Unstructured data is a type of data that does not have a predefined data model, making it challenging to analyze and manage. It includes information that is not organized or formatted in a specific way, such as emails, social media posts, images, videos, audio files, and more. Unstructured data is generated by users and devices every second, and it accounts for the majority of data that exists in the world today.
Unlike structured data, which can be easily managed and analyzed using traditional database management systems, unstructured data is much more difficult to handle. It does not follow a specific format or schema, making it hard to process or manipulate using traditional data analysis tools. As a result, businesses have to resort to new methods to handle the ever-increasing amounts of unstructured data generated every day.
The sheer volume of unstructured data generated every day makes it difficult to process and manage. Additionally, unstructured data can be challenging to secure, as it often contains sensitive information that must be protected from cyber threats and breaches.
The growth of unstructured data has been exponential, and it is predicted to continue in the coming years. A study by IDC shows that the amount of data created globally will reach 180 zettabytes by 2025, with 80% of that data being unstructured. This makes it essential for businesses to find effective ways to manage and analyze their unstructured data.
What Are Some Common Types of Unstructured Data?
Unstructured data is data that cannot be organized or categorized in a predefined manner. It is mostly qualitative data that cannot be processed and analyzed using conventional data tools and methods. Unstructured data can come from various sources such as social media, email, images, videos, documents, and more. In this article, we will discuss some interesting examples 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. Here we explore some common types of unstructured data:
Images and Videos
Images and videos can be another example of unstructured data. With the advent of smartphones and social media, people are sharing more images and videos than ever before. Images and videos contain a wealth of information, including faces, objects, locations, and more. However, analyzing and extracting insights from images and videos is challenging because they are unstructured. To make sense of image and video data, companies use computer vision and machine learning algorithms to recognize patterns, objects, and faces.
Email is a ubiquitous form of communication in the business world. However, the content of emails is unstructured, making it difficult to extract meaningful insights. Emails contain unstructured text, images, attachments, and metadata. Companies can use natural language processing (NLP) and machine learning algorithms to extract valuable insights from emails. For example, companies can analyze email data to identify customer complaints, sentiment analysis, or spam detection.
Text data is another example of unstructured data. Text data can come from various sources such as articles, books, and websites. Text data is unstructured because it is written in a free-form manner, without any predefined structure or schema. Companies can use NLP and machine learning algorithms to extract insights from text data. For example, companies can analyze customer reviews to understand customer sentiment or analyze news articles to stay ahead of market trends.
Internet of Things (IoT) Data
The Internet of Things (IoT) is a network of devices connected to the internet. IoT devices generate vast amounts of data, including sensor data, location data, and more. IoT data is unstructured because it is generated in real-time and in a free-form manner. Companies can use machine learning algorithms to extract insights from IoT data. For example, companies can analyze sensor data from manufacturing equipment to predict when maintenance is required or analyze location data from delivery vehicles to optimize routes.
Analyzing Unstructured Data
Unstructured data analytics are quite useful. 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 is Unstructured Data Stored?
Unstructured data refers to information that does not have a predefined format or organization, making it more challenging to process and analyze. Unstructured data can be stored in various ways, depending on the type of data and the intended use. Some common storage methods include:
Many types of unstructured data, such as documents, images, audio, and video files, can be stored on a computer's file system or in a cloud-based storage service like Google Drive, Amazon S3, or Microsoft OneDrive.
NoSQL (not only SQL) databases are designed to handle unstructured data more efficiently than traditional relational databases. They can store a wide range of data types, including key-value pairs, documents, column families, and graphs. Some popular NoSQL databases include MongoDB, Couchbase, Cassandra, and Amazon DynamoDB.
Object storage systems are designed for storing large amounts of unstructured data in a scalable, distributed manner. They store data as objects, which are assigned unique identifiers and can be retrieved using their identifiers. Examples of object storage systems include Amazon S3, Google Cloud Storage, and Microsoft Azure Blob Storage.
A data lake is a centralized repository for storing large amounts of raw, unstructured data in its native format. Data lakes enable organizations to ingest, store, and analyze diverse data types, including log files, social media feeds, and sensor data. They can be built on various storage systems, such as Hadoop Distributed File System (HDFS), cloud-based object storage, or other distributed file systems.
Content Management Systems
A CMS is used to manage and store unstructured content, such as blog posts, articles, images, and multimedia files. Popular CMS platforms include WordPress, Drupal, and Joomla.
Emails and their attachments, which often consist of unstructured data, are typically stored on email servers or in cloud-based email services like Gmail, Microsoft Exchange, or Yahoo Mail
So, there are various techniques and methods to store unstructured data, including data lakes, NoSQL databases, cloud storage, and a combination of storage methods. Organizations must carefully consider their data characteristics and usage to choose the best storage option. The increasing adoption of big data analytics and the growing need to analyze unstructured data will continue to drive the growth of the unstructured data storage market.
Challenges in Working With Unstructured Data
Working with unstructured data has its fair share of challenges. Here are some of the most significant obstacles that organizations face when working with unstructured data:
Unstructured data is inherently complex, with varying structures and formats. This can make it challenging to analyze, as different types of data require different analysis techniques.
Unstructured data is often inconsistent and of low quality, with errors and inaccuracies that can skew results. This can make it difficult to draw accurate insights from the data.
Unstructured data can be vast and rapidly growing, making it difficult to manage and store. This can be particularly challenging for businesses that lack the infrastructure and resources to handle large volumes of data.
Lack of Structure
Unstructured data lacks the predefined structure of structured data, which can make it difficult to organize and search. This can make it challenging to find the right data when it is needed.
Unstructured data can pose significant security risks, particularly if it contains sensitive or personally identifiable information. Businesses must take extra care to secure unstructured data and protect it from unauthorized access.
Unstructured data requires specialized tools and technologies to process, which can be expensive and time-consuming to implement. This can be a significant challenge for businesses with limited resources.
Despite these challenges, working with unstructured data can yield significant benefits for organizations. By harnessing the insights and patterns hidden in unstructured data, businesses can gain a competitive advantage and drive growth. However, to reap these benefits, businesses must address the challenges associated with working with unstructured data and develop effective strategies for managing and analyzing it.
How Can You Give Structure to Unstructured Data?
Good unstructured 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
Aparavi is a data intelligence 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.