The Impact of Generative AI on Business Intelligence

Summary

This video explores the impact of generative AI on business intelligence (BI), focusing on its ability to enhance data-driven decision-making. BI involves transforming raw data into actionable insights, and traditionally requires roles like data engineers, BI analysts, and business users. The adoption of BI tools has been slow due to complexity in data prep and limited self-serve capabilities. However, generative AI is changing this by allowing users to interact with data through natural language, automating tasks, and shifting the balance of power. This could significantly increase BI adoption, moving it from 35% to 50%.

Takeaways

• 📊 Business Intelligence (BI) involves collecting, preparing, analyzing, and presenting data to facilitate decision-making.
• 💼 The BI process typically involves three core roles: data engineers (who prepare the data), BI analysts (who analyze it), and line of business users (who consume the insights).
• 🚧 Despite heavy investments in data and AI, only 35% of line of business users rely on BI for decision-making due to challenges like complex data prep, steep learning curves, and disinterest in manual analysis.
• 🛠 Data preparation is complex, tedious, and creates bottlenecks, contributing to the low adoption of BI tools.
• 📉 The limited capabilities of no-code, self-serve tools require users to understand complex business logic and KPIs, further hindering adoption.
• 📊 Many line of business users prefer to skip the analytics process and focus solely on the final recommendation or insight.
• 🤖 Generative AI offers a breakthrough by allowing users to interact with data using natural language, reducing dependency on predefined reports and dashboards.
• 💬 With Gen AI, users can ask questions in plain language, and the AI will fetch and analyze data to provide answers in easy-to-understand formats.
• 📈 Gen AI will empower 90% of data consumers to become content creators, shifting the analytic power from BI analysts to business users.
• 🔧 Gen AI will also streamline tasks for BI analysts and data engineers, automating report creation, code generation, and data cleaning, freeing them to focus on higher-value tasks.

Q & A

What is Business Intelligence (BI) in the context of this discussion?

Business Intelligence (BI) refers to the practices and processes that organizations use to collect, prepare, analyze, and present data and insights to facilitate decision-making. Its primary goal is to convert raw data into actionable insights.

Who are the three key personas involved in BI processes?

The three key personas in BI processes are the data steward (or data engineer), the BI analyst, and the line of business user. Each plays a distinct role in collecting, analyzing, and using data for decision-making.

What is the role of the data steward in BI?

The data steward, or data engineer, is responsible for cleaning, collecting, transforming, and preparing data for analytics. They ensure that the data is ready for use by BI analysts and other stakeholders.

How does a BI analyst contribute to the BI process?

A BI analyst takes the prepared data, analyzes it, and builds reports and dashboards. They work closely with line of business users to address their needs and provide insights to facilitate decision-making.

What is the role of a line of business user in BI?

Line of business users consume the reports and dashboards prepared by BI analysts. They may interact with the data through slicing, dicing, filtering, and drilling, but their primary role is to use the insights to make decisions.

What are the three main reasons for low adoption of BI tools among line of business users?

The three main reasons for low adoption are: (1) Data preparation is complex and requires specialized skills, (2) No-code self-serve tools still require understanding of business logic and metric definitions, and (3) Line of business users have to manually interpret reports and insights, which is time-consuming.

How can generative AI improve the experience for line of business users?

Generative AI allows line of business users to interact with their data in natural language, reducing reliance on predefined reports and dashboards. Users can ask questions in everyday language, and AI will provide insights, visualizations, and answers in a digestible format.

How will generative AI affect the role of BI analysts?

Generative AI will automate tasks like generating code, building reports, and creating visualizations, allowing BI analysts to focus on higher-value tasks such as documenting business knowledge in the semantic layer and performing complex analysis.

What impact will generative AI have on data stewards?

For data stewards, generative AI will automate tasks such as code generation, data pipeline optimization, and data profiling. This will free up their time to focus on more complex data engineering challenges.

How will generative AI help bridge the gap between data and insights?

Generative AI reduces the manual effort required to interpret reports by providing automated insights and recommendations. It eliminates inefficiencies, making it easier for line of business users to act on data without needing to go through complex analytical processes.