“With every answer, Genie also provides an explanation of how it arrived at that answer, which gives users confidence in the result.” – Ken Wong, Sr. Director, Product Management, Databricks.

In the first part of this trilogy, which you can read here if you haven’t had the chance yet, we mentioned that Databricks Genie and Genie Code are two AI-powered solutions that enable both business users and data engineering and analytics teams to easily leverage their capabilities to analyze data in an agile, secure, and accessible way for strategic decision-making.

Now we present a second part that focuses on how business areas can find even more value in data, understand it, and interpret it without needing to be experts, thereby injecting a different kind of value into the strategic decision-making process we mentioned earlier.

We start from the premise that business areas, even when they have an idea of what they need, often have questions or doubts and don’t always have access to or clarity about the data. That’s where Genie comes in, because it allows them to ask questions in natural language, for example: What are the products with the highest margin this quarter? They can ask it to graph the relationships between investment and sales by channel, analyze weekly inventory levels, and more. They receive results and answers in seconds, with built-in visualizations and supported by the organization’s context.

Let’s then move from questions to actionable insights, where Genie plans the answers using metadata, histories, and previous queries; it generates the appropriate query, executes it, processes the result, and returns it in a clear and understandable format, which the user can accept or correct, thus helping the system “learn” to improve its accuracy in future queries.  

What changes in day-to-day operations? 

For users, there are three fundamental things:

• Speed: decisions that used to take days can now be resolved in minutes, with no tickets and no bottlenecks, because Genie’s interface is designed for non-technical users who can also generate automatic tables and visualizations.

• Autonomy: hypotheses and explorations no longer require prior technical “translation.”

• Clarity: users get explanations in both text and charts, which helps reduce misunderstandings.

In this way, the role of IT evolves, shifting from “report builder” to architect of context, defining spaces, data, and semantics, and enabling secure, scalable self-service for users, where Unity Catalog acts as the anchor for governance and permissions.

If your company is looking for ways to efficiently integrate artificial intelligence and “break records” in how business areas make decisions and respond to their daily challenges, get in touch with us, because at Vinkos we are Databricks partners and we’re ready to take you to new levels of AI.

Don’t forget to read the first part of this trilogy here and to follow us on LinkedIn as well.