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Everyday Insurance Analytics: Using Data at Every Level

Data analytics have now permeated most levels of insurance company organizations, creating exciting new opportunities but also some interesting challenges. Not all business units are equipped with data scientists and insurance analytics experts to help decision makers navigate what can seem to be uncharted waters or a flood of information. The challenge insurance companies face is making data accessible to a wide range of stakeholders who have little experience in the field of data science in a way that enables organizations to achieve the greatest value and benefit from both their data and human resources. There are several steps insurers can take to foster a culture that maximizes the effectiveness of data analytics at all levels.

Determine Your Question and Narrow Your Focus

For many, analytics can be intimidating simply because of the massive amount of data that is at our fingertips. I often liken it to an author working on a novel: the hardest step is usually just getting started. Being inundated with data from every angle makes it difficult for an everyday user to get started in gleaning insights from analytics. In order to overcome this challenge, it is important first to define the question one wants to investigate, essentially starting with the first step of the scientific method. This narrows the focus of inquiry, eliminating wide swaths of data that do not contribute to answering the question. This step is arguably the most important because the people who are at work on the frontline of the organization on a daily basis often have much greater insights into what kinds of questions need answering first.

It is important first to define the question one wants to investigate, essentially starting with the first step of the scientific method.

Beware the Interpretation Gap Trap

Even though the people working on a particular question may not be insurance analytics experts, it is still essential to understand the source of data and the methodology used to produce them. Think of this as the research an author does to get started on a novel. When telling the story of the results, either within or outside of an organization, it’s necessary to be clear about how a particular insight was produced and what data were used to produce them. To get there, one must work to bridge what the MIT Sloan School of Business calls an “interpretation gap”. Asking questions that help to ensure the data being produced are applicable to practical, real world scenarios will produce a more reliable result that business users can lean into with confidence.

Enhance Domain Expertise with Data

Insurances companies must remember that analytics are not a substitute for the intimate, first hand understanding of a business, something often referred to as domain expertise. The Boston Consulting Group defines domain expertise as, “superior knowledge and insight into a business or category,” and goes further to state that domain experts “use this insight to spur innovation, to see through complexities, and to imagine what could be.” Data are not a replacement for domain expertise but rather complementary to it. This distinction helps prevent viewing analytics in a vacuum.

If something doesn’t seem like it adds up in the data, use domain expertise and ask.

If something doesn’t seem like it adds up in the data, use domain expertise and ask, as Florian Zettelmeyer, the Academic Director for Kellogg Executive Education’s Leading With Big Data and Analytics program explains, “Knowing what you know about your business, is there a plausible explanation for that result?” This is where an author would combine research with personal experience to craft a story with a logical plot that will be most appealing to the reader. Companies cannot rely on data or observations alone to guide actions but should use the results of both to form a more complete view of the business or the particular question they have chosen to answer by analyzing data.

Make Small Improvements for Profound Payoffs

Success in asking the right question(s) and making sure the data analyzed meaningfully inform the decisions at hand means nothing if data analysis is not followed by action. The actions taken will look different depending on the questions that started the process. The author is now completing the process of actually publishing the novel and distributing it to the public. It is necessary for insurance business leaders to avoid creating action plans that completely overhaul or revolutionize their organization, but rather focus on more granular goals. According to McKinsey and Company, “The impact of ‘big data’ analytics is often manifested by thousands—or more—of incrementally small improvements. If an organization can atomize a single process into its smallest parts and implement advances where possible, the payoffs can be profound.” Too often, companies ignore the value of small improvements and their potential for exponential payoffs that accrue from building on the foundation of small improvements over time.

The real key to success with every day insurance analytics comes down to one thing: engagement.

Engage with Your Insurance Analytics

The real key to success with every day analytics comes down to one thing: engagement. Leaders and their teams that are simply engaged with analytics are already ahead. Producing analytics should not be a goal in and of itself; the goal for insurers is to use data to make better decisions and to increase efficiencies within the organization. The author does not publish a novel simply to do so, but to delight readers and to make a small contribution to society. On an organizational level, data analytics is really not so different.

Mitchell WorkCenter Scorecards
mitchell workcenter scorecards

In order to achieve high levels of engagement with insurance analytics, the team must have access to tools that take away the intimidation factor. For example, Mitchell’s WorkCenter™ Scorecards provide users at every level of an insurance company’s hierarchy access to simple, easy to understand analytics that don’t require an advanced degree in computer science to understand. With every day tools like scorecards and dashboards, managers have the ability to customize a full suite of key performance indicators in addition to being able to choose how results are presented to team members.

In order to achieve high levels of engagement with insurance analytics, the team must have access to tools that take away the intimidation factor.

Modern benchmarking functionalities, such as peer group rankings, give users insights into how their own performance measures up to the rest of the group and provide leaders with an easily consumable, transparent view into their organization—and where the greatest opportunities for those essential small improvements exist. By making performance data accessible in a format that is practical for all levels of data expertise, insurance companies are more likely to increase engagement with analytics—allowing for truly expert story telling.

Mitchell WorkCenter Scorecards

Contact a Mitchell sales representative for more information about our new and improved WorkCenter Scorecards.