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Analytics Programs to Navigate Complex Times

Jane Rheem, Director of Enterprise Analytics, UFG Insurance

While a majority of the insurance landscape remains hesitant to embrace new technologies and processes, a long-standing industry leader such as UFG Insurance is changing that narrative. Named as Forbes’ “Americas’ 50 Most Trustworthy Financial Companies” for a fifth consecutive year, UFG, founded in 1946 as a stock insurance firm in Iowa, is leading the charge with regard to the utility of analytics in the multifaceted world of insurance. By staying true to its “simple solutions for complex times” tagline, UFG is simplifying business practices and safeguarding assets, for businesses of all sizes and specialties, insurance agents, and policyholders.

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I believe the insurance sphere is experiencing a dynamic shift, bringing in analytics professionals from external industries. As someone from a software background, I observe a need for a fresh perspective from outside the industry, to truly drive progress in insurance companies. Due to my technological expertise, I am able to help UFG incorporate technical methodologies and techniques while building an analytics program.

It is fair to say that insurance companies are still hamstrung by legacy technology and are therefore unable to accept data in a data model format that is conducive for a modern reporting and analytics program. This kind of monolithic corporate culture seeps through most insurance companies and makes it difficult for them to modernize a platform around how information is disseminated.

"It is also important to spend enough time with the partner, as the education process goes both ways, in terms of analytics professionals understanding the business and vice-versa"

UFG’s analytics-driven approach encompasses five critical factors of a successful analytics program.

1) Choosing the right projects: An analytics program must always be tethered to the organization’s profit and loss (PNL). At UFG, our team always tries to pick a target that faces an existing pain point within the industry. Everyone wants to be a part of the solution, and such a rally cry for progress builds collaboration and momentum inside the organization we’re helping adopt analytics.

2) Simplicity, to build transparency: It’s essential to start with the basics. For example, in our environment, we dive straight into predictive models by providing a robust suite of Key Performance Indicators (KPIs) that have multi-dimensional programs. However, it is structured in a user-friendly format without any sophisticated predictive models. We did so by dealing with all the complexities in the data model separate from the user community. We built the visualizations of the KPIs in a way that businesses/users can go ahead and connect, pick-and-choose what they want to see, where they want to drill down. This way, we’re creating a lot of transparency with the data that already exists.

3) Cleaning up your data: While devising a successful analytics program, it is critical to treat data as a strategic corporate asset. As the old saying goes “Garbage in, garbage out.” It’s nearly impossible to build robust models with complete and accurate data.

4) Collaborate with partners: Once you have the model-ready data, it is essential to collaborate with your business partners—who must be able to adapt to the predictive model without any roadblocks. If you are unable to get the partners to buy into the analytics program, they will not enjoy the impact on the business that you initially anticipated. It is also important to spend enough time with the partner, as the education process goes both ways, in terms of analytics professionals understanding the business and vice-versa. This level of interaction between the business partner and analytics professionals leads to the adoption of a perfect analytics model.

5) No room for shortcuts: There is a need for a lot of patience from everyone involved in the process. While the analytics professionals need ample time to develop capabilities, the business partners must be able to co-operate for the successful execution of a program. There is no magic bullet; no vendor can just come in with a magic wand, to miraculously gift organization analytics. It is critical to building a program from the ground up. As much as we’d love to leapfrog a few sets, I haven’t seen shortcuts work in the past.

In conclusion, it is a prerequisite for the leadership team of an organization to “buy in” into an analytics program. Since an analytics program often challenges the status quo, it is imperative for an organization to change its processes, technology, and even corporate culture to fully embrace analytics. In a way, adopting analytics is like creating a new habit, which has to be deliberate and intentional until the process becomes muscle memory.

 Few Analytics companies: Adaptive Analytics, Atlas KPI, HAIvia​

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