The past few years have made clear that political organizations have become much more sophisticated in terms of leveraging “big data” and executing real-time, data-driven campaigns. In the same time period, life insurers have likewise been focused on big data and data-driven decision making, but they haven’t had the same level of success in extracting value. In fact, some life insurers might say, “While data may be the solution to some problems, in many cases data IS the problem.” Business leaders are asking, “Are our data capabilities better off than they were four years ago?”

Hope and Change
Traditionally, data has been considered technical in nature; hence, it has often become IT’s problem to figure out. In some cases, that has led to data becoming IT-centric. IT leaders are often heroic problem solvers, especially in the insurance industry. With today’s tools and robust data stores, IT can provide data in just about any form or frequency. But big data is not an IT responsibility. It is a business opportunity that should be led by business-unit leaders.

Ask Not What Your Data Architect/Scientist Can Do for You,
Ask What You Can Do for Your Data Architect/Scientist

At its core, a life insurer is basically a relationship and data organization. Just as senior leaders wouldn’t think of expecting the finance department to make branding decisions, asking IT to take sole responsibility for a strategic business asset such as data is just as risky – perhaps more so, because “big data” is much more than just data. It is data, analytical tools, and statistical techniques coupled with the technical skills to surface insightful correlations and patterns. And the holy grail of big data and analytics is the consumption of the data in various business context such as marketing (e.g. segmentation), underwriting (e.g. automated decisions), and claims (e.g. frequency and severity analysis). 

In order to reach this value-added consumption stage, it is paramount that business stakeholders and IT partners “tear down the walls” and put data discussions in a business-outcome context. Capitalizing on data in a specific business context or domain transforms it from data to insights, knowledge, and information for action. Business managers must lead that transformation process because they have the domain expertise and are ultimately accountable for making the critical business decisions. This combination of technical/science and management/art is the secret sauce around big data.

Read My Lips, No New Data Elements
One problem with data is a frequent focus on extremes. On one side, there can be too much of a good thing where data overload obscures information. In our experience working with hundreds of carriers, we often see data overkill. Insurance is a data-intensive business, and it’s all-too easy to “kitchen sink” data sourcing, focusing on quantity versus quality and potential. On the flip side, some insurers have a minimalist approach – relying on the same sparse data year after year, even as rich new data is readily available. The best practice is a “less is more” model: use data from diverse sources (e.g. multi-product information) that has a direct correlation on the desired business outcome, and ignore the rest. Recalibrate yearly, if not more often, based on the availability of new data sources and on observed effectiveness of the data being used. 

One source of impactful data that should figure into this regular recalibration is third-party data. Third-party data providers have a wealth of customer data (e.g. demographic and psychographic data) that can be appended to internal transactional data, enabling deeper analysis. A powerful combination I have witnessed was bringing together a carrier’s product ownership data with a financial advisor’s CRM data and appending third-party affluence indicators. This provided the financial advisor with an excellent “flag” that there was a likely gap between their estimate of a client’s net worth and a client’s actual affluence. This prompted the financial advisor to dig deeper and develop a more comprehensive picture of their client, resulting in more business for the carrier and a stronger relationship between the financial advisor and client.

The Only Thing We Have to Fear is Not Realizing the Value in Data
As innovators and new entrants continue to transform the life insurance industry and push into new business models, a lack of urgency and analytics-driven innovation by incumbents is perilous. Stagnant P&C companies, many of which have been acquired, are examples of that peril. Here are some actions which can be taken to improve the business impact of data within an organization:

  1. Data Quality: Ensure your in-house data is accurate and fresh. A good example of this is customer address data. Tremendous dollars are lost when materials or bills are sent to an incorrect address. Leverage address hygiene techniques and NCOA capabilities to keep your client address data fresh.
  2. Data Standardization: Create consistent data definitions and usage across the enterprise. You might be surprised how the definition of terms such as “household” or “production week” can vary within an organization. Is this happening in your company? 
  3. Data Literacy: Encourage and enable data/analytics literacy in your organization. The old adage that “you can’t manage what you don’t measure” becomes meaningless if you don’t understand what you are measuring.  
  4. Data Competency: Bring together enterprise-wide stakeholders. But you don’t have to jump right to a formal data/business intelligence center of excellence. Start with informal meetings with key business/IT partners where folks can start to share experiences and best practices.
  5. Data Thought Leadership: If you are using third-party data, revisit the relationship you have with your data provider. Are you in a transactional relationship or a strategic partnership? A good partner will bring their industry expertise to help you explore new and innovative ways to use your data assets to create value for your customers.
  6. Data Innovation: Consider the data/analytics innovation you experience in other industries and with your own trading partners. How might that be relevant in the context of life insurance?

No matter where an organization is on the maturity curve of data and analytics proficiency, there are opportunities for improvement. I welcome your questions and suggestions for making data and analytics even more impactful. Please drop me a line at