Big Data, Analytics, and the “Analytical Broker”

Timothy L. Coomer, Ph.D.

Imagine that you are sitting across from a risk manager during a sales presentation. The company is a fast growing manufacturing firm and would be the largest new account for your agency this year. You’ve covered your firm’s capabilities and are optimistic about the account’s acquisition. Then, the risk manager throws a potential wrench into the discussion:

“My CFO is making a push to make sure all departments are using analytics to improve decision making. I am meeting with you because I’ve been told to hire an ‘analytical broker.’ Now, between you and me, I am not sure what ‘analytical broker’ means. Give me something to take back to the CFO to explain what makes you an ‘analytical broker’ and the account is yours….”

Most of my blogs in 2015 will be focused on answering the following question, “what is an analytical broker?” I hope you will follow along through the year and email me directly with your thoughts and feedback.

Analytical Prowess
During 2014, I had the pleasure of being a student in Oklahoma State University’s executive PhD program. This was the first of a three-year program and a time of intense learning and exploring. I spent some time studying and writing about the “Big Data” phenomenon and, more importantly, the role of analytics in business. This series of blogs is designed to help you prepare for this new era of business competition – where analytical prowess will be the differentiator. Big Data

Now, you may be wondering what I mean by that. Analytical prowess is a term I began using while I was researching, writing, and speaking about analytics last year. It best summarizes the variety of abilities, systems, and processes that must all work together to create a business that is analytically based and data driven. I was able to study the topic of analytics from multiple industry perspectives and from an academic perspective. However, for this year’s series of blogs, I will focus on what it means to be an analytical broker. This term, analytical broker, is beginning to show up more frequently in articles and white papers and will define what it takes to be a successful producer in the years ahead.

Defining Big Data

In the arena of analytics, the most popular buzz word of late has been “big data.” The generally accepted definition of big data is “data sets so large and complex that it becomes difficult to process them using traditional data processing applications.” Of course, this says nothing about how you are going to analyze the data and extract useful insights. Big data is simply a massive quantity of data that most businesses are not equipped to process. When thinking about analytics, this is just one of the many challenges that must be overcome. Analytics can be applied to big data, but it can also be applied to small data – and most of what the analytical broker needs to address is in the arena of small data. I define small data as data sets that can be analyzed and interpreted using fairly easy-to-use tools and processes. Typically, big data is more of a challenge for insurance carriers than it is for brokers, because carriers must attempt to produce predictive and prescriptive models from their big datasets.

Big data as a colloquialism has been quickly popularized due to a “perfect storm” of technological changes during the past decade. The cost of data storage, for one, is less than 1/100th of what it was just ten years ago. Additionally, the number of Internet users has increased by a factor of 10 over the same time period, and the number of connected devices, known as the Internet of Things (IoT), is growing so fast it is hard to find reasonable estimates. Social networking has skyrocketed with 500 million tweets daily, and about 120 billion emails from 4 billion email accounts. One doesn’t need a very robust analysis to see that all of these volume numbers are increasing rapidly. This swift rise in available data is what defines the big data challenge and a great deal of amazing insight will emerge from its analysis. Some brokerage firms will tackle big data projects, but plenty of opportunity exists outside of the big data world, which is where the brokers we serve need to focus for now.


Analytics is the process of transforming raw data into insightful decisions. By far, “insightful decisions” is the key part of that definition. If you use analytical processes to summarize claims data, but gain no new insight or assistance in making better decisions, then this does not meet the definition of analytics. The analytical process results in improved probabilities of correct decisions and decreases in variance from desired or predicted outcomes. In other words, the end result should be that you are right more often. Also, if and when mistakes are made, they are defined by narrower margins of error.

As an analytical broker, you are helping large clients make important decisions and interpret data to explain phenomena. This is where you can deliver the most value and, naturally, is the answer to our risk manager’s query in the introduction.

The Analytical Broker

So, we have identified what big data is and is not, and we can agree that crunching numbers does not necessarily equate to analytics unless it supports better decision-making. Now, we need to explore what an analytical broker looks like.

An analytical broker approaches every decision his client must make by viewing it through an analytical lens. In doing so, he must start with these questions:

  1. What are we trying to accomplish with this decision or, in other words, what is the question we are trying to answer?
  2. What data is available to support this decision analysis?
  3. Are we willing to pay the cost for this analysis (whether internal or external)?
  4. How complex, broad, and accurate does this analysis need to be?
  5. Is this feasible given our available resources?
  6. Can we put together a team that includes an analytical person and a business (subject matter expert) person?
  7. How do we obtain and “scrub” the data so we start with a clean data set?
  8. Who is best equipped to complete the needed analyses?
  9. What is the time fame within which we need to make a decision?

In years past, decision-making was driven by wisdom and intuition. However, recent studies have shown that data driven decisions are statistically better decisions. However, wisdom and intuition continue to add value at the beginning of an analytical decision making process when the previously mentioned questions are being formed. Knowing what you are trying to accomplish – what is important and what is not important – is the foundation to a successful analytical decision making effort.

The combination of wisdom, intuition, and analytically based and data driven decision-making will lead to a sustainable competitive advantage for you and your clients. So, here is a definition:

Analytical broker: noun. A risk facilitator that utilizes data and analytical techniques to assist clients in selecting loss financing mechanisms that minimize the total cost of risk.

Now that we have a general definition for an “analytical broker”, I’m looking forward to exploring exactly what analytical brokers do throughout the year. Next month, we will look at enterprise risk management and the framework it provides to support analytics.


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© 2015 SIGMA Actuarial Consulting Group, Inc.

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