3 ways GenAI can build business intelligence from unstructured data

3 ways GenAI can build business intelligence from unstructured data

What hidden insights are buried in your unstructured data? Discover how Generative AI can unlock valuable business intelligence, turning data challenges into growth opportunities.

Many businesses today have an enormous amount of unstructured data, collected by business operations on a daily basis. Let's think for example of client visits reports, customer support tickets, customer satisfaction questionnaires, comments or posts in teams collaboration tools. This data often sits in internal systems, primarily serving as a burden for data retention rather than being actively used.

What if this data could contain valuable insights for your business, and extracting them wasn’t as cumbersome or costly as it once seemed?

This is probably not a mystery to anyone, but you might start thinking that finding those insights or generating that value is too cumbersome and costly for your organization. This might have been true in the past—until GenAI unlocked powerful new possibilities!

In this article, we will highlight 3 of the different ways that GenAI can be used to leverage unstructured data and build business intelligence. 

Large Language Models (LLMs) are probabilistic models designed to understand, predict and generate human-like text. Modern GenAI models like LLMs are pre-trained on vast amounts of text data and can perform a wide variety of language tasks, across a wide range of topics and domains. This means that an LLM is able to understand and interact with plain text. A famous example is the well-known ChatGPT with its different versions that have different functionalities. Each version represents advancements in the model’s size, complexity, and ability to handle a wider range of tasks. Discover how to chat with your data using ChatGPT 4.

Let's, for example, think about a customer support team which collects hundreds of client requests every day. Those requests are composed of plain text where the clients express their question, plain text for the answer and some basic metadata, like date, product, close date, country. All the incoming tickets are stored in a central database and used only for extracting statistics about responsiveness. 

Let's now see what an LLM could do to leverage the content of these tickets and how it could bring business value. 

Converting unstructured data into diagrams and statistics

When looking at plain text, an LLM can understand its context and analyze different aspects of the text, such as main concepts or overall sentiment. This approach is an attempt to convert unstructured data into structured insights that can be easily visualized and analyzed by the process owner for spotting anomalies and making more informed decisions.

As mentioned, LLMs - powered by GenAI - can understand plain text, but if you want to further enhance text analysis and enable deeper insights, techniques like document embeddings are essential. If you want to learn more about how document embeddings work, check out our guide to building document embeddings.

Looking at our example, basic insights on quantity, frequency, and topic can be easily extracted and shared. On top of that, an LLM could analyze all incoming tickets to detect trends in customer sentiment, flagging concerns, positive feedback, top words and topics that could inform decision-making.

GenAI doesn’t just provide insights from text—it can also empower creative industries. Discover how Generative AI is transforming design and creative processes in our guide Generative AI as a Canvas.

Identifying trends and business performance over time

Business performance often evolves in subtle ways, and these changes are frequently hidden in unstructured data. By tracking trends in this data, organizations can gain a clearer picture of certain business performances' evolution and can potentially also identify emerging issues by comparing past vs. current figures, helping the business to stay ahead of potential problems.

Let's assume that the customer support manager of our example is aiming at reducing the yearly ticket volume. At the beginning of the year, one of the main products was updated with key new features. All sales reps have therefore been instructed to focus their new sales strategy in explaining thoroughly the new features of the products. Despite efforts, the volume remains unchanged, and there are still as many tickets as the year before related to product usage explanation. How can the manager use an LLM, powered by GenAI, to support his analysis? The LLM can be directed to summarize and categorize tickets based on specific criteria, such as:

  • Summarize and group tickets based on specific topics (e.g., product features, user issues)

  • Identify common concerns across months or quarters

  • Highlight emerging trends, such as increased complaints after a product release, or customer feedback trends over time. 

This approach reduces the time required to review ticket content and helps teams rapidly extract actionable insights, enabling quicker and more strategic decisions. These kinds of tasks can be also predefined and always available for the user to review, as they could be linked to specific questions and monitoring goals. 

For more advanced applications, organizations can go beyond data analysis and build their own co-pilot systems. Find out how to create your own with GPT-4 in this guide: Create your own Co-Pilot with GPT-4.

Unlocking deeper insights with conversational analytics

When it comes to analyzing plain text, users may have specific, unanticipated questions that weren’t pre-defined. For those cases, having a co-pilot solution that can interact with your data using natural language can become a powerful tool and can support the generation of what we can call "conversational analytics". This capability transforms unstructured data into a flexible, on-demand resource, empowering users to ask and answer questions as they arise.

If we go back to our reference example, once the customer support manager has identified the group of tickets that have been mostly impacted by a substantial increase in number, they can deep dive into each specific group to identify the root cause of the increase. 

A simple way to perform such a root cause analysis is to use a co-pilot application that allows the user to start asking questions about a specific set of data, without the need of reviewing each item one by one. 

This approach could save the support manager a significant amount of time in the analysis and allow them to solve and escalate the issue in a timely manner. 

AI is also transforming visual content generation. Learn more about integrating AI image generators into your organization.

Conclusion

Unstructured data, such as reports, notes, and procedures, often holds valuable insights that go untapped due to the time-consuming nature of manual review. With the power of GenAI, organizations can now unlock the hidden value in unstructured data, turning a once untapped resource into a driving force for smarter, faster decision-making. The three approaches, discussed before, are just a few examples. 

GenAI enables businesses to automate and accelerate decision-making processes, ensuring that teams are not only responsive but also proactive. As more organizations embrace this technology, they will unlock hidden opportunities, improve operational efficiency, and maintain a competitive edge in an increasingly complex market landscape.

Author:

Maria Merenda

Solution Lead

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© 2024 Superlinear. All rights reserved.

Locations

Brussels HQ

Central Gate

Cantersteen 47



1000 Brussels

Ghent

Planet Group Arena
Ottergemsesteenweg-Zuid 808 b300
9000 Gent

© 2024 Superlinear. All rights reserved.

Locations

Brussels HQ

Central Gate

Cantersteen 47



1000 Brussels

Ghent

Planet Group Arena
Ottergemsesteenweg-Zuid 808 b300
9000 Gent

© 2024 Superlinear. All rights reserved.