It may be hard to believe, but the insurance industry is the original data industry. Before rise of Google, Facebook and other digital giants, insurers had better data and information than any other industry on its customers, its claims, and its products. They used to have warehouses of paper files which distinguished them from their peers. The trouble was just accessing and collating it together.

Amazingly, that problem is still around today. We have moved from mainframes to clouds, yet much of a carrier’s information is still locked away in filing cabinets. Those cabinets are digital, but the information is just as inaccessible in PDFs or images. What’s worse is that it is a problem that carriers often perpetuate themselves.

Just the other day, I went into a carrier to discuss their premium audit process. What we found was that their “modern” process was to email the customer a form that the customer had to print off, fill out, and mail back to them. They were driving the customer to create more paper for them. What’s worse, they were only using 20% of the returned information. The rest stayed locked away indefinitely except for exception cases.

This is a lose-lose scenario. Customers have their time wasted filling out needless paperwork. The carrier loses any valuable insights the information in that paperwork might yield. We’re creating costly processes and wasting valuable data.

The situation is much more common than you might think.

The problem of “dark data”

The unused information in those forms is one example of “dark data”—information that organizations have access to but don’t use. New research from Accenture Tech Advisory into dark data estimates that up to 75% of the data in an organization might be “dark.”

Sometimes, as with the example above, data is “dark” because the organization doesn’t have a program in place to use the information.

More often, there are challenges involved with accessing the information in a cost-effective manner—think scans of handwritten documents or voicemail recordings.

To see how dark data can hold an insurance carrier back, let’s look at an example from auto insurance: a picture of the scene of an accident. Depending on the details, this image could reveal:

  • The license plate and color of the vehicle
  • The severity of damage to the vehicle
  • The accident’s location, if the image includes street signs or other landmarks
  • Information about the weather and time of day

On its own, this information could be enough for an insurer to open a claim and produce an estimate of damages. But that can only happen if the data is “captured” by the business. Otherwise the data just sits on the shelf.

Illuminating dark data can provide valuable material for the big data analytics systems that leading carriers are now using to make smarter decisions across the enterprise.

But the benefits of tapping into these information sources go much further. Dark data, historically, has been the biggest barrier to achieving “straight-through processing,” or STP—the holy grail of business process automation.

Automation is increasingly popular across insurance right now, but its implementation is mostly piecemeal and partial. One leading automation services provider advises targeting 70 percent automation for a given process. (STP, for reference, would be nearly 100 percent automation.)

Dark data is why they don’t aim higher. Machines need human help to “digest” the data and watch out for errors.

Insurers have developed some workarounds for this in specific instances. But to date, finding the right tool for every use case has been impossible.

That’s what’s so exciting about the new report linked above from Accenture Tech Advisory, which outlines a new, comprehensive approach to illuminating dark data. Here’s the four-step system program in brief:

1. Use-case definition and enhancement

The first step to putting dark data to use is getting an accurate picture of all the dark data in an organization. Accenture can help insurers achieve a this through detailed analysis of existing documents, data channels, existing data extraction technology, and associated processes. This step lets us find both quick wins where we can eliminate self-inflicted wounds and broader opportunities for more transformational changes.

2. Tool selection

There are many tools and vendors available on the market that claim to be able to digest dark data. Knowing which will yield the best results is very difficult. Accenture’s Tech Advisory team has identified, tested, and benchmarked dark data ingestion tools across the industry to help an insurer pick the most effective one for their use case. In most cases, what we find is that the answer isn’t a single tool, but a selection of tools that together can be transformational.

3. Turn concepts into reality

Tackling dark data begins with defining the concepts and opportunities where illuminating dark spots in our data assets can yield value. This can be done by tackling processes where we are forcing paper-driven processes, or opportunities to unlock data in existing processes. Either way, once the end-to-end process for a given area is understood, an array of different process and technology solutions can be evaluated to see which solutions have the potential to work. Then rapid prototyping or piloting to prove they work will bring these concepts into reality. For example, in the premium audit example above, moving from a mailed to an online form might be a way to rapidly test or pilot how we can move away from the current paper process.

4. Transform the business

The goal of illuminating dark data is not to digitize it for its own sake. Illuminating dark data must be tied to real business outcomes. To do this, there must be plans to transform processes and operations to take advantage of the data with analytics, automation, and insights. This includes changes to technologies, operations, processes, and business intelligence and the ways we do business.

Illuminating dark data and making STP possible is not a simple or short process—but it does have the potential to realize huge gains for insurers.

If you’d like to continue the conversation about dark data in insurance, I’d love to hear from you. I can be reached here. 

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