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Unstructured Data Management: Challenges & Opportunities Explained

By |2023-01-23T15:29:41+00:00April 23rd, 2020|

80% of data in the world today is unstructured, and this number continues to grow rapidly. To illustrate further on this statistic, structured enterprise databases can consist of up to tens of terabytes of data (including backups and duplicated records). But when we talk about unstructured datasets, such as those generated from IoT devices, the size can be in exabytes (millions of terabytes). This sheer volume and complexity are factors that make unstructured data management (UDM) a difficult task.

Unstructured data management

What is Unstructured Data?

Unstructured data can be defined as data, in any form, that does not have a pre-defined model or format. This type of data is generated from various sources, including audio files, video, images, social media posts, and text files.

Most organizations have robust strategies for managing and analyzing their structured data, but the real value lies in managing this new wave of semi structured data or unstructured content. This blog post presents the fundamentals of unstructured data management solutions for IT teams and business owners.

Opportunities Available

Being able to leverage and utilize big data volumes- unstructured data management-can open many opportunities for organizations. By analyzing unstructured data, businesses can view information across new dimensions that greatly improve decision-making. Here are two key areas where managing unstructured data can be beneficial:

  1. Business Intelligence: A good approach to business intelligence is to use data from both internal and external sources for data analysis. It’s easy to access structured data from an internal database, but using information entrapped in third-party APIs and open-source datasets available on the web is challenging. This is because this data has to be processed before being fed into a BI system. However, using unstructured data can help you evaluate information from new angles. For example, you can identify bottlenecks in your online store’s customer buyer journey by studying customer interactions using a tool such as Hotjar. You can use his information to improve your website’s overall design and make call-to-actions more effective, which will ultimately positively impact the conversion rate.
  2. Product Development: Every organization wants to learn how they can improve their product development process. Capturing and analyzing unstructured data can help with this. For example, if you knew what your customers talked about on social media, you can learn more about their interests and behavior patterns. All this information can be used by your product development team to launch new products and services that have high demand, eventually leading to increased sales.

Unstructured Data Management vs. Structured Data Management

Structured data management is simple and convenient, particularly because this type of data is highly organized and well-formatted. Relational database management systems and schema generators are just two examples of the hundreds of available tools for storing, accessing and managing structured data.

On the other hand, unstructured data management (UDM) is not as simple because of the significantly higher volume of data and lack of a consistent format. Most unstructured data is machine-generated (e.g., through an IoT device), so it lacks proper formatting and consistency. Moreover, the availability of fewer tools and techniques also makes unstructured data management a challenge. However, investing in managing unstructured data storage is recommended despite its complications because, in the long term, an unstructured data management solution can provide you with a barrage of meaningful insights.

One of the major differences between structured and unstructured data is the type of information they provide. With structured database, you are limited to just descriptive or diagnostic data. But with unstructured data, you can apply artificial intelligence and machine learning algorithms to obtain predictive and prescriptive data.

Successful organizations around the globe are now making use of unstructured data to unlock insights that are otherwise hidden using traditional data extraction techniques.

Key Requirements

Managing unstructured data can be difficult, but the process can be made simpler through the use of the right techniques and tools. Given below are two key requirements that you need to fulfill for indexing unstructured data:

  1. Store everything: The first key requirement to manage data is to start storing all data that you generate, no matter in what form it is or where it comes from. With the cost of storing data becoming cheaper, retaining data in the long-term can cost you as little as a few dollars per terabyte annually on cloud-based storage solutions.
  2. Separate data from storage: Now that you are storing all this information, the next step is to use this data to gain insights. Using on-premise tools, such as ReportMiner, can help you extract unstructured data from various sources and integrate it with your structured data so that you have all information available for your data analytics tools.

Unstructured Data Management Example

To illustrate how these requirements can help with unstructured data management, let us consider an example. Assume that XYZ Corporation collects customer behavior data from social media and website heatmaps. This is unstructured data that is stored in PDF and Excel files.

Examples of unstructured data from a log file include:


Once they generate this information from different websites, they can extract it using ReportMiner and store it in a local database, where other customer information is stored as well.  They can integrate this data with other customer data stored in their CRM solution and then feed it to a business intelligence tool to learn important details about their customer needs. Using this information, the business can then plan and strategize its marketing and sales campaign to boost revenue.

Managing Unstructured Data with ReportMiner

Unstructured data management solutions can help businesses uncover the path to effective decision-making through better insights and improved analytics. They can help you gain a broader perspective of your business, customers, and products by utilizing all the available data.


ReportMiner is a modern on-premise unstructured data extraction software designed to help extract structured and unstructured data. The software can help you simplify the otherwise complex process of UDM by offering visual UI and automation capabilities.

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