For a long time, access to information depended on dashboards, reports, tables in presentations or previously built visualizations. Today, that model is beginning to be complemented —and even transformed— with tools such as Databricks Genie Space.
Its promise lies in bringing analytics closer to a more natural, conversational and accessible experience for technical and business users. However, although creating a Genie Space can be very simple, making it truly work well requires preparation, context and good practices.
From there, 5 key aspects must be taken into account for its implementation and measurement:
Understand that analytics no longer depends only on dashboards
One of the most relevant changes in the world of data is that we no longer always need a traditional frontend to analyze it. Now, the trend of frontless analytics proposes that many business questions can be solved without going to a specific dashboard, but through a direct interaction with an agent that interprets what we ask and consults the available information.
What is sought is to navigate multiple reports to find a metric, where the user can ask specific questions and receive contextualized answers. For this reason, rather than completely replacing dashboards, this trend expands the way in which people access business knowledge: from a predesigned visual experience to a conversation with the data.
That is why, before implementing Genie, the first thing is to understand that it is not only a new functionality, but a new way of consuming analytics.
Make sure that tables and fields are well described
If there is an indispensable practice for Genie to work correctly, it is this: the data must be well documented.
Genie interacts with information stored and governed within Unity Catalog, so the quality of the context it receives will be decisive for the quality of its answers. This means that it is not enough to have tables loaded; it is also necessary for each table to have a clear description and for the fields to have comments that explain what they represent.
When a column is well described, the agent has more context to correctly interpret a question and associate it with the appropriate information. On the other hand, when names are ambiguous, technical or unclear, the risks of inaccurate answers or mistaken interpretations increase.
A valuable point here is that Databricks facilitates this process by allowing description suggestions to be generated with the help of AI. This accelerates documentation and helps enrich metadata without the effort falling completely on manual work.
In other words: if you want better answers, give Genie better explanations about your data.
Explicitly define how the tables are related
In most cases, the value of analytics is not in a single table, but in the connection between several. This includes: customers, sales, reviews, franchises, products or cities usually live in different structures, but are related through IDs or shared fields.
That is why another of the keys to optimizing a Genie Space is to explicitly configure the joins between tables.
Although the system can try to infer relationships, leaving that decision completely open can introduce errors.
If the agent does not understand precisely how to join two sources, it can build incorrect queries or generate inconsistent answers. On the other hand, when we explicitly define which field connects one table with another, we reduce ambiguity and improve the reliability of the queries that are generated behind each question.
This configuration is especially important when Genie must answer quantitative questions, aggregate values or cross data between different entities. The clearer the relational structure is, the more solid the analytical result will be.
Give it clear instructions on how it should behave
A Genie Space not only needs data, but also business context and behavioral guidelines.
Within its configuration, it is possible to add general instructions that work as a kind of base guide for the agent. There, it can be indicated, for example, the language in which it should respond, the expected tone, the meaning of internal acronyms or any relevant clarification for the context of the organization.
This step is much more important than it seems. There are terms, abbreviations or proper names that for a company are completely obvious, but that a model does not necessarily know by itself. Defining them avoids misunderstandings and helps the answers to be aligned with the language of the business.
In addition, these instructions allow the experience to be standardized. Therefore, it is not only about answering well, but about answering in a way that is coherent with the culture, the language and the needs of those who are going to use the space.
Test, monitor and control access before taking it to the business
And although the first step is to create a Genie Space, before sharing it with end users, it is essential to validate that it is responding correctly, monitor its use and manage who can access it.
For the first, Databricks offers the benchmarks functionality, which allows test questions to be built and associated with the expected query. Thus, the team can compare what Genie generates against what it should generate and obtain a measure of accuracy. This is an excellent way to validate performance before putting the space in the hands of the business.
Then comes monitoring. Reviewing what questions are asked, who asks them and how users rate the answers allows opportunities for improvement to be detected, instructions to be adjusted or gaps in the data documentation to be identified.
Finally, there is access control. Not all users need the same level of permissions. While some should only consult and execute questions, others can administer, edit or share the space. Defining this correctly helps maintain order, security and governance.
And for a friendlier adoption by the business, Databricks One comes into play, a simpler interface focused on the components that a non-technical user really needs, such as dashboards, applications and Genie Spaces. This facilitates access without exposing people to a more complex experience of engineering or data science.
This is how Genie represents much more than a new tool within Databricks. It is a clear example of how analytics is evolving toward more conversational, more agile models that are closer to the real needs of the business.
But for that promise to work, it is not enough to create the space and connect it to some tables. It is necessary to prepare the data, enrich the metadata, define relationships, give clear instructions, test the operation and monitor the use.
