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Moving from Descriptive to Prescriptive Analytics

Moving from Descriptive to Prescriptive Analytics

Moving from Descriptive to Prescriptive Analytics

In its 2018 study, From Bottom Line to Front Line, Accenture identified a growing trend among CFOs and other senior finance leaders. The study, which interviewed nearly 750 key finance leaders, determined that some 53% of CFOs believe that their teams are not keeping up with today’s increasing needs to gather, analyze, and interpret data. Few are optimistic about resolving this soon; 46%, expect little to no improvement in the next two years.

The term ‘data analytics’ has gained a lot of traction in corporate boardrooms. It sounds smart. But what does it mean to implement a data analytics approach, to invest in optimizing your analytics approach, and to start to derive meaningful value from these investments of your time, talent, and resources into these initiatives?

Insurance Analytics = Data Analytics

Insurance companies rely on data analytics to guide business decisions. Various forms of data analytics serve different purposes:

Descriptive analytics answers the question: What happened in the past? Descriptive analytic routines mine data to form conclusions, identify trends, and deliver insights on past activity.

Predictive analytics answers the question: What could happen in the future? Like descriptive analytics, predictive analytic routines mine data. However, the routines used to analyze data include forecasting techniques and statistical models that indicate what might happen in the future.

Prescriptive analytics answers the question: What should we do? Prescriptive data analytics combines situational and optimization analytics to consider various outcomes and help an organization determine its business options.

 

3 Types of Data Analytics: Descriptive, Predictive, and Prescriptive Analytics

Moving Insurer Data Analytics from Predictive to Prescriptive

Insurers first used descriptive analytics. Data processing constraints and inefficient data gathering led to a past-focused approach to data analytics.

At first, insurers answered questions such as which insureds were mostly likely to incur certain types of losses, whether the premiums they assigned to certain insureds matched their risk characteristics, and which claims might be fraudulent.

Sometime later, insurers’ capacity for data analytics evolved to include a more predictive approach. At this point, claims personnel worked with sales, underwriting, and actuarial staff to determine the likelihood that insureds’ future claims activities would reflect past findings.

Although today’s data analytics still includes descriptive and predictive analytics, insurers are increasingly moving toward prescriptive data analytics by integrating situational and optimization analytics into their routines.

But, How Does an Insurer Move from Descriptive Analytics to Prescriptive Analytics?

Once you’ve decided the time is right and the resources are ripe for your insurance company, you should take the following steps to transition from descriptive analytics to prescriptive analytics:

1. Executive Buy-In

The shift from descriptive analytics to prescriptive analytics will require personnel, among other corporate resources, to succeed. It is, therefore, crucial to convince executive leaders that the shift to prescriptive paradigms, data usage, and tools is invaluable to setting the company’s strategic direction.

Without the support of your company’s top leadership, no attempt at transitioning your company to prescriptive data analytics will succeed.

2. Acquire the Talent You Need

Next, you will need to produce results. This means finding the right people to build your new data analytics program – data scientists, domain experts, IT personnel, and other staff members to grease the team’s wheels. You must build a well-oiled cross-functional team to maximize the chances of delivering a data analytics program that produces the most comprehensive and incisive conclusions.

3. Get the Ball Rolling

Sometimes the biggest hindrance to launching an initiative is a reluctance to just get started. This is especially true with analysis paralysis in the data analytics world. However, once you have leadership support and the talent you need, you must get started.

Since you already have a data analytics program, you can begin your company’s transition by simply building onto already existing data analytics infrastructure and incorporating the tools, techniques, and talents that are part of prescriptive data analytics routines. For instance, your company can start updating data storage tools and strategies, data moving procedures, and data integrity and protection mechanisms.

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4. Lay the Groundwork for Continued Success

Once you have begun building your company’s prescriptive analytics program, it is easy to lose sight of the more mundane housekeeping tasks. However, to keep your data analytics routine running optimally, it is important to define and assign tasks. Even basic tasks, such as the importing of new data, cannot be overlooked. Keeping track of and delegating simple tasks is an easy way to ensure that your updated prescriptive data analytics routines run smoothly and effectively in your business.

5. Run the First Project

There are sure to be bumps in the road when you run your company’s first prescriptive data analytics routines. In fact, as the program matures, it will continually require course corrections.

The first project is especially formative, as it is where your team can very quickly learn who should be assigned to what, as well as what procedures work or do not work for your company. However, even beyond the first project, be sure that your team constantly and consistently identifies, logs, and formulates solutions to hiccups and inefficiencies in your data analytics pipeline.

Read More:

How Predictive Analytics are Bridging the Gap Between Claim Management Demands

The Rise of Analytics Software in Risk and Insurance

What is Omnichannel and How Does It Affect Insurers?

Helping Insurers Accelerate to Great

Data analytics is far more than a buzzword in today’s corporate strategy meetings. Developing data analytics routines that deliver actionable descriptive, predictive, AND prescriptive conclusions is invaluable for producing the best conclusions from insurer data and providing the most optimal strategic steps for your business.

CHSI Connections can help your insurance company improve your omnichannel experience, solve clear-cut problems, and create efficiencies in how you gather, analyze, and ultimately use data. To find out more about CHSI Connections can help you, contact us and we will show you how our award-winning policy and underwriting software can work towards optimizing your business.