Why do so many data science projects fail?
The insurance industry has historically been - and continues to be - highly data led. Insurance professionals understand the value of data and the need for continual investment. As computing capability has expanded, the ability of data science to turn traditional insurance problems from descriptive, backwards-looking views to highly accurate, predictive insights has also advanced.
Today, insurers continue to be data-rich entities, gaining deeper insights captured more quickly than previously possible.
Today, insurers continue to be data-rich entities, gaining deeper insights captured more quickly than previously possible. For example, there has been fast-growing interest in using machine learning to improve claims operations via informed call routing decisions, or the ability to spot emerging problems early on and trigger the engagement of human intervention for remediation.
In the turbulent markets that the UK personal lines industry currently faces, data science can, when combined with experienced decision makers, deliver a compelling advantage to ride the “perfect storm” more effectively.
Yet, although insurers are increasingly using data to generate value, firms have so far done this with varying degrees of success. At the executive and senior leadership level, there is concern that significant investment in data science teams - and the technology infrastructure required to deploy these methods - is not delivering the practical, pragmatic business change or value they would like or expect.
The grace and favour once afforded to executives around data science as an “R&D” activity has passed, and the expectation of clear value from the investment is now being demanded. Close observation of the market has revealed five of the biggest drivers of underperforming data science teams:
The UK insurance market is seeing an explosion in the use of data science, with both winners and losers. Bad data science is often clever people doing clever things with data, but which all too often fails to filter through the organisation to drive real change and generates no commercial value. This results in poor return on investment, but more importantly a weight around the ankles of data science teams that results in reduced productivity and attrition.
Insurers who are pulling ahead of the pack are the ones thinking about how they can create the structure and culture to empower data science teams to deliver value. They also have a strategy and clear vision around team structure, what to model, deployment and maintenance, as well as having the technical expertise to ensure the implementation is robust and real business value is unlocked from data science, targeted at solving meaningful problems. Those who are successful in navigating these challenges are seeing significant tangible returns.
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