Gaurav Garg - Creating a Database to Analyze Quality of Revenue in the Healthcare Industry
Name: Gaurav Garg
Title: Senior Analyst
Country: India
Organization: Confidential
Awards Categories:
- Best MLOps Use Case
- Best Data Democratization Program
- Best Approach for Building Trust in AI
Business Challenge:
I'm a part of a diligence practice with one of the top consulting firms. I work mostly in the Healthcare industry, and there is a specific analysis relating to the Quality of Revenue. The source files were data-intensive, and there were some source file adjustments too. I was able to leverage Dataiku to make a database for the Revenue to analyze the same further.
Business Solution:
My organization primarily uses Alteryx for complex data-intensive tasks and projects. So far, QoR analysis has been driven through Alteryx only. There have been talks to replace the use of Alteryx and shift towards Dataiku.
Coming back to the QoR, the onshore team is quite hesitant to share this analysis with us. I was able to leverage my relationship with the onshore team and gain their trust so that I could process the analysis in Dataiku.
There is already a standard process to run this on Alteryx, but I went ahead and tried it in Dataiku. We were able to do it, and we are now leveraging this as a base to replace Alteryx throughout our organization.
Day-to-day Change:
The onshore team is now quite comfortable when it comes to sharing this analysis with us. This analysis was never received by our team, but because of this, we may be able to pitch more projects in Dataiku and lead it on our end.
Business Area: Accounting/Finance
Use Case Stage: Built & Functional
Value Generated:
Quality of Revenue is an analysis that is specific to the Healthcare industry where we identify clean revenue for the target entity (depends on buy/sell side diligence). This is basically an accrual adjustment where we analyze all the Charges (Anticipated Income) - shown by the Service provided with the Payments (Actual Income) received from the Insurance company.
Since the data was huge, it was difficult to process it in Excel. As such, we use Alteryx for this type of analysis. I was able to showcase an alternative using Dataiku to challenge the legacy of Alteryx within my organization.
My work impacts the valuation of the entity directly or indirectly in whatsoever manner.
Value Brought by Dataiku:
Since this was my first time using Dataiku, I admittedly did not save much time. That said, I got a grasp of the technicalities of things and how we need to structure the data. I'm confident enough to make this process efficient. I'm yet to deep dive more into the analysis part. However, I have performed databasing as of now. Further, I'm planning to standardize it in such a manner that we could replicate this across projects.
Value Type:
- Save time
- Other - Valuation impact
Value Range: Hundreds of thousands of $