June 10, 2015
3 Analytics Strategies for the Middle Market
Middle market carriers face an imperative: Make sense of data through analytics. But that means fighting the war for analytics talent.
As if there isn’t enough pressure on middle market carriers today, with the big players combining to get even bigger and with the rolling up of supply chains — the carriers are now faced with a strategic imperative: Make sense of their data through analytics.
Meeting that imperative comes with a new competitive issue: fighting the war for talent to recruit and retain data scientists.
The demand for data scientists is spiking at a time when it can’t be met by supply. The largest organizations have enough scale to fund and attract a team of analysts, but what is the middle market insurer to do?
There are some straightforward strategies:
Count on partners
Many of the business demands for analytics will be met with software tools. The vendors for these solutions will be more than happy to have some data science types participate in your implementation and help to sort out your data. The same is true of marketing campaign vendors. They will have in their circles the experts needed to slice and segment targets, just like the large insurers can do on their own.
The services vendors are investing and building muscle in big data and analytics. Just as insurers augment their in-house actuarial talent when needed, we see the ecosystem of services vendors maturing nicely. You may pay more per hour than if you hired someone, but you only pay for what you need and you get a team that has “been there and done that.”
Decide not to decide
We talk to a lot of middle market companies that are looking at big data analytics. Some are saying that they aren’t seeing the demand for it from the business areas. They know this may mean that people aren’t doing enough to evangelize about analytics within the business, but analytics have no value if they don’t meet some kind of demand. If there’s no demand, push analytics out on the road map — but keep it on the road map. That allows you to revisit the subject when the labor market for data science talent is less frothy.
As is often the case, the reality is that most of the companies we see are doing some combination of these three strategies. They are engaging tool vendors for particular complementary needs, reaching into the service companies when that makes sense and putting the investment in their full-time staff until resources are more available.
At the end of the day, we see the middle market reacting creatively and nimbly to the challenge. But, hey, that’s what they do with all of the challenges they face, so why would this time be any different?