Last week, we spoke about how analytics in the insurance organization has been growing up in different locations and that it will continue to be interesting to see how and where analytics grows into maturity. Click here if you missed that blog and you would like to catch up. Today, we’re going to step into the future and look at the most likely scenario in most insurance organizations, with the caveat that this will be highly dependent upon carrier size, type, unique features, etc.
To look at the logical location of analytics central within the insurance organization, it will be helpful to understand who will be using and needing data analytics and business intelligence (BI) reporting and how frequently it will be needed. In most organizations, this will naturally require some sort of assessment, because data gathering and analytics are changing at a rapid rate, and, if there is no current oversight, a survey/report will be needed.
For our purposes here, I’m going to assume that, sooner or later, nearly every area of the insurance value chain is going to be a consumer of data analytics. When we discuss analytics with insurance organizations today, we operate under the notion that data and analytics systems should be built with the capability to plug into areas of the organization that aren’t clamoring for analytics yet. Operations or human resources might be excellent examples. Both are areas that may one day be composed of analytics power-users but today are only flirting with the fringes of data analytics. In the case of human resources, it may be making some data-driven decisions today, but often it is supplied through health insurance payers or other areas where it is pre-analyzed. As analytic capabilities grow, staffing choices and HR communications will benefit from entirely new levels of observation and reporting.
Once we make the case that anyone in the insurance organization could be a candidate for using analytics, we can also assume that data sources and analytics may frequently overlap from department to department. To address efficiencies, security and tool use throughout the organization, it may make sense to create an analytics department that operates as a central hub serving all other areas.
Let’s use an analogy. We’re in the midst of summer, and tomatoes or cucumbers may be growing in some of our back yards. With most vining plants, one root produces multiple fruits, varying in their maturity dates. Provided they are pollinated properly (go, bees!), the one vine will give many good tomatoes at various locations along the vine.
This is roughly comparable to what may happen in many insurers. Functions under the chief data officer will be responsible for gathering, housing and securing reliable data. Imagine that function as the roots and the soil of the vining plant. The data organization will then deliver the data to the analytics organization…the main vine that will turn the data “food” into the analytics “fruit.” The fruit is the business intelligence every area needs to run its portion of the business. If we want to carry the analogy one step further, we can also consider that the fruit contains the seeds of the next generation’s growth. So the analytics organization is not only going to produce good fruit but will also offer to plant its intelligence in areas where the business wants to see new growth.
Instead of having data gathering and analytics strewn all over the insurance greenhouse, there will be one location for warehousing and one central source for analytics. It is going to require oversight by the chief actuary, the chief data officer and in all probability a chief analytics officer. The chief marketing officer and the entire C-level will be a part of determining how this new unit is built to ensure timely and effective service to the organization. The analytics team will represent a unified core that will need to balance business needs with departmental priorities. In some ways, it will look much like today’s management team, only with one goal – transforming the organization to be data-driven while keeping information secure and flowing through an ever-improving analytics infrastructure.