Analytics investments: why cool isn’t enough

It's really about value and execution

During a recent industry briefing call, the Chief Analytics Officer of a life insurance carrier asked me an interesting question: “Should I try to get funding support for our new analytics initiative on the basis that it’s cool and sexy?”

When I prompted her about what’s sexy and cool about her proposal, she replied, “Well, machine learning is sexy, because especially in our industry, it’s the new hot thing. And it’s cool because I think we can improve how we make underwriting decisions on new policies.”

Then, I followed up with an important clarifying question, ”Do you have the technology support to implement that model, if you did indeed find a better underwriting mousetrap?” She had to admit she didn’t yet know how or whether she could implement such a new model in their production systems, which is maintained by the technology organization. She also didn’t know the best way to work with the underwriters to adopt the pricing policy changes that her upgraded model would support.

Why cool isn’t enough

Here’s why I have found cool doesn’t win the day: In many financial services firms, few executives want to be a guinea pig for an investment that is novel in their industry, especially in fiscally and operationally conservative environments like the life insurance industry. Cool simply isn’t enough.

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Data might be cool, but decisions are sexy!

Decisions live in the domain of courage and enduring values

At the gym last week, I saw someone wearing a company-branded t-shirt proclaiming “Data is cool”.

I had a lot of reactions to that statement, not the least of which was the subject-verb disagreement (since every Latin major knows “data” is plural, but let’s get past that). This might sound strange coming from an analytics professional, but no, in my opinion, data are not cool.

To many people entering the domain of data science, data might be novel and mysterious, but really, by itself, data are the measurement of a process. Measurement is only academic unless it’s informing a decision, so by itself, data is not actually cool.

What is cool? Our ability to capture data at ever-greater frequency, latency, and accuracy, and how it allows us to make decisions faster, more efficiently, and at a more fine-grained level—which produces stronger outcomes. When those decisions allocate resources towards strategies that maximize our chances to produce wins… that’s pretty cool.

It’s fundamentally the role of leaders to make decisions that allocate resources towards winning strategies. That role as the decision-maker is hard, risky, requires persistence and faith, and is the domain of courage and enduring values. When people make decisions about allocating resources towards those outcomes producing the best results, for as many people as possible, and reducing the pain of loss as much as possible, while still being efficient and utility-maximizing… now that’s more than cool, it’s sexy.

So there really ought to be a t-shirt that proclaims the core truth: “Data might be cool. But decisions are sexy!”

Financial services CDO feedback on open source analytics deployment

Are open source analytics ready for production?

When I attend industry conferences or speak with Chief Data Officers (CDOs) and Chief Analytics Officers (CAOs) of large financial institutions, one popular question that arises is, “What do you hear about open source analytics in other large banks? Is it ready for production?”

While I’ve encountered substantial growth of exploration and analytics development occurring in private and public clouds using open source analytics, I’ve also been a little surprised (in two ways) at the findings of these CDOs and CAOs when it comes to actually deploying analytics assets using open source.

First source of surprise: large financial institutions with whom I’ve spoken are getting nasty wake-up calls about failures of their selected open source platforms to provide robust, reliable results. The initial attraction was the price of open source tools; the subsequent feedback is a broader appreciation for total cost of ownership, which isn’t as attractive as they first anticipated.

Second source of surprise: that collectively, we didn’t see this coming.

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Corios Harmony next-best offers, delivered in the real world

Omnichannel customer monitoring and offer delivery via SAS and Salesforce

These days, customer acquisition costs are through the roof – and it’s become more important than ever to understand buyer behavior and target promotions effectively. To be truly effective, information about your next best offer campaigns should come from more than just traditional product and market data.

In order to maximize the effectiveness of contact strategies, we think our clients should deploy a data-driven approach, leverage predictive analytics, and attack the problem from an omni-channel perspective – allowing them to present the right offer to the right buyer at the right time.

The Corios Harmony solution delivers our Marketing Analytics Platform, or MAP. MAP is a closed loop system, recording interactions with customers across all touchpoints, for marketing and sales, to deliver the appropriate message, and to monitor the customer’s own interactions with us. In short, it’s a learning and predictive platform to help you present the right offer to the right customer at the right time through the right channel.

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Three implications of mathematics on the World Series

Sabermetrics provides useful advice for modern data science

Many of my friends who live in the American Midwest are captivated by the Major League Baseball World Series, not only for the drama of two teams with a rich history and many years of waiting for this opportunity, but also because it provides a welcome respite from news about our presidential election. While the Oakland Athletics, the baseball team for whom I root, will be waiting for a while before our next World Series appearance, I’ll share my enthusiasm for the Indians and the Cubs fans, and one of my favorite sports.

I remember, as a college student, being enthralled with reading the box scores every morning in the San Francisco Chronicle, watching the ERA of the pitching squad, and monitoring the amazing batting average of Carney Lansford, the A’s third baseman at the time. This experience of reading the box scores, and how they changed day to day, influenced the way I watched the game. I was thrilled when Michael Lewis published his book “Moneyball” in 2003, focused on the emerging role of analytics and how it shaped the strategy for the very same Oakland A’s that I loved and rooted for.

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