It’s time for insurers to start getting serious about advanced analytics.

Whatever segment they work in, insurers are all data-driven businesses. And the volumes of data coming their way are growing enormously. In fact, thanks to the billions of sensors and devices linking the connected world, they’ll soon have unimaginable quantities of information on the lives and assets they insure.

In this new series of blogs, I’ll be explaining how, with a new approach to advanced analytics, forward-thinking insurers will equip themselves to unlock much more value from the data they gather – and outperform their more traditional competitors in the process.

Right now, insurers lag behind companies in other industries where their use of advanced analytics – specifically machine learning – is concerned. The reason why? It’s certainly not down to under-investment. After all, insurers have been using data for pricing and risk management for years. In fact, it’s precisely their longstanding relationship with data technologies that’s preventing them from taking advantage of the latest developments.

Their prolonged focus on traditional data capabilities is getting in the way. This has damaging impacts in three core areas, each of which is key to successful uptake of advanced analytics: data, technology and people.

First, data. As things stand, insurers typically organise, manage and govern data for management information (MI) and business intelligence (BI) applications. Only once that’s done do they adapt it for analytics.

It’s the source of huge frustration for data scientists within these organisations. They spend most of their time re-engineering data for analytics purposes and then attempting to retrofit the outputs into their existing systems.

Along with the added effort this demands from their data scientists, it means the majority of the data at insurers’ disposal remains unused and unexplored, whether that’s because it’s unstructured, excluded from the traditional MI infrastructure, or its value is simply not being recognized.

Next technology. Instead of being built for advanced analytics, most insurers’ legacy technology stacks are built to support MI and slower cycles of analysis. This means that the latest advances in maturing technologies like cloud-based NoSQL databases, open-source R language, data ingestion and visualisation layers are all out of reach.

And last, but certainly not least, people. All too often, we see insurers attempting to leverage the management structures created for MI/BI and apply them to the delivery of advanced analytics. That’s not going to work. MI teams create highly regimented outputs that must be consistently repeated to tell the business how it’s performing. They’re performing a vital function. But the qualities they possess are diametrically opposed to the core qualities of successful advanced analytics teams: highly creative, constantly seeking out new value and structured for agile-lean delivery.

Insurers know they stand to gain significant competitive advantage by transforming their businesses on a foundation of analytics. In an increasingly tough marketplace, it’s the gateway to actionable insights and tangible value. But despite widespread recognition of this fact, they’re being held back by a legacy setup and a mistaken belief that advanced analytics can be executed using traditional approaches and existing data.

In the next two blogs, I’ll be demonstrating how, by building new, analytics-specific capabilities across data, technology and teams, they can reap the benefits from actionable insights, enterprise-wide and in near-real time. Thanks for reading.

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