[Blog Series] The Painful Irony of Insurance and Data

In our blog series, we 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 keep 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 supercomputer 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 technological advancements 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

Insurance Data: Where Did That Number Come From? — Dealing with insurance data? Apply these four concepts for a single version of the facts, a single interpretation into truth and confidence in your conclusions. 

Insurance Business Intelligence: How To Spend More Time on Value-Added Analytics — There is no one-size-fits-all solution for insurance business intelligence needs. But, there’s a four-part approach and set of technologies that can help. 

Data in Insurance: How Real is the Need for Real Time? — Learn how you can provide real-time status updates on insurance policies and claims as well as personalized reports and statistics for in-field adjustors and agents. 

Data Governance in Insurance: All Pain, No Gain? — There is no way to eliminate all the pain in data governance. It is hard work. But with a good plan, you can minimize the pain and maximize the gain. 

A Modern Insurance Analytics Platform is Better, Faster, Stronger and Cheaper — Modern technology, along with industry knowledge and experience, means an analytics project can be delivered better, faster, stronger and cheaper. 

Enriched and Raw Data in Insurance: Can’t They Just Get Along? — Raw data in insurance needs processing, cleaning, and polishing. Only then can it be packaged. This is what we call data enrichment. 

Recasting IT Leaders as Strategy Partners for Growth at P&C Insurers — To remain competitive in a disruptive market and drive growth, insurance companies must recast IT leaders as strategy partners. 

Applying a Customer 360-Degree View to P&C Insurance Companies — A Customer 360-view allows forward-thinking, customer-focused insurers to differentiate themselves, retain customers and capitalize on opportunities. 

Technical Debt: Why is Such a Simple Change Taking So Long? — Are you paying for decisions made in the past, with increased costs and complications? Learn how to avoid this technical debt in the future.