Case Study
Accelerating Financial Close by Up to 50% with AI-Powered Flux Analysis
Download Case StudyIndustry: Hi-Tech / Semiconductor
Key Technologies/Platforms: Databricks, Databricks Lakeflow Jobs and Pipelines, Databricks AI/BI, Genie, Databricks AI Functions, Power BI, Oracle, Generative AI
About the Client
The client is a global semiconductor company developing high-performance data infrastructure solutions for cloud computing, artificial intelligence, networking, and enterprise applications.
As financial data grew in scale and complexity, the finance team needed a faster way to investigate fluctuations, understand transaction-level drivers, and prepare variance narratives during close cycles.
Business Requirements
- Bring more automation into financial flux analysis and the preparation of monthly and quarterly variance narratives.
- Cut down the manual work involved during financial close and help teams complete the process sooner.
- Give finance users a direct path from financial statement variances to the supporting subledger transactions.
- Enable self-service, ad-hoc analysis for finance users.
- Improve the speed and consistency of variance investigation.
- Build a scalable data and analytics foundation on Databricks.
Business Challenges
- Manual Flux Analysis: A large part of flux analysis was still being done manually, including the narratives prepared during close. This took time away from the finance team and made the process difficult to manage consistently across reporting periods.
- Limited Subledger Visibility: The finance team could identify variances in the reports, but tracing them back to individual transactions was difficult. Limited access to subledger detail meant more time was spent finding the source of those variances.
- Gaps in Ad-Hoc Analysis: The existing reports worked for routine analysis, but not for every question that came up during close. Finance users had to turn to technical teams for queries outside those reports, adding another step to financial variance analysis.
Impact and Business Benefits
- Financial close time came down by 50% for monthly and quarterly cycles.
- Finance teams now get to subledger details up to 30% sooner when reviewing financial movements.
- Less time is spent putting together variance narratives and the documentation that goes with them.
- Enabled finance users to perform ad-hoc analysis and drill down to transaction-level details.
- Improved financial variance analysis by connecting reported movements with their underlying transactions.
- Shifted finance team capacity from repetitive reporting toward higher-value analysis.
Our Approach and Solutions
- Databricks Data Foundation: Jade Global established Databricks as the central platform for finance data and analytics. A near real-time ingestion framework using the Databricks JDBC Connector brings Oracle data into the Data Lake for downstream reporting and analysis.
- Automated Data Pipelines: Jade Global used Databricks Lakeflow Jobs and Pipelines to move finance data through the required transformations and dimensional models. The automated flow replaced several preparation steps that previously had to be handled manually and supported financial close process automation.
- Financial Reporting and Drill-Down: Balance Sheet and Income Statement flux reports were built in Power BI. From a reported variance, finance users can drill down to the related subledger transactions instead of working only with summarized figures. Databricks AI/BI was also made available for users who needed to explore the data beyond the standard reports.
- AI-Powered Flux Analysis: Databricks Genie enables finance users to explore financial data through natural-language questions. Databricks AI functions generate analytical narratives around financial movements, bringing AI-Powered Financial Flux Analysis directly into the reporting process.