According to Gartner, 80 to 90% of the world’s data today is unstructured and growing at an annual rate of 61%. To illustrate further, 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 make unstructured data management increasingly important for organizations of all sizes. In the last couple of decades, the type of data that businesses store and how they handle it has changed considerably. The simultaneous growth of cloud storage and big data has also contributed to the rise of unstructured data.

But before talking about unstructured data management and its importance, let’s get a clear idea of what unstructured data is for enterprises and how it differs from structured data.

We’ll also look at some unstructured data challenges, how to overcome them, and what you can do to leverage unstructured data for analytics and business intelligence (BI) functions.

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, videos, 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.

Read more: Understanding Structured, Semi-Structured, and Unstructured Data

Importance of Unstructured Data Management

Data is the most important non-human asset that organizations have, and yet very few are able to extract full value from the huge volumes of unstructured data at their disposal.

However, leveraging and utilizing big data volumes can open many opportunities for enterprises. Organizations can view information across new dimensions by analyzing unstructured data, improving decision-making.

Here are two key areas where managing unstructured data can be beneficial:

Business Intelligence

A good approach to business intelligence is using internal and external data 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 users have to process this data before feeding it 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 like Hotjar. You can use this information to improve your website’s overall design and make call-to-actions more effective, ultimately positively impacting the conversion rate.

Product Development

Every organization wants to improve its product development process, and capturing and analyzing unstructured data can help with this. Data from sources like social media is largely unstructured but it contains valuable insights that can help companies develop products to cater to unmet needs.

For example, if you know what your customers talk about on social media, you can learn more about their interests and behavior patterns. Then, your product development team can use all this information to launch new products and services backed by data-driven demand forecasting, eventually leading to increased sales.

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Unstructured 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), lacking 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. 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. You are limited to just descriptive or diagnostic data with a structured database. But with unstructured data, you can apply artificial intelligence and machine learning algorithms to obtain predictive and prescriptive data. Let’s look at a more detailed comparison between the two types:

Key Requirements for Managing Unstructured Data

Effectively managing unstructured data requires using the right techniques and tools that can simplify the process. 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 the data you generate. 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. Data management tools, such as Astera, can help you extract unstructured data from various sources and integrate it with your structured data to have all information available for your data analytics tools

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Challenges of Unstructured Data Management

Managing unstructured data comes with a unique set of challenges due to its inherent complexity and variety. These are some key challenges enterprises face when dealing with unstructured data:

1.   Lack of Standardization

Unlike structured data, unstructured data lacks a predefined schema, making it difficult to classify, index, and store effectively. This variability may create significant challenges in building a consistent data management framework.

Solution: Implementing AI-driven classification and indexing solutions that use natural language processing (NLP) and machine learning (ML) can help identify patterns and categorize unstructured data. These technologies dynamically generate metadata and establish a flexible framework for effective storage and retrieval without requiring predefined schemas.

2.   Volume and Scalability

The ever-growing volumes of unstructured data—often measured in petabytes or exabytes—make storage, retrieval, and analysis challenging. Traditional systems are often unable to scale to handle this deluge effectively.

Solution: Cloud-based storage and processing platforms with elastic scalability can handle large and dynamic datasets. Pairing these solutions with distributed file systems and parallel processing frameworks can optimize storage efficiency and enable high-speed data analysis at scale.

3.   Data Integration

Integrating unstructured data with structured data systems is complex, as relational database management systems are not designed to handle unstructured data.

Solution: Leveraging hybrid integration tools that use APIs, data lakes, and middleware can bridge structured and unstructured data systems. These tools allow seamless data flow between diverse systems and enable real-time integration while maintaining data integrity and coherence.

4.   Data Quality and Consistency

Unstructured data often comes from multiple sources, such as IoT devices, social media, or emails, which may lead to inconsistencies and inaccuracies. Poor data quality can hinder analysis and decision-making.

Solution: Deploying data quality solutions that leverage AI can automate the detection and correction of inconsistencies across data sources. Using these tools, users can implement validation frameworks that standardize formats and verify data accuracy during ingestion to maintain reliability in analytics.

5.   Limited Tool Availability

While structured data can be managed using well-established relational databases, unstructured data lacks similar tools. Specialized solutions are required, which may involve significant investments in AI and machine learning technologies.

Solution: Companies can invest in specialized AI and machine learning solutions tailored for unstructured data management, such as deep learning-based data extraction tools or semantic search systems. These tools are increasingly accessible and can deliver high ROI.

6.   Security and Compliance

Ensuring the security and privacy of unstructured data is challenging, as it often contains sensitive information dispersed across multiple formats and locations. Due to this dispersed nature, compliance with regulations like GDPR and HIPAA becomes complex.

Solution: Data governance platforms equipped with encryption, access control, and automated auditing can help secure unstructured data. Integrating tools that map sensitive information and provide detailed reporting for regulations like GDPR or HIPAA ensures compliance.

7.   High Processing Costs

Processing unstructured data requires robust computing resources and advanced algorithms, which can increase infrastructure and operational expenses.

Solution: Processing costs can be lowered by using cost-efficient cloud solutions offering pay-as-you-go models and AI-driven optimization to reduce resource consumption.

8.   Search and Retrieval

Without predefined indexing or tagging, locating specific information within unstructured datasets can be a time-consuming and resource-intensive process.

Solution: Implementing intelligent search technologies, such as semantic search engines and AI-powered tagging systems, improves retrieval efficiency. These solutions enable context-aware searches, ensuring faster and more accurate results even within vast unstructured datasets.

Leverage Unstructured Data for Insights with Astera’s AI-Powered Solution

While the challenges surrounding unstructured data have persisted for a while, breakthroughs in AI technologies enable data management solutions like Astera to help enterprises leverage their unstructured data. Astera Intelligence, our AI capabilities, help streamline and automate unstructured data management. Here’s how:

• Semantic Data Mapping: Using AI and machine learning (ML) algorithms, Astera Intelligence can analyze the meaning behind data, regardless of its format, and map it accordingly.
• AI-Powered Extraction: By leveraging AI to extract data from unstructured documents, you can automate the extraction process to save hours of manual work and thousands of dollars.
• File type Support: You don’t have to be limited by file types anymore. Whether your unstructured data is in PDFs or Excel files, you can easily extract it with the same level of accuracy.
• Built-in Validation Checks: Data quality is of utmost importance, which is why you can rely on our built-in validation checks and save hours that would have been spent double-checking the output.
• Smart Search Your Data: With our RAG-powered solution, you can conduct smart searches to extract contextually relevant key details within seconds.
• Superior Accuracy and Efficiency: Manage terabytes of unstructured data with unmatched accuracy and efficiency using our AI-powered platform.
• Integrate Effortlessly: Deploy workflows within hours and integrate your data within minutes with our vast library of connectors and compatibility with all the popular on-premises and cloud solutions.

Astera’s AI-powered data extraction solution is designed to extract structured and unstructured data. By offering a visual UI and automation capabilities, the software can simplify the otherwise complex process of unstructured data management.

Get a free trial today to see how it works.