Data has always been critical to underwriting. The difference today is that the amount available has increased by orders of magnitude. The new data density can alter underwriting outcomes radically.

Third-party datasets from all kinds of sources now cover a huge range of factors. They help underwriters understand the risks in front of them, and assess the wider risk portfolios they control. Data is used daily to augment risk selection, aid policy pricing, and support the application of terms and conditions.

Even the humble slip is a rich source of data, but in the era of digital placement it’s easy for underwriters to overlook what’s there, and miss enlightening information that could help enhance present and future risk understanding.

But having too much data can cause problems. Cost is one, both in terms of money and governance. Data sets come in all shapes and sizes, from many different vendors. Adopting a third-party dataset therefore increases the need for due diligence, pushes up admin, and hikes supply-chain exposures.

But even a whittled-down supply may leave data density impractically high. Underwriters are of course able to differentiate between insightful data which improves performance and unhelpful noise – it’s a key skill – but with live-streaming data cascading towards them from all directions, sorting gold dust from slurry could become all-consuming. Data overload is a real threat.

Fortunately, ways to manage and interpret dense data have improved beyond imagination in the past few years – just a few paces behind the new data onslaught. The challenge is to get the right data to the person who needs it at the right time. Access must be sufficiently smooth to make the effort worthwhile, which requires compatibility with internal systems and processes. Businesses must ensure that the technology they deploy is nimble enough to ingest the data: it must be API-friendly. Essential data maintenance and quality control must be performed.

If data is to be applied daily by the people who need it, integration with existing workflows is mandatory. For any process change to be implemented successfully, underwriters must be in total alignment with the technology team creating virtual bridges into data. Careful planning is needed to get the process implementation correct.

And it’s not just tech and underwriters. People from risk, claims, compliance, data, and actuarial must be intrinsically involved. Imposed processes seldom suit users and often end up rejected, so all key stakeholders must be involved from the start. Casting that wide net is the only way to get everyone’s buy-in and ensure the right data arrives at the correct place at the right time, in a format relevant to the problem it is intended to solve.

A clumsy integration can stagnate, irritate, or simply deliver poor results. It will ensure underwriter take-up is poor, and therefore make enterprise reliance on data low. But when it’s done right, integration of data into processes drives efficiency, automates laborious tasks, and enhances risk selection. Data density management should be at the top of every competitiveness agenda.