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data symmetry

Competing in an Age of Data Symmetry

For centuries, people have lived in a world where data was largely proprietary, creating asymmetry. Some had it. Others did not. Information was a currency. Some organizations held it, and profited from it. We are now entering an era of tremendous data balance — a period of data symmetry that will rewrite how companies differentiate themselves.

The factors that move the world toward data symmetry are time, markets, investment and disruption.

Consider maps and the data they contained. Not long ago, paper maps, travel books and documentaries offered the very best views of geographic locations. Today, Google allows us to cruise nearly any street in America and get a 360° view of homes, businesses and scenery. Electronic devices guide us along the roadways and calculate our ETA. A long-established map company such as Rand McNally now has to compete with GPS up-and-comers, selling “simple apps” with the same information. They all have access to the same data. When it comes to the symmetry of geographic data, the Earth is once again flat.

Data symmetry is rewriting business rules across industries and markets every day. Insurance is just one industry where it is on the rise. For insurers to overcome the new equality of data access, they will need to understand both how data is becoming symmetrical and how they can re-envision their uniqueness in the market.

It will be helpful to first understand how data is moving from asymmetrical to symmetrical.

Let’s use claims as an example. Until now, the insurer’s best claims data was found in its own stockpile of claims history and demographics. An insurer that was adept at managing this data and applied actuarial science would find itself in a better position to assess risk. Competitively, it could rise to the top of the pack by pricing appropriately and acquiring appropriately.

Today, all of that information is still very relevant. However, in the absence of that information, an insurer could also rely upon a flood of data streams coming from other sources. Risk assessment is no longer confined to historical data, nor is it confined to answers to questions and personal reports. Risk data can be found in areas as simple as cell phone location data — an example of digital exhaust.

Digital exhaust as a source of symmetry

Digital exhaust is the data trail that all of us leave on the digital landscape. Recently, the New York City Housing Authority wished to determine if the “named” renter was the one actually living in a rent-controlled apartment. A search of cell phone tower location records, cross-referenced to a renter’s information, was able to establish the validity of renter occupation. That is just one example of digital exhaust data being used as a verification tool.

Another example can be found in Google’s Waze app. Because I use Waze, Google now holds my complete driving history — a telematics treasure trove of locations, habits, schedules and preferences. The permissions language allows Waze to access my calendars and contacts. With all of this, in conjunction with other Google data sets, Google can create a fairly complete picture of me. This, too, is digital exhaust. As auto insurers are proving each day, cell phone data may be more informative to proper pricing than previous claims history. How long is it until auto insurers begin to look at location risk, such as too much time spent in a bar or frequent driving through high-crime ZIP codes? If ZIP codes matter for where a car is parked each night, why wouldn’t they matter for where it spends the day?

Data aggregators as a source of symmetry

In addition to digital exhaust, data aggregators and scoring are also flattening the market and bringing data symmetry to markets. Mortgage lenders are a good example from outside the industry. Most mortgage lenders pay far more attention to comprehensive credit scores than an individual’s performance within their own lending operation. The outside data matters more than the inside data, because the outside data gives a more complete picture of the risk, compiled from a greater number of sources.

Within insurance, we can find a dozen or more ways that data acquisition, consolidation and scoring is bringing data symmetry to the industry. Quest Diagnostics supplies scored medical histories and pharmaceutical data to life insurers — any of whom wish to pay for it. RMS, AIR Worldwide, EQECAT and others turn meteorological and geographical data into shared risk models for P&C insurers.

That kind of data transformation can happen in nearly any stream of data. Motor vehicle records are scored by several agencies. Health data streams could also be scored for life and health insurers. Combined scores could be automatically evaluated and placed into overall scores. Insurers could simply dial up or dial down their acceptance based on their risk tolerance and pricing. Data doesn’t seem to stay hidden. It has value. It wants to be collected, sold and used.

Consider all the data sources I will soon be able to tap into without asking any questions. (This assumes I have permissions, and barring changes in regulation.)

  • Real-time driving behavior.
  • Travel information.
  • Retail purchases and preferences.
  • Mobile statistics.
  • Exercise or motion metrics.
  • Household or company (internal) data coming from connected devices.
  • Household or company (external) data coming from geographic databases.

These data doors, once opened, will be opened for all. They are opening on personal lines first, but they will open on commercial lines, as well.

Now that we have established that data symmetry is real, and we see how it will place pressure upon insurers, it makes sense to look at how insurers will use data and other devices to differentiate themselves. In Part 2 of this blog, we’ll look at how this shift in data symmetry is forcing insurers to ask new questions. Are there ways they can expand their use of current data? Are there additional data streams that may be untapped? What does the organization have or do that is unique? The goal is for insurers to innovate around areas of differentiation. This will help them rise above the symmetry, embracing data’s availability to re-envision their uniqueness.

Analytics at the Next Level: Transformation Is in Sight

Although insurance companies are embracing analytics in many forms to a much higher degree than other businesses, adoption by the insurance industry is still only in its adolescent stage. Deployment is broad but inconsistent. The use of analytics may be about to mature considerably, though, based on a recent series of mergers and acquisitions.

Currently, while a majority of large carriers use predictive modeling in one of more lines of business, and mostly in personal lines auto, a smaller percentage use it in their commercial auto and property units. Insurers recognize predictive analytics as a critical tool for improving top-line growth and profitability while managing risk and improving operational efficiency. Insurers believe predictive analytics can create competitive advantage and increase market share.

Fueling even greater excitement – and soon to be driving transformational innovation – is the recent surge of M&A activity by both new and nontraditional players, which have combined risk management and sophisticated analytics expertise with robust and diverse industry database services. The list of recent deals includes:

  • CoreLogic’s 2014 purchase of catastrophe modeling firm Eqecat, following its 2013 acquisition of property data provider Marshall & Swift/Boeckh; a significant minority interest in Symbility, provider of cloud-based and smartphone/tablet-enabled property claims technology for the property and casualty insurance industry; and the credit and flood services units of DataQuick.
  • Statutory and public data provider SNL Insurance’s 2014 purchase of business intelligence and analytics firm iPartners, which serves P&C and life companies.
  • Verisk Analytics’ 2014 acquisition of EagleView Technology, a digital aerial property imaging and measurement solution.
  • LexisNexis Risk Solutions’ 2013 acquisition of Mapflow, a geographic risk assessment technology company with solutions that complement the data, advanced analytics, supercomputing platform and linking capabilities offered by LexisNexis.

Other 2013/2014 transactions that have broad implications for the insurance analytics and information technology ecosystem include:

  • Guidewire Software, a provider of core management system software and related products for property and casualty insurers, acquired Millbrook, a provider of data management and business intelligence and analytic solutions for P&C insurers.
  • IHS, a global leader in critical information and analytics, acquired automotive information database provider R.L. Polk, which owns the vehicle history report provider Carfax. 
  • FICO, a leading provider of analytics and decision management technology, acquired Infoglide Software, a provider of entity resolution and social network analysis solutions used primarily to improve fraud detection, security and compliance.
  • CCC Information Services, a database, software, analytics and solutions provider to the auto insurance claims and collision repair markets, acquired Auto Injury Solutions, a provider of auto injury medical review solutions. This transaction follows CCC’s acquisition of Injury Sciences, which provides insurance carriers with scientifically based analytic tools to help identify fraudulent and exaggerated injury claims associated with automobile accidents.
  • Mitchell International, a provider of technology, connectivity and information solutions to the P&C claims and collision repair industries, plans to acquire Fairpay Solutions, which provides workers’ compensation, liability and auto-cost-containment and payment-integrity services. Fairpay will expand Mitchell’s solution suite of bill review and out-of-network negotiation services and complements its acquisition of National Health Quest in 2012.

Based on these acquisitions and the other trends driving the use of analytics, it will be increasingly possible to:

  • Integrate cloud services, M2M, data mining and analytics to create the ultimate insurance enterprise platform.
  • Identify profitable customers, measure satisfaction and loyalty and drive cross/up-sell programs.
  • Capitalize on emerging technologies to improve pool optimization, create dynamic pricing models and reduce loss and claims payout.
  • Encourage “management by analytics” to overcome departmental or product-specific views of customers, update legacy systems and reduce operating spending over the enterprise.
  • Explore external data sources to better understand customer risk, pricing, attrition and opportunities for exploring emerging markets.                       

As the industry is beginning to understand, the breadth of proven analytics applications and the seemingly unlimited potential to identify even more, coupled with related M&A market activity that will drive transformational innovation, indicates that the growing interest in analytics will be well-rewarded. Those that are paying the most attention will become market leaders.

Stephen will be Chairing Analytics for Insurance USA, Chicago, March 19-20, 2014.

Is M&A in Data and Analytics Setting a Path for Innovation?

The trend of acquisitions of software and data providers is continuing, but with a twist that may lay the foundation for innovation in the insurance industry. 

CoreLogic closed out 2013 with a bang by announcing its acquisition of Eqecat, a catastrophe modeling firm, on Dec. 20, adding to an already impressive list of acquisitions in the past year. CoreLogic added three real estate companies from TPG Capital Decision Insight Information Group–Marshall & Swift/Boeckh, DataQuick Information Systems and DataQuick Lender Solutions (credit and flood-services units)–further extending into the insurance data and analytics space.  

On Jan. 13, 2014, SNL Insurance, which provides a range of statutory data for insurers that links with public data, announced the acquisition of iPartners LLC, a SaaS business intelligence and analytics solution for both the property and casualty and life and annuity insurance industries. SNL says the acquisition will provide its clients a robust BI and reporting tool for operational needs.  

And Verisk Analytics, a supplier of data to insurers and banks, announced on Jan. 14 the acquisition of EagleView Technology, a provider of property images for nearly 90% of all U.S. structures. The images, based on digital aerial image capture, are analyzed to provide information to estimate property size and proximity to risks to assist in underwriting and claims assessments.

While the initial reaction might be to think of these as just another series of acquisitions, these actually point to the great possibility for change in how we access and use data in the insurance industry, a real mash-up of ideas and technology. As an industry, we are intensely dependent on data. But the data we use is fragmented across multiple organizations, accessed and paid for based on 10- to 20-year-old business/pricing models, requires significant integration and is often ineffectively used because of a lack of analytic capabilities. But these acquisitions have the potential to change this landscape.

These acquisitions and others are positioning the industry to be ready to move beyond long-held traditional offerings into “pay-by-use models” for both software (SaaS) and data (DaaS). Each offers the deepening analytics capabilities and expertise that can be used to analyze and create data from all the source data acquired, offering new data points for insurance business processes. Together, these create the opportunity to completely change the business model for data providers. This will enable the provision of new pricing, ease of access and new data based on analysis for many insurers, particularly mid-sized to small insurers, that may not have the expertise, resources or technology to do this by themselves.  

As new models emerge, the implications and the opportunities will be substantial. There could be a new wave of innovation for insurance products, business processes, markets, competition and business models. The playing field could be leveled for access to traditional and new data and information for insurers of all sizes and even for new entrants to the industry.

Who will take the first step forward in creating and offering a new model? How quickly will the innovator bring value and potential to the industry? If the traditional providers don’t, then companies outside our industry who see the strategic importance of data, cloud and new models will. Does Google come to mind?