Pervasive data science is becoming a core enabler of business innovation and competition. Given this importance, it’s worth examining the context of data science to consider its trajectory and future value.
In my view, primary challenges around data science for business leaders comes from three sources:
- Business people do not always understand the power and implications of what’s possible with data science and machine learning. The impact on business models, operations, and customers can be profound.
- Lack of available data to feed the machine learning beast. Without historical or other data, machine learning has little value. Aggregating useful data can be expensive and time-consuming.
- Insufficient talent and resources to create models and set up analyses that can best serve users and customers. Making effective use of data science demands a different kind of thought than traditional analytics; even a culture shift, so it’s hard for established organizations to adapt quickly.
On episode 259 of the CXOTalk series of conversations with innovators, I invited two experts to examine these issues and talk about data science in financial services and insurance.
The discussion included an outstanding exposition of the broader business context and value of data science, so we pulled it out as the short, standalone video embedded above.
My two guests for this episode are:
To learn more, check out the short video above and read the edited transcript below. You can also watch the entire episode and read the complete episode transcript over at the CXOTalk site.
Where does data science fit into the insurance industry?
Murli Buluswar: The core challenge for the insurance sector is similar to some of the financial services. In insurance, you’re trying to predict your cost of goods sold at the point of sale. Getting that right is absolutely critical in your ability to achieve margins down the road. Anything and everything that you can do to understand that at its core will give you a significant competitive advantage.
Machine intelligence is the collective experience of an institution manifested through data.
— Murli Buluswar
Now, if you zoom out from that problem statement, there are many similarities in insurance other industries around the role of data science and machine learning in augmenting human intelligence and making better decisions. More structured, granular, sophisticated, consistent decisions, in sales and marketing, as well as in pricing, underwriting, and in claims, which is a significant part of the fulfillment of the promise that insurance carriers make to their customers.
Michael Li: What we call data science today is part of a long history of the application of mathematics and computing to industry.
When I joined the industry, and I started my world in finance at Wall Street, back then we used to call these jobs quant roles. You would figure out how to trade in capital markets, make predictions about which way the stock price would move. I think what we’ve seen is that the tools and the technologies that we used there were then really adopted in Silicon Valley, really turbocharged, frankly made, actually, much more usable. Then the cost of computing made it so that you could apply this not just to a few select problems on Wall Street, but all over Main Street, all over the rest of the financial services industry.
Please elaborate on similarities of data science across insurance and other industries?
Murli Buluswar: The first big dissimilarity, so to speak, when comparing insurance to other sectors is that the role of the actuarial profession dates back to the early days when insurance was created as a sector. Analytics in insurance has largely been driven by the actuarial function, which brings nuanced competencies and capabilities that are relevant to insurance.
If you think about the broader role that data science could play today in insurance, you can fundamentally reshape human judgment when it comes to sales, when it comes to underwriting judgment. Even when it comes to claims through the lens of data and technology in ways that might not have been feasible 10, 12, 15 years ago.
Like many other sectors, in insurance, you’ve got a sales or distribution channel. You’ve got a product channel that is around pricing the product. Some of that is your cost of goods sold, and some of that is trying to understand the market’s appetite and the customers’ demands, or demand elasticity if you will.
Last, but not least, you’ve got the fulfillment of the promise of that very, very data rich. So, if you break down that value chain to its core elements, there are similarities to other sectors.
Now the difference could be that if you think about healthcare, for instance, healthcare is much more a transaction, data-rich industry perhaps compared to insurance. You’re engaging with customers on a very consistent basis, just as you are in financial services, in banking, and credit cards and such. The difference, perhaps, between insurance and these other sectors is you’re not necessarily as data-rich, as transaction-data rich, as other sectors. [However,] certainly getting your cost of goods sold right early on remains critical.
Michael Li: Right, but you see this with retail. You see this through the smartphone, and we were doing a lot of that when I was at Foursquare trying to make that retail brick and mortar experience a bit more digital through your smartphone. You see this all over the place.
I think that that’s going to be a major driver of consumer electronics; you’re going to see the need for companies to have data to drive interactions onto smartphones, tablets, and wearables.
What about the technical aspects?
Murli Buluswar: To build on what you just said, Michael, if you were to contextualize that to insurance, I see the big leap in innovation happening in the next two to three years around this notion of making more granular, real-time decisions with machine learning.
By defining data not just in the traditional internal structured terms, but thinking of it in four quadrants: internal / external on one dimension and structured / unstructured on the other dimension.
The ability to build machine learning algorithms on these platforms will reshape what humans do with decision-making and judgment and where models harmonize or balance human judgment with machine intelligence.
Often, people think of it as an either/or. But if you re-paraphrase machine intelligence as nothing but the collective experience of the institution manifested through data, it brings consistency and granularity to decision-making.
That’s not to say that it obviates the role of human judgment completely, but it is to say that that balance, that harmony should and will look dramatically different two years, three years from now than it has for the last decade and before that.
The next big step-change that I see for this sector as a whole is evolving from a predictor of risk to an actual risk partner that can mitigate outcomes through the power of real-time insights.
The most obvious example of that is the role that sensors can play in providing real-time feedback to drivers of vehicles in a way that hopefully reduces risky driving and mitigates the likelihood of accidents. To me, that is the true power of data science in insurance.
Not only does it mitigate accidents from happening, or adverse events from happening, but reduces the cost of insurance and expands the reach of protection to a much broader population, both in the developed and developing worlds. To me, that’s a beautiful thing if you think about society having a much higher level of financial protection across every aspect of our lives.
Michael Li: Think about what’s new in data science, that is, why is data science different from or how does data science expand upon things like the actuarial tradition, like statisticians, the quants of yore.
We’re no longer just using structured data, so it’s not just SQL queries anymore. It’s now semi-structured and unstructured data. How do you start handling things when they don’t come in nice tables that you can load into Excel or that you can put into SQL?
We are also in a world where data is much larger. You mentioned telematics. If you took a reading from every car every second, that’s a lot of numbers you’ve got to store, and that’s a very different paradigm for computation. You have to think about storing this data. How do you deal with data now that it’s stored across multiple computers? How do you think about computation in that context?
Then, of course, the last thing is real-time data. Analytics has historically been — you might call it — a batch process. Run it once; generate a report; show it to people; you’re done.
Now it’s a continuous process. You run it; you have to instantly find the latest trends; put that into production so you can intelligently adapt; and then do it again the next hour, the next minute. That’s where competition is driving you.
If you look at what Silicon Valley has been doing, it is very much your server is constantly learning from user behavior and adjusts how it interacts with users in a way that — to borrow an expression — delights the user. I think that we see that.
Traditional companies, non-tech-based companies, have to emulate that kind of level of customer service and satisfaction. A lot of that comes down to big data and having a team capable of understanding new and different kinds of data in a world that’s rapidly evolving.
Murli Buluswar: If you think about the historical definition of transactional data in healthcare and banking, we know that that’s been at the core of how they think about analytics. Traditionally, insurance has not had that version.
If you zoom out and define data in a much broader sense that includes images, audio, all sorts of unstructured data: insurance then has its own version, layered on top with IoT and such. Insurance has its own version of transactional data. The ability to harness that and dramatically change the cycle time of decision-making, as well as the granularity of decision-making, is where the goldmine is for insurance in the coming five years or so.
Share your advice on building a data science culture?
Michael Li: Two basic, first steps. First, get the data, collect it, [and] store it, what have you. Second, find the talent that’s necessary to deal with the data, manipulate the data, and come up with actionable insights from that data. If you can do both of those things, you will at least take the first few steps in the direction of building a data-driven culture.
(Cross-posted @ ZDNet | Beyond IT Failure Blog)