Challenge: Enhance the efficiency and relevance of reporting processes

Solutions: An AI-based solution integrated with existing semantic layers and reporting frameworks, ensuring high trustability through a proprietary generative AI framework to streamline insights extraction

Benefits:

• Faster, better decision making
• Reduced dependence on technical skills, empowering a broader range of users
• Enhanced relevance and accuracy through feedback and continual updating
• Greater operational efficiency
• Dynamic generation of reports and insights to derive business conclusions in natural language

The challenges the company faced stemmed from a reliance on traditional reporting methods, which were time-consuming, rigid, and required technical expertise. They needed a system that would aid in the interpretation of the most relevant key performance indicators (KPIs), acting as a decision-making assistant through conversational reporting interfaces. They wanted the insights from these reports to be readily applicable by the user, without having to rely on technical expertise.

SDG Group tackled this challenge by integrating advanced natural language processing (NLP) capabilities and leveraging large language models (LLMs) to transform how the business users interact with data. To achieve this, they developed a user-friendly conversational assistant and a cognitive engine for generating insights, fully integrated with existing semantic layers and reporting frameworks. The aim was to enable data queries to be made using natural language and without the need for advanced technical skills. By specifying a product, region, or time frame, each user is able to define their analysis targets. The result: empowered users and simplified data access, incorporating multiple security mechanisms and safeguards to ensure that the information provided is always accurate, while recognizing the importance of avoiding hallucinations or data leaks in enterprise-grade contexts.

The cognitive engine accesses relevant data from the Snowflake data platform and generates actionable insights in real time. The assistant scans relevant key performance indicators (KPIs), producing personalized, automated reports to give users the specific insights they need to back their decisions. It incorporates a feedback loop, enabling the user to refine results through an iterative process that continually enhances the accuracy of insights. This provides for ongoing improvement of the database over time, enhancing its relevance and accuracy. Amazon Bedrock ensures robust AI infrastructure.

• The user types in their queries or select predefined cognitive dashboards and dimensions to filter the results.
• The cognitive engine understands the query, accesses the company’s data (stored securely in Snowflake) and analyzes it to highlight trends, KPIs, and other relevant insights.
• The AI system creates customized, automated reports with the relevant insights, helping teams to make faster, data-driven decisions.
• Users give feedback or refine their queries in natural language, continuously improving the AI’s ability to deliver useful results.