Analytics and business intelligence platforms continue to evolve in a number of ways, including expanding the number of people who can use them, getting insights faster, and improving the accuracy and value of the results. In recent years, modern analytics and business intelligence (ABI) platforms have been referred to as “augmented analytics,” as vendors have added AI to their products so that humans can augment their own analytics with AI.
Before the advent of generative AI (GenAI), ABI platforms already used natural language processing and machine learning to understand queries and explain analytical results. GenAI features have been added to the platformsalthough they tend to vary in their level of maturity when it comes to AI and GenAI.
Ultimately, the goal is the same: to provide accurate and up-to-date information to more users.
The change made possible by GenAI
GenAI enables organizations to do more with their ABI platforms, such as generating data stories, metadata, and code, creating summaries and storyboards, and providing automated AI-driven insights, with or without a dashboard. GenAI can also be used to query data. These advancements are made possible through proprietary and third-party LLMs, such as those from OpenAI, Microsoft, and Google. Platform vendors also continue to acquire point solutions that enable more comprehensive ABI offerings. which now include GenAI companies.
Ultimately, organizations can do more with data, including continuing a conversation that preserves context.
“Augmented analytics is one of the key ways we use AI. While we refrain from using generative AI for content creation or communication, AI can be a fantastic tool when it comes to data analysis,” says Edward Tian, CEO of GPTZerothat detects artificial intelligence in written content. “What really stands out is the generation of really valuable insights. It can find patterns and use algorithms to identify information that we might never have noticed on our own, helping us make the most informed decisions possible.”
Local solutions also benefit
SME lending platform Credibly was able to successfully enhance its internal analytics with GenAI. The company experimented with vector databases and augmented retrieval generation to develop more robust business profiles for its applications. These types of enhanced analytics, combining GenAI with supervised models, led to faster approval times, better accuracy, and deeper customer insights, according to Ryan Rosett, co-CEO and founder.
“GenAI takes Credibly’s internal business intelligence efforts to another level,” Rosett said in an email interview.
The most important innovation for his company was finding real-world use cases that combined GenAI and supervised models to produce more accurate results.
“We didn’t jump on the AI bandwagon and start throwing all kinds of use cases at it to see what works. We’re constantly experimenting, comparing results, and figuring out how GenAI can work with existing models and how they can improve each other,” Rosett says.
As a lending platform, Credibly must be able to quickly and accurately assess the risk of business owners seeking financing. To achieve this, the company developed a methodology to risk-adjust external data, created a proprietary search engine using GenAI that quickly ingests and summarizes metadata from external and internal sources, and combined this with automated machine learning models to provide more accurate, risk-adjusted determinations for underwriting purposes.
“A key benefit has been increased speed and accuracy, while eliminating the costs associated with manual review, which has allowed our underwriters to be more productive. Productivity and revenue per employee have increased when we layered multiple use cases,” Rosett says. “In one example, we were able to reduce the complexity of using a few thousand selections to less than a hundred, and improve search time from a few minutes per transaction to less than 30 seconds (by integrating) automation. In another use case, we were able to reduce our offshore footprint by offloading the task to on-site employees by removing a manual step from the process and using the GenAI model to provide recommendations for review.”
Of course, the journey hasn’t been all rosy. Mitigating hallucinations, non-determinism, and user adoption of GenAI can be challenges.
“It’s important to keep users engaged when deploying GenAI models[because]there’s a ton of training involved,” Rosett says. “Using existing databases and supervised models to help classify responses also helps reduce the issues associated with[hallucination mitigation and nondeterminism].”
Conclusion
GenAI integrates with all types of applications, including analytics and BI. Its addition enables better natural language capabilities that benefit data scientists, analysts, and citizen data scientists.
GenAI’s capabilities and usage vary across vendors and within enterprises, but things are changing rapidly. GenAI may be a temporary competitive differentiator, but it will soon become a staple in the ABI space like predictive analytics, data visualizations, and dashboards.
