Nowadays, the creation of data has expanded so rapidly that it has become difficult for organizations to manage their data growth. Working methods are not always optimal and there is not enough time to review them. Storage landscape has become so important as users continue data creation without knowing the ultimate consequences.

 

What exactly is Unstructured Data?

Unstructured data are datasets that have not been structured in predefined workflows. Unstructured data is typically textual, but can also be images, video, and audio.

The growth of unstructured information is increasing due to the use of digital applications and services. Some estimates show that more than 80% of most organizations’ data is unstructured, and it will continue to grow at a frightening scale every year.

While structured data is imperative, unstructured data is even more valuable to organizations.  If analyzed properly, it can provide feedback and statistical insights that would never have been discovered.

 

What is the difference between structured, unstructured, and semi-structured data?

They are all data that fall under the umbrella of “big data”, which is a huge volume of data, growing exponentially over time. All three types of data offer great insights, but users should know which type of information to collect and when, and which one to analyze.

Unstructured Data

As previously described, unstructured data is usually text-heavy or configured in a way that’s difficult to analyze. It can also contain figures, statistics, and facts.

Social media posts, for example, might contain opinions, topics that are being discussed, and feature recommendations. But this information is difficult to process in bulk. First, specific bits of information must be extracted and categorized, then analyzed to gain valuable insights.

Structured Data

Structured data is mostly numeric content and usually easier to analyze. It’s organized in a pre-defined structured format, such as Excel and Google Sheets, where data is added to standardized columns and rows relating to pre-set parameters. The framework of structured data models is designed for easy data entry, search, comparison and extraction.

Semi-Structured Data

Although semi-structured data also can be text-heavy, it is roughly organized into categories or “meta tags.” This information can be easily broken into its individual groups, but the data within these groups is itself unstructured.

Below, you’ll learn how you can get more from your unstructured and semi-structured data with SaaS unstructured data analysis solutions like DataIntell.

There are several tools with highly advanced algorithms for data analysis that are designed specifically to break down unstructured data.

Before we take a look at these tools, let’s quickly go over how to properly manage unstructured data so that it’s ready for you to analyze:

1. Choose the End Goal

Make sure you define a clear set of measurable goals. Do you want to understand the peak usage of your data? Do you want to reduce the cost of your storage and save space? Do you want to plan your next purchase of storage equipment? Knowing this will help you identify what insights of unstructured data you need to collect.

2. Collect Relevant Data

Now that you know your end goal, perhaps you don’t need to work with all your unstructured data. You may need only current data, historical data or data from specific projects. You might have some data on-premises, in the cloud or in a remote location, and you want to put all this data in one data lake.

3. Clean Data

To make unstructured data easier for software to analyze, you’ll need to preprocess or clean your data first. Preprocessing data involves reducing noise, eliminating irrelevant information and slicing data into more manageable pieces of content.

DataIntell does the preprocessing for you, meaning you view only clean data. The advanced search engine lets you filter results based on specific queries like the access date of a file or if a file is duplicated. The tagging feature let you create custom list of files and folders that will let you analyze the right information to take better decisions.

4. Implement Technology

You’ll need more than just unstructured data analysis tools to get the most out of your data. Data storage and information retrieval architecture, like NoSQL databases, for example, are essential to help manage your data flow, while data visualization tools help summarize unstructured data.

DataIntell uses ElasticSearch’s powerful search capabilities to help organizations analyze their storage data. With this advanced search tool, DataIntell can accelerate the implementation of new functionalities.

5. Analyze Unstructured Data with advanced Tools

Once you’ve stored, organized, and cleaned unstructured data, it must be analyzed. Using advanced analysis tools, like DataIntell, is the most effective way to transform data into valuable insights.

DataIntell’s no-code tools meets the needs of every type of user. You can easily navigate in a user-friendly interface using your own unstructured data and connect your data to your apps through the API and other available integrations.

Don’t wait any longer; start gaining insights from your data right away.

6. Visualize your Data

Compelling data visualizations help summarize unstructured data. Let your data speak for itself through simple charts and graphs, making it easy to draw out actionable insights that you can share with your team and management.

DataIntell provides an all-in-one solution that allows you to analyze your data and create customized visualizations that help you dig deeper into your data while finding the most relevant information.

 

Why Should You Manage Unstructured Data?

Organizations are facing big challenges when it comes to data management.

• Data spread between clouds, on-premises and multi-faceted infrastructures
• Data size that doubles every 2 years
• The unstructured data that represent as much as 80% of an organization’s data

Having a solid data management strategy to collect, organize, and analyze unstructured data can help overcome the above challenges, and eventually lead to:

• Increased efficiency. Users know where to find data when they need it because it’s all in one place and easy to search. If you’re using storage analytics tools to manage your data, you can even speed up internal workflow and gain efficiency.

• Accurate & fast decisions. High-quality data is reliable and leads to better decision-making. Using tools to analyze unstructured data in real-time allows you to detect urgent issues and act quickly. Also, uncovering trends in large datasets helps you anticipate market shifts.

• Better compliance. Ensuring your data is organized and always up to date makes it easier to keep up with current regulations and standards and reduce the exposure to legal trouble.

• Improved data security. Data breaches and cyber-attacks threaten every organization. Effective data management helps to keep your data safe, create backups, and real-time monitor to identify potential risks.

In short, knowing how to manage your data effectively can help you extract more value from unstructured data and translate this value into opportunity.

 

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