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Stuart Rose, <a href=SAS" />
Stuart Rose, SAS
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Signal to Noise: Nate Silver Offers Lesson on Controlling Big Data Complexity

Simply adding more variables can amplify meaningless feedback and add complexity. Profitable use of big data for improved pricing accuracy and enhanced customer experience will require isolating predictive factors.

The Internet and social media have created a new explosion of data, making most of us wonder: Is big data a friend or foe? Is all this new information helping us make better decisions — or is it distracting us from reaching our goals?

Stuart Rose
Stuart Rose, SAS

This theory was raised by Nate Silver, who recently keynoted at the SAS Financial Services Summit in Cary, N.C. In Silver’s book The Signal and the Noise, he discusses that despite all the available information and data, many predictions still fail. Since insurance is built on forecasting, his ideas bear on the challenge of increasing the accuracy of our predictions.

The Relative Complexity of Signals and Noise

To increase pricing accuracy and be more competitive, the philosophy of many insurance companies has been to add more variables to their rating models. However, according to Silver, this can cause additional and potentially unnecessary complexity — what he refers to as “noise.” Adding a new variable increases the number of data points and relationships, exponentially. For example, in a very simplistic model, if you are testing for relationships between any five variables, there are 10 two-way tests to run, shown in the equation: (5x4)/2 = 10. If you double the number of variables to 10, you more than quadruple the number of relationships to test, shown by: (10x9)/2 = 45. With that thought in mind, consider the complexity of the Federal Reserve website that tracks 61,000 statistics in real time, creating a potential 1.86 billion relationships to analyze. That's big data analytics! The problem is that many of those relationships may be redundant or trivial, and hidden among them are the “real nuggets” or “signals.”

[For more of Stuart Rose's industry insights, see Distribution Insight – Analyzing Agency Performance and Profitability.]

Fortunately, unlike the Federal Reserve, insurance companies are not analyzing more than 60,000 data points. However, new data-intensive insurance products, such as telematics, will dramatically increase the variables collected. To analyze this new information, insurance companies will need high-performance analytics to determine which variables are predictive and which are not.

Silver ended with an interesting thought: As big data becomes more prevalent in our lives, a greater emphasis should be made on teaching probability and statistics in school. Fantasy sports leagues should be mandatory for all school children!

Aligning Technology and Business

Another interesting presentation at the executive summit included a panel discussion about the ongoing challenge of aligning technology and business, even more demanding in the age of big data and analytics. James Miller, VP of Enterprise Decision Support at USAA, discussed three factors that are helping better align business and technology within his organization. First, executive buy-in is important; luckily the USAA CEO is a big fan of data and analytics. Second, agile methodology helps with processes even if you have business folks sitting with IT to work iteratively via question and answer. Third, member experience must be the central focus, meaning everything needs to be about aligning with the USAA core competency of knowing its members better than anyone else.

Customer Experience

A concluding panel had executives from the largest bank and insurance company in North America and focused on data-driven customer experience. Ginger Hlebasko, Marketing and Sales Analytics Executive at State Farm, discussed that while insurance is about the relationship between the carrier, agent and customer, State Farm recognizes that success is about the customer experience. To that effect, it is creating a seamless integration between all channels, using data and analytics to predict what services are wanted online or in person. Kathryn Black, Customer Offers and Targeting Executive, Global Marketing and Corporate Affairs at Bank of America, discussed how data and understanding customer sentiment is extremely important. As Bank of America re-launches its brand, it is monitoring feedback through various channels (including social media) and making changes in real time.

While having big data does not necessarily translate into success, organizations need to learn how to befriend it since the amount will continue to escalate. Recognizing the signal from the noise will help insurance companies prosper through improved pricing accuracy and enhanced customer experience.

About the Author: Stuart Rose is global insurance marketing manager at Cary, N.C.-based SAS. Rose, a 20-year veteran of the insurance industry, began his career as an actuary. He has worked for a global insurance carrier in both its life and property divisions and has worked for several software vendors, where he was responsible for marketing, product management and application development. He has driven successful development and implementation of enterprise systems with insurance companies in the U.S., the U.K., South Africa and Continental Europe.

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Nathan Golia
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Nathan Golia,
User Rank: Author
5/2/2013 | 8:00:01 PM
re: Signal to Noise: Nate Silver Offers Lesson on Controlling Big Data Complexity
Very interesting, Stuart. It's interesting how the "big data" trend is being discussed outside the context of b-to-b tech media. I wonder how much of that is attributable to Silver's fame during the last presidential election.
Phil Simon
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Phil Simon,
User Rank: Apprentice
5/1/2013 | 4:36:03 PM
re: Signal to Noise: Nate Silver Offers Lesson on Controlling Big Data Complexity
Nice piece. I agree. In Too Big to Ignore, I write about how car insurance is about to undergo a transformation. Old pricing models based upon five or six variables are going by the wayside. Big Data is being used to create more accurate policies with respect to risk.
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