Artificial intelligence has a bunch of stories associated with powerful chatbots, text generators, and creative tools. The majority of them revolve around unstructured data: Gen AI involved in text, images, and videos. But when dealing with structured data such as tables, databases, and spreadsheets, the same does not quite apply. This blog gives reasons why structured data requires a new generation of AI stacks, as well as how data-aware generative AI can enable special value in business contexts.
Before getting into it, first, we need to know what the distinction is between structured and unstructured data.
Unstructured data refers to openly structured data such as emails, social media, videos, and documents. That is where GenAIinstruments such as chatbots or writing assistants can do their finest work.
Then there is structured data, which is arranged in rows and columns—Excel spreadsheets, financial dashboards, or databases.
Though generative AI models have achieved enormous successes working against unstructured data, they cannot generate any meaningful explanations based on structured datasets that lack context, reasoning, and domain-specific knowledge.
The majority of large language models (LLMs) are trained on unstructured data on an internet scale. In the case of tables or records that are aligned, they have little sense to:
Learn relationships of columns
Patterns over time
Keep numerical correctness
Reach trusted conclusions from statistics
The mere request of an LLM using a CSV file or database seldom offers up business that can be acted upon. It is due to the fact that the AI generative models are not inherently data-aware. They do not understand structure in any other sense than in case it has been translated into some form they were trained to understand, most often natural language.
It brings us to the requirement of another AI stack that is designed explicitly towards structured data.
Gen AI is created with the vision of intelligent work with structured data. It does not only read columns and rows. Rather, it knows what the data is about, what questions may be asked, and what responses truly are meaningful.
These systems are a good marriage between two worlds:
The conventional machine learning (ML) of analytics, prediction, and pattern recognition
Gen AI applied to natural language generation, explanations, and easy-to-understand interaction
This is a combination of the two; thus, businesses can now automate the thinking coupled with the talking.
Companies require a pipeline to derive value out of structured data with the combination of machine learning models and generative AI. A hybrid system will work like this:
Conventional algorithms are applied to clean up data and aggregate it and model it. To give an example, sales can be predicted using forecasting models, or customers can be clustered.
The Gen AI model produces narrative reports that are easy to understand once results are available. Based on the ML findings, these can be presented in the form of summaries, such as Sales decreased by 20 percent in Q2 because of the low audience of the city stores.
With both data analytics and generative AI, users can send natural language queries such as, Why did revenue reduce in June, and receive results driven by both data analytics and generative AI logic?
This hybrid pipeline allows the AI generative production to be based on accurate and real-world data: this makes it useful, reliable, and business-ready.
Text generation is only part of the story in the applications of structured data. There is also insight generation: visual insight.
Data-aware Gen AI may create dashboards, charts, and visual explanations that reflect usages of data, the same way for business questions. To give an example, it could generate a graph of such customer churn within the past 12 months using one query.
This automation has two solutions:
It saves the analysts time on creating reports.
It enables the non-technical users to visualize data insight.
Integrating generative AI with clever visualization tools would enable anyone in the organization to explore data without involving IT and BI teams.
So where does this new AI stack change the game dramatically?
Historical patterns are something that can be predicted by the traditional models; it becomes more difficult to understand the reason why there are certain numbers. Gen AI provides a story in order to enable faster actions to be taken by the decision-makers.
Such questions as the one concerning the region that had the best performance last quarter can be answered by sales leaders, and it is to be supported by visualizations and context-aware summaries.
Patterns of bottlenecks could be detected in real time by the AI generative dashboard, and it could recommend potential optimizations.
Formatted HR data can give summaries such as, with generative AI, Attrition is most prevalent with mid-level managers, who are in the East region
There are disadvantages as well as advantages:
Data security: At times sensitive data are stored in structured data. Security standards should be achieved through an AI stack.
Contextual Accuracy: Numbers require precision as opposed to free-form text. Gen AI systems should not misrepresent even a single percentage, as it may result in bad decision-making.
Integration Complexity: Complexity of Combining ML Models, Data Platforms, and Layers of Generative AI: This process involves the dedication of knowledge-related work and careful engineering.
The organizations would have to find partners that have already demonstrated their capabilities of realizing such hybrid AI solutions.
Unless you belong to the small minority of businesses that do not use structured data heavily (in which case, no, a generic Gen AI platform will not do), that reliance will mean that a generic Gen AI platform will not pass muster. You require a stack, which comprises:
Data connectors to retrieve in databases, BI tools, or ERP systems
ML engines to derive patterns and give predictions
Gen AI modules to describe, visualize, and produce human-like responses
Management and checkrails to make it accurate and credible
That is, you require a data-aware, domain-specific AI solution that not only generates text but also provides real-time decision support.
The future of AI does not only imply writing stories, creating art, or summarizing documents. It is all about building systems that are able to tell you about your sales reports and churn trends and respond to strategic business questions in a natural language.
However, we have a long way to go to achieve that goal, and along the way what we require is not a text chat-based chatbot, but data-savvy Gen AI that also has a clue of how to deal with numbers, logic, and business rules.
It is your task to develop the correct AI stack, one in which machine learning is married to both generative AI and data visualization in order to create greater insights, automation, and wiser decision-making in every department.
