As if there isn’t enough pressure on middle market carriers today, with the big players combining to get even bigger, and the rolling up of supply chains — they 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 that will continue to make sense in this case:
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. (Shameless plug…so is Majesco.) Just the same way that insurers augment their in-house actuarial talent when needed, we see the ecosystem of services vendors maturing nicely. (Shameless plug…so is Majesco’s.) 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.’ (Shameless plug…like Majesco’s.)
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 from the business areas for the big data tools. They know this may mean that there’s a gap in analytics evangelization within the business, but analytics have no value if they don’t meet some kind of demand. If there’s no demand, push it out on the roadmap, but keep it on the roadmap. That allows you to revisit the subject when the labor markets 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. They are engaging tool vendors for particular complimentary 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 this challenge. But hey, that’s what they do with ALL of the challenges they face so why would this be any different?