Data Chief Podcast
THE DATA CHIEF | EPISODE 16

In Search of Bad Data's Saving Graces

Chad Hawkinson

Chief Product and Data Officer

Vertafore

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EP16: In Search of Bad Data's Saving Graces
EP16: In Search of Bad Data's Saving Graces
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Joining Cindi today is Chad Hawkinson, the Chief Product and Data Officer at Vertafore, the leader in creating modern insurance technology. A seasoned data and analytics guru, Chad has seen first-hand the profound impact data-driven insights can have on customers’ success.

On this episode, Chad and Cindi discuss how the cloud makes it safer to use sensitive data, when it was long-feared it might do just the opposite. They cover everything from alleviating client concerns about the use of this data and securing this data from cyberattacks, to the value of streamlining data collection into everyday workflows rather than making it an additional headache for the client to manage. Plus, Chad shares the three components responsible for the 38 percent failure rate of analytics investments in insurance and what can be done to fix them.

Key Takeaways:

  • The cloud gives us more power over our data, not less. Qualified availability unlocks a level of control over that data that would be impossible on a purely local level.
  • Having a clear direction where data can be brought to bear . The three problems most responsible for a high failure rate in analytics investment and how to solve them.
  • The value of insights in context. Data is most useful when streamlined into the everyday workflow rather than existing as a number of separate processes that bring that workflow to a grinding halt.

Key Quotes:

Storing sensitive data in the cloud was once strongly resisted, but is now generally accepted as a necessity. Does Chad see this as a step in the right direction or a loss of control over that data?

I would say the movement of cloud has actually been a very great benefit to folks from a security standpoint, because you get the best of the best in terms of security protection by leveraging cloud. … It's better protected and you get to take advantage of the latest threat-identification and resolution solutions by being in the cloud.

How can the insurance industry alleviate customer fears about putting their personal data in the cloud?

If there's something that's going to help the industry use data better, it's being really, really specific in human language about what this data is used for, and what it's not used for, and what you're signed up for, and what you haven't signed up for.

One common misconception about data.

If you think data's just going to come pouring in and you're going to use it and drive your business forward without really taking any action on your side, it's just going to fail. ... Despite all the automation and machine learning, there's still work to be done [by human beings].

A key mistake analytics software makes.

Where I think a lot of analytics software vendors fail is that we provide analytics in this kind of separate solution over here. Keeping those separate is one of the reasons these projects fail from adoption, because nobody wants to use them; they're doing something already and trying to use a separate tool. The more of these analytics that you can embed into the workflows, the more successful the data and analytics project is going to be.

On coping with existing bad data in the system and minimizing its introduction in the future:

We view it as our role [to help] folks in this. We leverage algorithms and machine learning and third-party data to cleanse and enrich data. ... But if [you're] not entering that information in at all, and there's no real easy way to go get that information somewhere else, then those are the places where process is going to have to change within your organization.

[Ultimately], there is no such thing as perfect data. If you wait until data is perfect, you will never actually engage on a data and analytics project. You've got to find the basic insights that you can get using the data quality you have.

Does Chad agree that even bad data is useful as long as it's directionally accurate?

I agree with that, but you have to understand the limitations of that. If you understand the limitations, and the users understand the limitations, then great. The other thing to think about is: how do you use data in aggregate? Because that's another thing where individual data may have some issues with it from a quality perspective, but if you add it all up, you get the benefit of large numbers and you start to be able to get trends and directions from data at the aggregate level that you might not see at the individual data point level.

On getting agents to adapt to the help that technology can offer and change old habits that no longer serve them:

You've got to build the credibility of that data ... [by showing] how it actually supports some of the things that are true and then ... indicate ways in which you can do your job better.

Like, 'Hey, you used to take five minutes filing that email. I can just file it for you. I can just take that email from the client, put that policy where it belongs, and you don't need to be involved.' Being able to show that I can do it accurately and they can say, 'Okay, I don't need to actually file that away. I can trust the machine to file it away.

Or, 'This customer's likely to cancel on you,' 'No way! Bob's been my customer for 20 years!' Then Bob cancels on you. So starting to get where those data show you [when you don't] completely understand the facets of what's going on and how the data might be able to help [you] foresee things that [you] didn't foresee.

As a chief product and data officer, what value does Chad see in the marriage of these roles?

When I think about data, I think data out of context and data out of workflow isn't nearly as valuable as data in context and data in workflow. So I think that marriage between ... the chief product officer and chief data officer ... I think getting that partnership right, if those are separate roles or the combination of those roles, is really important. ... Being able to take that data and actually do something with it and change the way in which people work and build strong businesses on the backs of that, I think it's critically important that that partnership is there between those roles.

In spite of its challenges, here's why Chad finds his job in data so rewarding:

There are days when the data doesn't behave and do what you want it to do, and the customers don't adopt what you think they should adopt. There are certainly those days, but [these challenges are] what make it fun, because ultimately you see the value that you're driving in. The beautiful part of that data is you can measure it. You can actually measure the impact that you're having.

Bio:

Chad Hawkinson is the Chief Product and Data Officer at Vertafore, the leader in creating modern insurance technology. A seasoned data and analytics guru, Chad has seen first-hand the profound impact data-driven insights can have on customers’ success. He brings deep experience with AI and ML technologies in the energy, aerospace, defense, automotive, and construction industries to help agencies and carriers discover new opportunities to drive dramatic business growth by leveraging insights previously hidden deep within their data.

Prior to Vertafore, Chad spent seven years as Senior Vice President and General Manager for the Engineering & Product Design division at IHS Markit. An experienced go-to-market strategist in both the software and data analytics industries, Chad also held leadership roles at Progress Software, PTC, and Intel.

He earned a BA in Mechanical Engineering at Virginia Tech, as well as an MBA at the MIT Sloan School of Management.






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