Taking a different approach to using existing technology has proved a bonanza for Westpac Life.
The life insurer last year introduced a data analysis strategy, defining a framework to address silos, and integrating data to move toward a common trusted source, says Torrance Mayberry, systems manager management information.
Westpac Life had gone to tender, but eventually decided it already had an asset that was not being fully capitalised. This was the bank’s Informatica data integration software.
“We took a strategic look at our data-quality inconsistencies. We brought the business users together to shape a view of how analytics looked from a business perspective. We gained quite an understanding of change,” Mayberry says.
“The announcement of version 9.1 of the software gave us the ability to adapt even faster.”
Insurance data at Westpac Life had been collected and stored in many disconnected applications, making it difficult to quickly focus on fundamental trends and closely align them to strategic capabilities or proposed innovations. The business’s ability to quickly gain visibility into how best to reposition its products and services was being stifled.
Resources were being utilised to gather, reconcile, consolidate and cleanse business data in spreadsheets before informed decisions could be made. There are more than 1500 users of the system, including branch managers at banks and call centres. There is an element of wealth management in the business, with retail customers being advised on products that are a suitable fit for their lifestyle.
Westpac Life had also been using MicroStrategy’s business intelligence platform for some years.
Mayberry says both platforms are open.
“We looked at indexing known quality problems, using Informatica as a metadata platform that showed the business relationship between platforms.
“We have created a collaborative environment. The users now have the ability to self-serve, to interrogate and discover information on their own.”
Westpac Life has projected a return on investment from the insurance project of 242 percent (bearing in mind it already had the platform in-house). It foresees increased revenue of at least one percent, a drop in customer churn of one percent, and improved cross-sell rates. Another benefit is the automation of complex tax liability calculations and reconciliation processes, enabling the timely identification of policies that are outside risk tolerance.
Mayberry says lessons learned came from tackling the problem from a data-centric perspective, looking at how to leverage the extracted data at the right time and to put it in the right context.