Modernizing Unstructured Data Processing With AI

AI’s integration into data analytics and processing is a natural progression for an industry marked by rapid innovation and growth. The big data analytics market is moving toward an expected valuation of $655 billion in the next five years, and unstructured data processing tools will be responsible for a major chunk of this revenue. 

With technological advancements and the incorporation of AI, these tools enable organizations to make sense of vast, previously untapped data stores. 

This blog will discuss how data processing has evolved, examine unstructured data processing, and highlight the role of Astera’s AI-driven solutions in transforming how businesses handle unstructured data. 

Unstructured Data and Its Unique Challenges 

Dedicated unstructured data processing solutions have gained traction recently, but many organizations are still struggling to fully leverage this resource due to its unique nature and features. 

Unstructured data represents around 80 to 90 percent of all new enterprise data. It comprises a variety of formats, lacks a predefined structure, and is typically complex and heterogeneous. These characteristics make unstructured data unsuitable for generic solutions and standardized data processing methods. 

Modernizing Unstructured Data Processing 

AI is being increasingly integrated into data management and processing platforms. It can also solve the most common unstructured data issues. When enterprises leverage AI-driven tools for modernizing their unstructured data processing methods, they benefit in three key ways: 

1. Richer Insights: The valuable insights obtained from analyzing unstructured data can give companies a competitive advantage. When different kinds of data sources are collated and analyzed, the results are more comprehensive and paint a more detailed picture.
   For example, analyzing a customer’s purchases, reviews, and call recordings with support staff—all in different formats—will reveal more about them than just looking at the customer’s purchase history.
    
2. More Effective Decision-Making: Better insights lead to better decisions. Working with unstructured data, organizational leadership can predict market trends more accurately, understand customer preferences, recognize operational gaps, and identify potential risk factors. Together, these factors can contribute to more well-informed strategizing and direction-setting, helping to secure an organization’s position in its industry.
    
3. Improved Personalization: The deeper an organization’s understanding of its customers, the better it can cater to their needs. With a keen awareness of customer behavior, organizations can work on boosting customer satisfaction through personalized services, products, and marketing efforts. In this way, unstructured data improves how an enterprise executes its primary role of catering to its customers. 

By yielding powerful insights, unstructured data supports a business in performing better at the macro and micro levels.

Five Applications of AI in Unstructured Data Processing

1. Natural Language Processing (NLP): 

NLP techniques can be implemented on unstructured text-based datasets to enable named entity recognition, summarization, and topic modeling. 

Other NLP applications include AI-powered language translation solutions and text-generation platforms. 

2. Computer Vision

AI models can analyze images and classify the patterns, scenes, and objects contained therein. This facilitates applications such as facial recognition, object detection, and image tagging. AI algorithms can similarly analyze video content, enabling data extraction from video streams. 

3. Machine Learning (ML) 

An ML algorithm identifies patterns, outliers, and trends in unstructured datasets. It can also predict potential outcomes by reviewing historical data and crucial factors such as market trends, customer behavior, and sales. 

4. Contextual Understanding 

Instead of analyzing unstructured data in a vacuum, AI models can perform contextual interpretation. They can incorporate additional factors such as location, user behavior, and browsing patterns to provide a more nuanced understanding. 

5. Extraction Templates

Template-based extraction allows organizations to capture unstructured data from large volumes of documents. Manual template creation can be time-consuming and complicated, forcing users to build, test, and then use their required extraction template.  

AI-powered tools simplify and accelerate the template creation process, reducing the time it takes enterprises to implement automated extraction on unstructured data. 

Advantages of AI-Powered Unstructured Data Processing 

Organizations actively incorporating AI-based unstructured data processing into their workflows can benefit in multiple ways: 

• Increased Efficiency 

AI algorithms process unstructured data more rapidly than humans. This enables an enterprise to analyze unstructured data in a fraction of the time that manual processes would take. 

• Greater Accuracy 

AI models can perform analytical tasks while maintaining a high degree of accuracy. Regardless of the complexity of the data, the risk of errors is minimal, and the results are reliable. 

• Adaptability 

Using machine learning techniques, AI models can learn and self-improve through feedback and new data to maintain reliability in dynamic environments. 

• Innovation and Development 

AI offers plenty of opportunities for enterprises to think outside the box and develop innovative solutions. With so much potential still untapped, AI can push companies to try new approaches for dealing with data-related challenges. 

Minimizing The Common Risks Associated with Overreliance on AI 

As with all new technology, AI in unstructured data processing comes with certain risks. However, an organization can mitigate these risks with the right systems in place. Here are two examples: 

1. Non-Deterministic Results 

AI models maintain great accuracy most of the time. However, due to their probabilistic nature, there can be instances where these models won’t be as accurate in their recommendations or solutions.  

To counter a potential lack of accuracy, organizations can implement AI during the design stage, when manual intervention is easier, and mistakes can be quickly rectified. In contrast, mistakes during runtime by a fully automated AI model are more difficult to catch. 

2. Lack of Explainability 

It can be tempting to overuse AI as a catch-all solution for every unstructured data issue an organization faces. By simply generating a solution, AI can take away explainability, which is essential for understanding how a problem is solved and the steps involved. 

To counter this, enterprises can craft a specific role for AI in their unstructured data processing methods. With a well-defined problem and clear expectations for the outcome, AI solutions become easier to review, document, and explain. 

How Astera’s AI-Driven Solutions Can Help 

Astera uses a combination of AI and template-based extraction processes to accelerate unstructured data processing.  

Users can extract, cleanse, prepare, and export unstructured data from multiple sources to their specified downstream destinations for further use. They can automate their workflows to run at certain times or when certain conditions are met. 

Best of all, they can do all this without having to write a single line of code. The result is a seamless, hassle-free process for unstructured data processing and management. 

At Astera, our aim is not just to democratize and simplify data operations. We also enable our clients to meet their data management requirements with strategic AI integration.

Hear from our COO Jay Mishra about the intersection of AI and data management and where he thinks things in this sector are headed. Check out his EM360 Podcast episode today! It’s also available on Spotify, Google Podcasts, and Apple Podcasts.