The application of Generative Artificial Intelligence, specifically Large Language Models (LLMs), is a new frontier with promising potential in the enterprise sector. These LLMs have the capability to generate context-aware, human-like text, and thereby, deliver information of surprising quality. However, a significant challenge emerges when attempting to connect these models to intricate enterprise data. First, they don't speak the same language. Knowledge derived from data is rarely found in natural language, and instead takes the shape of statistical models, mathematical expressions and ML features. Furthermore, despite the data revolution of the last decade, most profound insights are rarely surfaced or extracted at all, remaining unrevealed in the mountain of raw data collected over the years.

The first opportunity is to discover knowledge that remains unrevealed across siloed data lakes and repositories of raw data. The SparkBeyond Discovery Platform shines at connecting disparate data sources and scanning for hidden patterns and correlations within the data, surfacing previously un-hypothesized insights on what affects positive or negative performance. The platform generates its own ideas on what to test, given only the input of the raw data and related performance challenges to focus on. This ideation process is done at massive scale, allowing for the testing of insight hypotheses a human analyst would never think to try. The result is a robust collection of insights that show a high impact correlation to desired (or non-desired) outcomes.

In a typical AutoML setting, feature engineering and insight generation processes result in potential ‘truths’ that are both unexplainable and illegible for laymen decision makers. SparkBeyond is now able to translate complex statistical patterns into plain English, helping managers and subject matter experts make sense of their business reality and track performance fluctuations to their origin.

An additional feature of SparkBeyond's platform is its ability to elucidate the "why" behind insights. It unravels the reasoning and rationale behind detected data patterns, providing a more profound understanding. This advancement in AI technology moves towards more transparent models, enhancing explainability. Comprehending the "why" imparts decision makers with increased confidence in AI-generated insights and boosts AI adoption throughout an organization. Aware of the time and attention constraints many decision-makers face, we've also introduced an automatically generated executive summary that organizes the output of the platform in highly legible natural language. This at-a-glance awareness of the key influencers of core KPIs is no less than transformational in fast paced environments that all too often keep managers dealing with surface level results rather than their less visible root causes.

SparkBeyond's platform goes a step further by delivering action recommendations based on extracted insights. These recommendations are tailored to an enterprise's specific needs and contexts. By offering clear, actionable steps, SparkBeyond's platform enables decision makers to transition smoothly from insights to actions. This not only improves operational efficiency but also creates a competitive edge by enabling quick responses to data-derived insights.