Answers, Not Reports: The Value of Using LLMs for Operational Insights 

The need for timely and actionable insights is more critical than ever but traditional reporting systems, while valuable, often burden businesses with thousands of redundant reports, high costs, and limited actionable insights. This is where Large Language Models (LLMs) come into play, offering a transformative solution that enables instant, natural language access to meaningful data insights without the overhead of maintaining extensive report inventories. 

Problem with traditional reporting

Strategic reporting capabilities, such as OKRs and KPIs, are vital for helping businesses understand, predict, and manage their operations effectively. However, much of the operational reporting in many organizations is redundant.

The push for “democratization of data” has enabled widespread access to data within businesses. Instead of directly utilizing this access, most business users rely heavily on reports, when what they actually need are answers to specific questions. Reports typically present measures and statistics, but they rarely offer trends, predictive insights, or actionable outcomes.

It’s not uncommon for organizations to maintain over 2,000 reports in their data estate. Many of these are redundant, run only once or annually, or duplicated across the system, leading to inconsistencies. The cost of maintaining, hosting, and supporting these reports is disproportionately high compared to the value they provide. Managing these reports has become a function in itself, straining resources without delivering sufficient impact.

The gap between what decision makers need (meaningful answers) and what they get (reports) usually stems from limited data technical skills. 

Transformative power of LLMs

Large language models (LLMs) have the potential to revolutionize how businesses interact with their data. By providing non-technical decision makers with a natural language interface, LLMs enable them to find instant, actionable answers without the need for building, maintaining, and supporting thousands of reports. 

Real-world example

We recently helped a leading train operating company do just this. In collaboration with Microsoft, Ensono developed an innovative reporting engine using Azure OpenAI Service, AI Assistants, and Prompt Engineering.

Train performance data had been locked up in a complex legacy reporting system that required significant technical and business knowledge to interpret. In just four weeks, Ensono and Microsoft built AI agents that provide instant access to powerful performance data via natural language prompts.

The impact of using LLMs for operational answers is profound. Businesses can now simplify reporting, lower costs, and unlock new opportunities for data-driven decision-making. At the train operating company for example, the leadership team now has instant access to performance data and can ask probing questions, which they previously required a support team for. They are also now able to prompt engineer and introduce new supported areas, easily ingest and add more data sources, and start looking at trending and prediction. 

Traditional reporting systems have their place, but the future lies in leveraging the power of LLMs to provide instant, actionable insights. By doing so, businesses can reduce the burden of maintaining extensive report inventories, lower costs, and make more informed, data-driven decisions. The era of answers, not reports, is here, and it’s time to embrace it. 

To learn more about Ensono’s data services, visit Data Engineering & Management | Ensono