Filed Under:Technology, Analytics & Data

Turning Data Into Business Intelligence

Deloitte Consulting’s John Lucker looks at what insurers need to be doing to get the most from their BI initiatives.

Insurance carriers have accumulated massive amounts of data over the years, which can present a daunting task when it comes time to turn that data into business intelligence (BI). That’s why Deloitte Consulting principal John Lucker believes carriers must conduct a proper data inventory if they haven’t done one already.

“It’s not just a matter of what comes off the top of their head,” says Lucker. “But looking throughout the organization for non-traditional data and in particular in-house data they have purchased or they have licenses for that might be useful for them.”

Carriers also have large amounts of manual data that Lucker maintains could be made electronic. For example, carriers want to perform BI or predictive analytics around MVRs, but very often the companies don’t have that data in an electronic format.

Lucker believes the MVR reports are an easy thing to capture, though, and having the files in an electronic format provides tremendous value.

Lucker also advises insurers not to obsess on data quality, either.

“This is a big topic with regard to embarking on BI projects,” he claims. “Typically, business intelligence is not a counting exercise. The phrase you hear often is ‘good is often good enough.’”

Lucker recommends performing an exercise in which you find data you think is dirty and you design an analysis of the data before the data is cleaned. The company then cleans the data and performs the exact same analysis on the data after it’s been cleaned.

“What I’ve found almost every time I’ve done this with a client is they’ve seen the cleaning exercise doesn’t significantly change the trends and pattern that they were setting out to see in the business intelligence exercise,” says Lucker.

Lucker believes there are various statistical reasons why this occurs.

“Very often the dirt in the data is random,” he says. “If you think of it like a bell curve, if there is dirty data on the right of the bell curve, statistically speaking there would be another dirty data point to the left of the bell curve. If you plotted all the dirty data around distribution you would see they average out. The statisticians on my team always say typically dirty data cancels itself out. There’s some inherent bias in that dirtiness, you can understand that but you don’t necessarily have to clean it.”

The Next Steps

Lucker maintains it’s important for carriers to be thoughtful and holistic in determining what they want to accomplish with the business intelligence project. Once they design the docket of work to determine what specific BI findings they hope to discover, it’s important to anchor that work to a cost-benefit analysis.

“Look for ways to create the best short, medium, and long term project dockets so you can provide the organization with some tangible return on investment to help the executives and the rest of the team realize value throughout a timeline,” says Lucker.

Lucker finds that some companies—at the end of the two-year effort—hope they finally get something for their effort.

The project dockets enable insurers to accrue benefits throughout the course of the project and this gives them the opportunity to achieve business satisfaction and a return on investment.

The dockets also give the carrier the opportunity to alter their course, which is something a lot of companies need.

“They go so far along they make [the project] bigger than they need to,” says Lucker. “If it doesn’t go as well as they had hoped there can be a tremendous emotional hurdle to overcome.”

Follow the Rules

Sticking to the 80/20 rule is important, according to Lucker, to avoid scope creep, which inevitably takes place when carriers begin these BI projects.

“I’ve been in project planning meetings and clients will say they understand the 80, but what about focusing on a small amount of the business,” says Lucker. “They end up focusing an inordinate amount of time on the 20 percent and completely lose track of the business value and the value of time in accomplishing the 80 percent sooner rather than later.”

Organizing the staff is an important initial step because companies are not always thoughtful around determining who the right people are to work on these BI or analytics projects.

“My experience is it takes a certain aptitude and a passion for BI and analytics,” says Lucker. “Not everybody is good at the mechanical aspects of working with data or working with the software tools or passionate about the level of granularity that’s required. Find out who the go-to people are and make sure they are involved from the onset.”

Generic or Industry-Specific

Lucker believes the term business intelligence is often misused or overused.

“I think software vendors try to paint their tools to be bigger or broader and sometimes even overly specific,” he says. “There are tools that are foundational for BI that help you organize data, populate data marts or data warehouses, but ultimately when you get to the data side there is a whole suite of agnostic data tools, BI tools, and industry-specific tools that have different purposes.”

For insurers, it depends on the type of people they expect to be doing the business work and the level of flexibility they want to provide those people.

If you are giving BI responsibility to business people who are technically oriented—like a really good business analyst—they are going to be confident with a tool that is flexible but more complex,” says Lucker. “But if you are expecting a more business-oriented desk-worker to be doing BI work or using BI tools, you require a tool with a different level of ease of use—less programming and more clicking type of tool.”

Lucker believes smaller carriers seem to be further along with business intelligence and the larger carriers—because they are bigger and more complex—have some pockets that are struggling with BI.

“It’s easier for smaller carriers to be more holistic,” he says.

Lucker maintains industry-specific tools are not all that important for insurers.

“Companies sometimes pick tools that are highly specific at that point in time, but their vision or their needs might change,” he says. “There’s a real need to think seriously about the claims of the software industry to say this is good for this particular industry versus this is a more robust tool. You need people who are expert in using the tool and they can create specificity with a more generic tool.”

Hampering the Efforts

Lucker believes BI has become universal throughout the insurance industry.

“Virtually every company would say they do it,” says Lucker. Obviously there’s a continuum of what that means, but I do think a lot of companies are struggling to take business intelligence to a higher level. Companies are struggling in how they get enough people to understand what advanced analytics mean, what the art of the possible is.”

Lucker continue to see significant organizational inertia issues—a fear of change—in how companies can become more information driven and fact based and less intuition driven and allowing things to happen to them by the market rather than being more forceful in the market.

“I’m seeing plenty of success stories,” says Lucker. “The way I always think about success is not everything translates to dollars—very often it does and should—so how do you articulate your ROI. That’s a very basic question, but it’s still a vexing problem for insurers in terms of how they calculate the value. You have to be comfortable with the idea you can’t always measure everything in dollars and cents.”

 

 

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