In our blog series, we’ll explain how to modernize insurance data and analytics by pairing a modern data architecture with an agile delivery approach.

Anyone who has spent a large portion of their career in the insurance industry has likely experienced a very strange occurrence.

We work in a business that sells its ability to predict risks by analyzing vast amounts of complex information. The data comes from a variety of disparate internal and external sources and has driven profits for decades.

Knowing this, newcomers might naturally expect that data is robust, widely available, and extremely reliable.

If you’re passionate about data and analytics, you might even gravitate toward the career path given that expectation. Unfortunately, you will quickly find yourself experiencing the painful irony of insurance and data:

  • Data isn’t as widely available or reliable as you expected.
  • It is difficult to make business decisions using quality data.
  • And you are not able to analyze the data you need to.

Finding yourself in this situation, it’s tempting to assume you just picked the wrong company and that the grass is greener at another carrier. As data professionals who have worked with dozens of insurance carriers over several decades, we can tell you, it isn’t.

What’s the answer?

Now you’re probably wondering “Is our industry ever going to keep up?” or “Will we be able to attract new talent?”

The simple answer: it doesn’t have to be this way, and we’re working to make sure it won’t be for much longer. The longer answer will be covered in this blog series.

No problem worth solving is ever simple, fast, or easy. And this is a problem worth solving. If you need to explain the importance to a layperson, suggest they ask themselves a question or two:

  • Would you buy that house if you knew a fire would leave you homeless and on the hook for the mortgage?
  • Would you own a car if you knew one reckless driver could cost you your investment?
  • Would you risk running your own business if you knew one accident could bankrupt it, and your family?

Improving the efficiency, flexibility and predictive power of the insurance industry helps us all.

How did we get here?

When the idea of accident insurance was first implemented almost 170 years ago, it was extremely narrow in scope. It was intended to cover injury in the event of an accident while traveling on a train. Losses were predicted based on the quality of the cabin you traveled in.

Now, consider the modern-day auto insurance policy. It covers multiple parties, risks, and loss types all driven by a list of different variables that keeps growing with every passing year.

In short, the problem keeps getting more complex and the technology available to handle it has been changing just as quickly.

Let’s not forget, the smartphone in your pocket is more powerful than the average super computer of just thirty years ago and costs between $500 and $1,000, not tens of millions of dollars. As you add this all up, the current situation was inevitable.

With only so much money to invest, carriers spent wisely and focused on core business: improving pricing, billing, and claims modernization.

Unfortunately, with all the technology advancement that made that smartphone in your pocket possible, the industry now has a lot of catching up to do. Just ten years ago, catching up meant an investment of tens of millions of dollars and a high likelihood of failing before you reaped any benefits, assuming you succeeded at all.

Today, we know more, have more options, and the price tag is a fraction of what it used to be.

Read the Blog Series

Over the coming months, we’ll be releasing a series of blog posts focused on modernizing insurance data and analytics. We’ll cover common problems and discuss how a comprehensive modern data architecture paired with an agile delivery approach can help tackle those issues.

Here’s a sneak peek at some of the topics (in no particular order):

  • For every “season” of data, there must be a reason! – Choosing between the cost, complexity and selective necessity of real-time, near real-time and batch data delivery isn’t simple. Whether it’s quote data or third-party data supporting underwriting, how the business will use it is just as important as what it is for when determining how it is delivered.
  • Was my customer your customer? Wait, what do you mean “was”? – Knowing who your customers are and how you can better service them is important. In insurance, multiple source systems with various levels of maturity are coupled with inherit differences between commercial and personal lines. This complexity can create pain points when decisions are made without having all the information.
  • Where did that retention number come from? – A common set of terminology and the associated definitions isn’t as prevalent as you might think. Looking at three different pieces of information, all claiming to represent the same thing but giving different answers is common and happens for good reason. Unfortunately, it also causes confusion and distrust in information that would otherwise be invaluable in making complex business decisions.
  • How do I get all this brainpower to spend more time on value-added analytics? – The 80 / 20 rule of analysis, meaning you spend 80 percent of your time analyzing data and 20 percent organizing it, is all too often inverted. When living in a world of spreadmarts, lost productivity is a fact of life, and a problem worth solving.
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