What Does It Mean To Be A Chief Analytics Officer?

I named this post, “What Does It Mean to Be a Chief Analytics Officer,” because the CAO role has a certain titular appeal. However, it’s sometimes the case that those who lead analytics functions for their organizations don’t have that exact title. And whether you’re a CAO, a “Director of Global Analytics,” a “Head of Science,” or a “Chief Data Scientist,” the job has some similar elements across organizations. This column is about the elements, not the specific title.

I’ll illustrate those common elements with reference to a particular CAO, Adam Kornick at Aviva PLC. Aviva is the largest insurer in the UK, the second largest in Canada, and also has operations elsewhere in Europe and in Asia. Kornick is an American who previously headed a big data and analytics group at Progressive Insurance in Cleveland, but he became the CAO at Aviva in September 2014. I don’t have any financial interest in Aviva; I just admire Kornick’s work.

One common element of these types of jobs is that they are important to their organizations. Big new “Chief” roles aren’t established from scratch without reason. And to companies like Aviva, it’s hard to overemphasize the importance of analytics. Insurance companies have always relied on analytics to price risk, but they are also increasingly using analytics to market to customers, analyze “pay as you drive” data, and evaluate claims for fraud. Aviva and other insurance firms are also increasingly personalizing their offerings to specific customers, which demands a considerable amount of data and analytics about customer behaviors and preferences. It’s hard to imagine an industry with a greater dependence on data-based decision-making. Aviva has made a strong commitment to the capability, with over 100 quantitative analysts in Kornick’s organization.

Another common factor among CAOs is a strong emphasis on evangelism about analytics and what can be done with them. They are effectively sales reps for a different approach to decision-making. Many managers don’t understand either the basics or the subtleties about these topics. Kornick reports that he spends a lot of time evangelizing and explaining about the differences between reporting and analytics, what might qualify as “big data,” and what the business benefits of using analytics might be in particular contexts. If you are considering making someone a CAO and they’re not particularly good at evangelizing and selling the capability, you might want to reconsider the appointment.

Kornick says that the selling has to be approached with some humility. He asks each manager, “Tell me what’s different in your market?” After he hears a particular problem or issue he offers, “How can we use analytics solution to make it better?” He insists to all concerned that Aviva isn’t doing science for science’s sake, but rather to execute more effectively and make the business perform better.

CAOs everywhere also face the issue of potential role overlaps and conflicts with other jobs and organizations. Analytics in some form or other have been around for a while, as has been the need to prepare data to be analyzed. So it’s not surprising that a CAO organization’s capabilities would overlap with other parts of the organization. In insurance, the most likely overlaps are with the actuarial organization—a group that primarily analyzes risk data to develop pricing models. Kornick’s group contains some actuaries by background, but he tries to make clear that the primary focus of his group is not actuarial work. There are increasing numbers of issues in insurance that go well beyond traditional actuarial concerns.

Another constant in the emerging CAO profession is variability across the business. Particularly if the business is global—as Aviva’s is—there will be substantial variation across global business units in data and analytics needs. Aviva has life, health and P&C businesses across a number of countries, and they’re all a bit different.

Kornick’s approach is not to assume that a solution developed for one part of the business will be applicable elsewhere. But he does try to spread and leverage solutions if they’re relevant. Originally his group worked on one business and one business line at a time, but he’s trying to make it at least two at a time going forward. Since his group can’t do everything simultaneously, they try to share information. He might address the head of a P&C business with an analytical approach from a life business that seems relevant. Everybody does pricing, so how can pricing approaches from one business work in another? If the UK has made some improvements in the health insurance business, how might that apply to Ireland, France, and Singapore, all of whom also have health businesses? Automatically assuming that ideas will work everywhere is foolish, but not pursuing leverage when it’s feasible means leaving a lot of value on the table.

Data and analytics aren’t going away, and it seems very likely that Chief Analytics Officers—or their equivalents with slightly different titles—are going to be with us for a while as well. If this is an important business resource—and who would doubt that it is?—it needs leadership. Given the importance of analytics to many businesses and industries, it wouldn’t surprise me if CAOs prosper and become even more important to their companies than they are today.

This post originally appeared in WSJ’s CIO Journal.