Unstructured Data: The Must-Have For Analytics In 2022

Let's investigate the current need that enterprise organizations have to rapidly parse through unstructured data and examine several data management trends that are highly relevant in 2022.

Unstructured Data: The Must-Have For Analytics In 2022
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Data management has always been crucial to maintaining business continuity for enterprise organizations. For a long time, though, data management referred to the storage of information and the occasional need to access that information. And, for much of that time, the importance of data management has come second to data analytics techniques like machine learning and artificial intelligence (AI).

Now, in 2022, the critical nature of data management for enterprise organizations can no longer be understated. Organizations have so much data through which they need to sift that they can't afford to view data management as an afterthought to their data analytics efforts, especially considering that as much as 90% of data worldwide is unstructured. Enterprises must rely on modern methods with which they can parse through sets of data that are largely unstructured. 

To that end, let's quickly investigate the current need that enterprise organizations have to rapidly parse through unstructured data and examine several data management trends that are highly relevant in 2022.


Commercial data management is becoming automated

Enterprise organizations have, for pretty much the entire time the field of data science has existed, relied on manual techniques to parse through their sets of unstructured data. These manual techniques are unfortunately highly tedious and require the manpower of multiple teams of data scientists just so that unstructured data can be parsed through and properly indexed.

More organizations than ever are realizing that manual techniques to process and categorize unstructured data are no longer feasible solutions. Instead, enterprise organizations require commercial, automated data management solutions that make it much more viable for data scientists to parse through petabytes of data and continuously catalog it. These commercial solutions will make use of AI to automate the storage of unstructured data and can even recommend to data scientists ways to better optimize their methods of unstructured data storage.

As commercial data management solutions become more mature and readily available to the public, enterprise organizations should seriously consider supplementing their data analysis efforts with project management software. Robust project management software that comes with critical features such as web-based centralized storage of project files and project progress trackers can expedite the rate at which businesses analyze and process their large sets of unstructured data. 

A combination of cloud-based project management software and automated data management workflows can significantly reduce the time it takes for data scientists to process and index unstructured data and affords them more time to invest in new-and-improved ways to analyze data with AI and ML-based data analysis techniques. 


Data warehousing is helping monetize unstructured data

In the past, enterprise organizations have monetized their sets of (mostly structured) data by parsing their business systems for insight into trends in their customer activity. These days, however, the practice of data monetization is relying much more heavily on sets of unstructured data. 

Consider the following scenario: a business wants to use machine learning to increase its rates of customer satisfaction when it comes to its support chats and phone calls. That business needs a method with which they can analyze disparate customer conversations, and that method relies on innovations such as machine learning that need unstructured data to continuously improve systems and solutions. 

Fortunately for businesses that want to improve their data monetization strategies, a growing number of companies are now offering products such as cloud-based data warehousing to better support systems to parse through sets of unstructured data. These companies recognize that unstructured data is becoming more useful than structured data for building relations with clients and consumers, and they are providing cloud-based data warehousing that can analyze disparate customer interactions in order to better hone in on intelligence such as trends in customer behavior and product demand.

Venture capitalists have taken note of the fact that a growing number of businesses are providing data warehousing solutions to better manage unstructured data, which may contribute toward a greater public perception of unstructured data's importance regarding data management. This trend in public perception may convince a greater number of organizations to invest in data management workflows that rely on unstructured and ensure the protection of customer data such as personally identifiable information (PII). 

This possibility doesn't seem too far-fetched considering that more PII is being transmitted between customers and businesses than ever before - the number of people investing in an insurance policy has risen by 50% since the start of the pandemic, for example - and customers will likely expect that businesses have solutions in place to safeguard their sensitive information and not lose track of it.


Data silos are helping - not hurting - data management

Data silos have garnered a bad rap among some data professionals, and for understandable reasons. Although they may seem innocuous, data silos can prevent the sharing of information across relevant parties and can contribute toward inconsistency in data across multiple departments. Some IT leaders may even feel that data silos make it more difficult to paint a holistic picture of a business's data when barriers such as data silos exist. 

With that said, it's highly unlikely that data silos will be disappearing any time soon. In light of this fact, it's important that IT leaders embrace ways to parse unstructured sets of data and secure it across silos without feeling like they need to store all of their data in a single silo. 

Once they feel more comfortable embracing data silos and the opportunities they present to search for, classify, and secure unstructured data, IT leaders should consider how data silos can improve tag management across data storage platforms. Portable data management that exists across multiple platforms makes it much easier for data professionals to transfer their sets of data across new cloud-based environments and software solutions while holding onto tags that enable the rapid segmentation of data. 



Unstructured data, cloud-based data management solutions, and new ways to monetize data are contributing toward a greater awareness of data management's importance. There is no doubt that unstructured data is becoming more essential than ever to the ways businesses search for insights within the gigantic sets of data that they generate and store across multiple data silos and storage environments. 

Considering that innovations in technology such as AI and ML are crucial components of modern data analysis, the importance of unstructured data will likely keep growing for the foreseeable future as it enables more effective ways to drive more informed business decisions.

Nahla Davies is a software developer and tech writer. Before devoting her work full time to technical writing, she managed—among other intriguing things—to serve as a lead programmer at an Inc. 5,000 experiential branding organization whose clients include Samsung, Time Warner, Netflix, and Sony.