Powering Data Trust with Databricks Genie Agents and Self-Serve Analytics for an Energy Provider
A UK based energy company partnered with Aimpoint Digital to transform how their teams validate and trust financial reporting data, using Databricks Genie Agents to put self-serve analytics directly in the hands of the people responsible for data quality.
.png)
The Challenge
At a UK energy company, the teams responsible for data quality, including data owners and stewards, lacked a practical way to carry out their responsibilities effectively.
Validating the data used in financial reporting required custom scripts or a slow, manual review process. With workloads already stretched, neither option was sustainable nor scalable. As a result, quality checks were not happening with the frequency, consistency or rigor that financial reporting demands.
For a business where accuracy, traceability, and auditability are critical, this created a growing and material risk. The data existed, but the people accountable for its quality lacked a simple, reliable way to access, validate, and act on it.
The organization didn't have a data problem. It had an access problem.
Our Approach
Aimpoint Digital built a governed semantic layer in Databricks around the financial reporting domains that mattered most to the business. This layer encoded the agreed definitions, business logic, and metric calculations needed to ensure that data owners were working from consistent, auditable outputs.
On top of that foundation, Aimpoint configured Databricks Genie Spaces to give data owners a practical self-service interface for validating their data. Instead of writing scripts or submitting requests to analysts, users could ask questions in plain English and receive immediate responses through charts, tables, and trends.
To make the experience reliable enough for financial reporting, Genie outputs were grounded in governed metric views. This ensured that the same question returned the same answer every time, calculated using the same approved logic. Historical benchmarking was also built in, helping users quickly identify anomalies and determine whether results looked expected or required further investigation.
Finally, Aimpoint tuned the experience to match the language of the business. Synonyms and domain-specific terminology allowed different teams to ask questions in familiar terms while still returning the correct data from the underlying systems.
Results
Data owners can now validate financial reporting data directly, without relying on technical teams by default. By making quality checks accessible through Databricks Genie Spaces and a governed semantic layer, validation became part of the business workflow rather than a separate technical process.
Issues are now surfaced earlier in the reporting cycle, when they're still easy to address. The shift from reactive firefighting to proactive validation has meaningfully reduced the risk of data quality problems reaching — and affecting — the final financial output.
Because results are grounded in governed metric views and approved business logic, stakeholders can understand how each figure was calculated. The same framework can also be extended to additional reporting domains, creating a scalable foundation for trusted self-service analytics.
Key Takeaways
Aimpoint Digital turned a manual, fragile validation process into a governed self-service model that data owners could run themselves, independently, confidently, and in real time. By utilizing metric views to create a semantic layer in Databricks and leveraging Genie Agents, the solution removed the technical barrier to data quality ownership without sacrificing the consistency that financial reporting demands. The same question always returns the same answer, and every answer can be audited. The combination of accessibility and rigor is what makes self-serve viable in a high-stakes reporting environment.
- Natural language removes the barrier to data quality ownership. When data owners can ask questions in plain English and get immediate visual answers, validation becomes something they can own themselves without depending on other teams.
- In financial reporting, consistency is critical. Metric views mean that self-serve doesn't come at the cost of reliability. The same question gets the same answer every time.
Related case studies
%20(15).png)
.png)
Let's talk AI & data. We'll architect what's next.
Whether you need advanced AI solutions, strategic data expertise, or tailored insights, our team is here to help.