Business intelligence (BI) is essential for modern businesses, transforming raw data into actionable insights to improve decision-making. Despite significant investments in BI technologies, many organisations face challenges in data adoption and utilisation. Generative AI is set to change this landscape by enhancing and optimising BI processes across various dimensions.
Understanding Business Intelligence
Business intelligence involves practices and processes that help organisations collect, prepare, analyse, and present data to facilitate decision-making. The goal of BI is to convert raw data into actionable insights using a variety of tools and methodologies. Traditionally, this involves a range of roles, but we’ll focus on the overall process and how generative AI impacts it.
Current State of BI and Its Challenges
The BI process involves several steps:
- Data Preparation: This is the foundation of BI, where data is cleaned, collected, transformed, and prepared for analysis. This stage is crucial but often fraught with challenges due to its complex and tedious nature.
- Data Analysis: Once the data is prepared, it undergoes analysis to extract meaningful insights. This step often involves building reports and dashboards to visualise the data.
- Decision Support: The ultimate goal of BI is to support business decisions by providing stakeholders with intuitive and insightful reports and dashboards. Users interact with this data by filtering, slicing, dicing, and drilling through it.
Despite innovations and the introduction of no-code, self-serve capabilities by BI vendors, there remains a significant adoption gap. Research shows that while a vast majority of companies plan to invest heavily in data and AI, only a small fraction of users actively use data and analytics for decision-making. This stagnation is due to:
- Complex Data Preparation: Often tedious and manual, requiring specialised skills and creating bottlenecks.
- Limited Self-Serve Capabilities: Users need a deep understanding of underlying business logic, which is often challenging.
- Lack of Interest in Detailed Analytics: Many users prefer final insights over the process of creating and interpreting detailed reports and dashboards.
How Generative AI Can Bridge the Gap
Generative AI is transforming BI by streamlining and enhancing each part of the BI process, helping to improve the data adoption rate from its current plateau.
1. Enhancing Data Preparation
Generative AI can dramatically simplify data preparation by automating complex tasks. It can automate code generation for data cleaning and transformation, optimise data pipelines, and perform data profiling and semantic enrichment. This reduces the manual effort required and speeds up the entire process, making data ready for analysis more quickly and accurately.
2. Optimising Data Analysis
Generative AI revolutionises data analysis by enabling more dynamic and interactive report generation. Users can ask questions in natural language, and AI interprets these queries to fetch and analyse the right data. It automatically generates visualisations and reports, reducing the need for predefined templates and allowing for more customised, on-the-fly analysis.
3. Improving Decision Support
For decision support, generative AI enhances the accessibility and interpretability of data. It translates complex data findings into easily understandable formats, using natural language and intuitive visualisations. This makes it easier for stakeholders to derive insights without deep technical knowledge, broadening the base of data users and empowering them to make informed decisions more efficiently.
The Virtuous Cycle of BI and Generative AI
The combination of generative AI with BI results in a positive cycle that improves overall business effectiveness. Generative AI makes data preparation and analysis more user-friendly and accessible, allowing stakeholders to concentrate on more important tasks and strategic thinking. This change is predicted to raise the current adoption rate of 35% to beyond 50%, significantly enhancing how businesses utilise data for decision-making.
Conclusion
In summary, generative AI is not only improving individual roles but also reshaping the entire BI landscape. It aims to streamline BI processes and make them more user-friendly, enhancing decision-making abilities across all industries, leading to a more promising, data-driven future for businesses. Looking ahead, the role of generative AI in BI will undoubtedly become more crucial, ushering in a new era of efficiency and insight in the business world. For more tech insights visit us or contact VE3!
