Zurich Insurance recently celebrated its 150th anniversary operating in one of the most highly regulated industries where data is a formidable part of day-to-day operations. As one of the leading global insurers with numerous products across different markets, the need for data to drive decisions and inform business leaders is second-to-none, but transforming the data landscape at Zurich has been an ongoing journey over several years.
Zurich is one of the world’s leading insurers, providing a range of property, casualty and life insurance products to customers including individuals, small- to mid-sized companies, large organisations and multinational corporations. The continued success of this huge customer base relies on accurate data and rapid access to the learnings of different data sets. Historically, this has not always been straightforward due to the scope and complexity of the organisation.
Despite owning huge amounts of data spanning many decades, Zurich has not always fully embraced a data-led decision-making process. This meant a change of culture was needed to drive more value from the data within the business.
“Back in 2016 when we started the journey, everything was predominantly IT focused,” said Alex Sidgreaves, chief data officer, Zurich. “Data was often seen by Zurich as a side effect of IT change rather than a core focus, which has led to a slower strategic transformation. Some data processing was still very manual with pockets of data silos.”
As a result, Zurich was operating with insight fragmentation. The traditional centralised approach was unable to scale to meet emerging cross functional data needs. A fundamental shift in approach to data at Zurich was needed and the structure of the data team required finessing. Most importantly, a clear strategic vision focused specifically on data was required to achieve the ambitious transformation goals set out by Alex and the data team.
Assessing the issues
The first step was to cut across silos and identify key business needs for the present and future, assessing them accordingly based on the existing constraints of the Zurich data team. This would then allow the data team to see where the shortfalls in data learnings were and guide them to the best approach to transform people, process and technology for improved customer outcomes.
“To date, every data-related problem had received a point-to-point solution, which had worked for many years,” explained Alex. “But operating in a heavily regulated industry with a complex emerging regulatory horizon, something had to change; particularly as Zurich was diversifying its offerings and growing. There was an urgent need for a data team with the ability to respond to increasingly complex cross functional needs.”
At this point in time, data provision relied upon specific subject matter experts tied to individual point-to-point solutions – a model which was complex to operate but had worked well historically. The underlying challenges with the model came to a head when exacerbated by a period of rapid company growth which quickly changed the data needs and meant a new approach was vital.
“The data team came under increasing pressure with shorter deadlines to deliver,” said Alex. “As a business we had gone almost overnight from specific reporting needs from specific systems to trying to pull together huge quantities of data in one format across many disparate platforms and markets – at scale. Collating everything needed was incredibly complex and time consuming – simplistically we stress-tested our existing data delivery mechanism and it was not ready to meet these new needs. Data had gone from being very useful to a potential bottleneck.
“We started off focusing on risk-based issues, which I think is the best place to start in a heavily regulated financial sector,” continued Alex. “We then moved through into a simplification and rationalisation phase – removing complexity, improving our technology and frameworks and freeing up budget for reinvestment to fuel growth. Now, in the final stage of the transformation, the focus is very much value-based and driving new outcomes that give us competitive edge.”
Changing the fundamental data operating model and implementing new data strategies takes time, but also requires a lot of active stakeholder management. Historically, the data team had reacted quickly to immediate issues and the organisation was used to being able to lean into this capability.
“When the transformation started, there was a centralised team, but centralised teams are difficult to scale for our business model and aspirations, so this needed to change,” said Alex.
The initial centralised data team allowed Zurich’s team to work in a standardised and governed way, providing a single front door for the whole business. Eventually, however, it was noted that the data demands of the organisation had started to outstrip the capacity of the data team.
“In effect, the business reached a point where the team is very unlikely to ever be big enough to respond to the whole organisation without some fundamental changes,” said Alex. “At the time, we were a team of around 30 individuals supporting up to 5,000 people. You can imagine the amount of data requests that we would get, and not just for one business – we support seven different brands and multiple business segments, which is a huge amount of demand on one team.”
When this tipping point occurred, the data team found themselves struggling around prioritisation as there was simply not enough capacity to be able to respond to every request. It became the catalyst for change and an overhaul of the way data was used across Zurich.
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