If you haven’t heard of generative AI by now, where have you been? Much more than 2023’s hottest tech trend, generative artificial intelligence represents an extraordinarily promising avenue for transforming businesses and reshaping entire industries. One encouraging use case? Database analysis.
Harnessing machine learning models trained on significant content corpora, generative AI can enhance search and discovery across massive troves of information — from market research to business intelligence to government reports and databases containing everything in between. The ideal generative AI-powered product can rapidly sift through millions of pages to surface the most relevant excerpts, summaries and insights — in near real time.
This capability would provide tremendous value for countless companies, nonprofits, government organizations and their clients. Let’s dive into considerations for leveraging generative AI to bolster database analysis.
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Generative AI in Rapid Database Analysis and Search
Imagine poring over a mountain of reports to find that one golden nugget of information. With a collection of thousands of reports totaling hundreds of thousands of pages, simple keyword searches often struggle to pinpoint precise details.
With tens of thousands of reports — each spanning dozens of pages — and new ones piling up every month, knowing where to look for which specific statistic or sentence affirming your point sounds like a recipe for an eternal game of hide and seek. Bummer.
Now, picture generative AI as your super-powered librarian who can sift through this massive database at lightning speed. Generative AI offers a more intuitive way to explore content and find answers. For instance, users could ask, “What do the most recent reports say about trends in the shoe industry in Asia?” The system could respond with a summary of key findings, properly contextualized and cited.
But what is generative AI? And how does it work?
Generative AI is a type of artificial intelligence that uses advanced machine learning techniques to create new content like text, images, video and audio rather than simply recognize intricate patterns in data. Generative models are trained on massive datasets of examples and “learn” the underlying structure and patterns of the data through techniques like neural networks.
By building an “understanding” of the relationships between words, sentences and pixels, these intelligent systems can allow rapid searches across large datasets without breaking a sweat (or taking lunch breaks). Again, this accomplishment is not just helpful but revolutionary when time is money and precision is priceless.
Generative AI: Advantages in Report Database Analysis
Generative AI shows potential in transforming data analysis, particularly in dealing with large report databases. A few benefits include:
Efficient Information Retrieval
Generative AI’s agility and contextual understanding expedite and accurately retrieve specific information, exponentially boosting search efficiency. The technology can also surface nonobvious connections to derive novel insights humans may miss by learning latent patterns.
Natural Language Queries
Users can search databases by asking questions in plain language rather than keywords, enabling a more intuitive content exploration.
Contextual Understanding
Beyond matching keywords, generative AI excels in contextualizing information within reports, enabling deeper comprehension of complex texts and nuanced data points.
Rapid Synthesis
Generative models can quickly sift through millions of pages to identify and summarize the most relevant information. This intricate, tedious process would take humans prohibitively long.
Constant Improvement
Models continuously enhance as they train on new reports added to the database, which means the analysis capabilities can scale with the data.
Next-Generation Innovative Potential
Embracing generative AI unveils new opportunities for businesses by enabling them to access, interpret and utilize their vast databases of proprietary information in innovative ways.
While generative AI holds promise in revolutionizing data analysis, especially within extensive report databases, developing this competency presents monumental challenges.
Challenges of Engineering Products with Generative AI
Generative AI relies on neural networks with billions of parameters, trained on massive datasets over weeks or months on robust cloud computing infrastructure. Consequently, choosing between utilizing an existing model and training a bespoke one is a critical decision.
While pretrained models can generate text, images and even code, they often hallucinate or make up facts. Since training complex neural network models demands extensive — and expensive — computing resources, including specialized hardware like GPUs, architecting innovative and well-designed models is challenging. Worse yet, fine-tuning specific reports can improve accuracy but doing so requires extensive labeled data and significant engineering and computing resources.
Systematically evaluating generative models is also hard given the subjective quality of outputs. Potential risks related to biases, toxic outputs and misleading information require careful monitoring and mitigation. Sure, we can teach machines to learn, but teaching them ethics and responsibility is another beast altogether.
To learn more about training your own machine learning model, check out our blog post on overcoming machine learning model training challenges.
Complicating the issue further, by the time you train your own model, one of the leading tech giants will likely have developed a similar out-of-the-box solution. Challenges with this route initially involve waiting for the advanced database search and analysis tool you need to be built and then trusting a third party with your proprietary information. Worse yet, these solutions will likely be designed for general knowledge, not niche domains, and would likely need help interpreting the nuances of proprietary data.
This potential development might lead to a “wait-and-see” approach, minimizing immediate upfront costs but risking delayed innovation and possible redundancy.
Building with Generative AI: Our Recommendations and Expertise
Given the current limitations of generative AI and uncertainty around future offerings from the biggest names in tech, we advise waiting to see how this technology evolves. Companies like Google, Amazon, Microsoft and others are investing billions into the future of AI and racing to provide enterprise solutions, so the capabilities likely needed for a robust gen AI-driven database search and analytics platform may soon become commoditized.
In the meantime, companies across industries can realize incremental improvements by focusing on more established techniques. Adding metadata and summaries would help categorize reports based on themes, topics or relevance, enabling traditional keyword searches to operate more efficiently. Using natural language processing and information-retrieval methods tailored to search could also enhance discovery. While not as forward-looking as generative AI, these approaches can unlock value in the near term.
Ultimately, generative AI clearly represents the future of search and analytics — especially for expansive proprietary datasets. Realizing this potential still remains challenging today due to the technology’s limitations, but as a leading product engineering company, our team closely tracks developments in this rapidly changing field to help our clients prototype and validate generative AI search solutions for their databases.
With continued breakthroughs on the horizon, our measured approach will ensure we deliver maximum value while managing risks and costs. Let us know how we can provide additional perspective on data analytics, edge machine learning, TinyML and leveraging generative AI to enhance your business.