ensure a comprehensive data inventory.
Automate the curation and labeling of unstructured data and files to enhance the precision and utility of data for specific GenAI projects.
Ensure that the dataset is free from duplicated and outdated information to maintain the high-quality data that will be utilized for GenAI applications.
Some level of sanitization, such as redaction or masking of sensitive data, must occur to reduce the risk of privacy and compliance issues in GenAI applications.
Enable clear visibility of data that flows across GenAI applications or systems to trace its usage and optimize processes.
Catalog and assess all approved AI models, noting their best use cases and associated risks, such as bias or toxicity.
Assess and document the origins and uses of data in GenAI projects, focusing on compliance and risk evaluation.
Ensure that data entitlements in source systems are preserved when used in GenAI prompts to maintain security and access controls.
Leverage context-based LLM firewalls to protect GenAI interactions, such as prompts and responses, against cyber threats and unauthorized use.
Ensure compliance with current and emerging AI regulations, such as the EU AI Act and the NIST AI RMF, throughout the GenAI lifecycle.
Unstructured data isn’t going anywhere anytime soon. It exists, and it will eventually grow and become even more challenging to manage. With Securiti Data+AI Command Center, organizations can automate and streamline their unstructured and structured data discovery, classification, and cataloging to define their data privacy use case, implement AI governance, establish security controls, and meet compliance.
Request a demo to learn more.
Structured data is organized and formatted information that is stored in a fixed format, making it easily searchable and retrievable by computer systems. Examples include data in databases and spreadsheets.
Unstructured data is information that doesn't have a specific format or structure, such as text documents, images, audio files, and social media posts.
Structured data is organized into a predefined format, while unstructured data lacks a specific format and is more flexible. Machines easily process structured data, while unstructured data requires more complex analysis methods.
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