The insurance claims’ data science team was grappling with challenges like single point dependency, considerable lag between project conception and insight generation and minimal view on project benefits. Other than these, adoption of project outcomes and their applicability to business information demand was always questioned and kept under the radar.
These challenges stemmed from key operational inefficiencies like need based documentation, unavailability of data science model deployment processes and non-democratic approach to shortlist business critical problems.
INGRITY spent considerable time detailing out the key drivers behind client challenges and proposed improvements across four areas:
The proposed approach also established a working group, tasked to implement the proposed improvements. The task force had people across business and technical teams so that the right responsibilities can be shared with people having right skill set.
It was anticipated that with the standard procedures being documented, the time to project deployment will reduce by more than 30%. With proposed activities like inter squad road shows, data science knowhow sessions, business’s acceptability of data science projects and their openness towards sharing business problems will lead to the correct projects being picked up by data science teams. Due to socialization of project benefits on monthly basis, the data science adoption within the business community was expected to improve.
