Client Stories

Automated Rolling-Forecast Data Model for Cost Optimization

Written by Admin | May 25, 2022, 7:00:00 AM

ISG helped an insurance company establish a rolling-forecast data model with Power Query and Power BI to deliver executive-level visibility on workforce cost breakdown, provide scenario-based insight on future planning with solid data, and identify cost-saving opportunities.

Opportunity

One of the biggest general insurance companies in the Australia/New Zealand (ANZ) region spent $80 million dollars over the course of its half-year budget. It sought to establish a rolling-forecast model to understand budget variance resulting from workforce imbalance. It wanted to use solid data for all its future planning. Its challenges were:

  • Limited data analytics capability: The company predominantly used lagging indicators; its lack of capability dealing with large commercial data sets caused fragmented and disparate data sources; its limited data governance structure resulted in a low level of confidence in commercial insights.
  • Lack of scalability: The company heavily relied on manual processes with mostly copy and paste approach when handling data; its siloed band-aid solutions prevented consistent data usage; it had a low flexibility to accommodate scenario analysis.

Imagining IT Differently

ISG led the company in a data analytics engagement to design and build a rolling-forecast data model. The goal of the project was to allow the client to adjust the number of contractors/ employees it hires and the type of work it could strategically focus on in future, while growing the company to meet consumer demand.

Leading the company from ideation through to implementation, ISG successfully established the model with real-time data visualization to provide a scalable solution for better internal cost control and data-driven decision making.

Future Made Possible

The data model allowed the company to implement a scalable system, beginning with a small team of under 300 people and scaling to over 3,000 people.

The approach has achieved the following results:

  • Provided a flexible and scalable solution allowing effective decision making and future workforce planning;
  • Implemented structured data governance to enhance credibility and control; and
  • Used leading indicators to create real-time feedback, thus enabling the company to stem cost overruns and manage workforce numbers more effectively.
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