6 min read

Data Strategy in Middle & Large Market Commercial Insurance

Data Strategy in Middle & Large Market Commercial Insurance

A massive transformation is happening within the insurance world, and brokers feel the pressure. Increased consumer demand is pushing out the status quo, mergers and acquisitions are bolstering the competition’s competencies, and new entrants are crowding an already congested space.

Keeping pace meant making regularly scheduled updates to internal systems and software. Unfortunately, this is no longer the case as the pace of technology has accelerated past the monolithic, siloed systems the industry has relied upon for more than two decades. The legacy these outdated programs have left is mountains of incomplete, non-uniform, and stagnant data. 

Today’s broker will move only as fast as its data. Therefore, organizations must adopt innovation as a business function where data quality, uniformity, and accessibility form the foundation. Once the groundwork is laid, digital transformation can take place, and brokers will be armed with the tools necessary to lead in the next era of insurance.  

To showcase how embracing innovation can accelerate broker growth, we share a real-world success story of a broker using data-driven insights to their advantage. But, before we get there, we first must understand where the state of data in our industry is today. 



The Sorry State of Data Quality

A study involving 75 executives found that only 3% of their departments fell within the minimum acceptable range of 97% data accuracy. In other words, nearly every department had at least four incorrect data records out of 100. 


Data Quality 1


An accuracy rate of 97% may seem extreme. However, correcting data errors is not only time-consuming, but they are also exceptionally expensive. For a matter of perspective, let’s consider “The Rule of 10,” which has been applied to any linear application from software design to assembly lines.

The concept asserts that the cost to complete a unit of work is 10x’s higher when the input is defective than if the input were perfect. Using a machine manufacturer as an example, the rule of 10 looks something like this:

Level of Completion

Cost to Repair Defect

The part itself


At Sub-Assembly

10 X

At Final Assembly

100 X

At the Customer

1000 X


The longer it takes to discover an error, the more costly it becomes. In the example above, a $1 part would cost the manufacturer $100 to find and repair during final assembly. We have seen this play out with automobile makers where a cheap, defective part costs billions in a recall. 

Like an assembly line, the underwriting process is also a linear application. However, instead of parts, we are working with data where an error is paid for with time and energy. Time and energy better served for much more profitable endeavors.

How does your data stack up?

Fortunately, there is a simple exercise you can do to gain a glimpse of the quality of your brokerage’s data. Pull the last 100 bind orders and identify how many records are error-free using the following steps:


Data Quality 2


  • Step #1: Identify the data attributes required for a “complete” submission.
  • Step #2: Review each record and data point to determine if data attributes were completed perfectly or whether data bits were missing during submission. 
  • Step #3: Sum the number of missing or incorrect data bits and divide that number by the total number of data attributes reviewed. 


The above is a quick way to determine how often minor data inconsistencies are leaking into your everyday processes and records. The results provide a quantifiable figure to use in prioritizing where data quality needs to be improved.



Up to the Challenge

Most brokers have realized the need to innovate. This was the case with one of our broker partners who wanted to understand how to extract value from their most important assets - their data. 

The broker was feeling pressure from the competition where they were losing ground on their speed from submission, to quote, to bind. The challenge required a wide-reaching approach to data policy encompassing data siloed between different systems, clients, partners, and markets. There was a seemingly infinite amount of value to be gained from their data, and the broker set forth their goals in working with Highwing:


  • Improve data quality: The broker realized before value could be extracted from their data, their database would need to reach optimal accuracy. 
  • Develop data uniformity: Once data accuracy was achieved, new data policies would be required to eliminate future errors, and format data enabled for integrations with best-in-class tools. 
  • Electronic submissions: With accurate and uniform data, underwriting-ready electronic submissions could be built to lower costs, improve client satisfaction, and grow their book of business faster than the need to increase headcount. 


This broker, like many others, was already busy managing relationships with clients and partners, underscored by overseeing the processes keeping the company going. The amount of expendable time for a data quality project was virtually non-existent. For our partnership to succeed, we knew their digital transformation would need to be simple, measurable, and exceptionally impactful.



Taking the First Step

A journey of a thousand miles begins with the first step. In the case of digital transformation, the first entails an analysis of the state of data within your brokerage. At Highwing, our process involves an in-depth evaluation culminating in a comprehensive data quality report.

Our assessment aims to identify areas within the brokerage ripe for improvement. Hard numbers are crunched, proficiencies are determined, and data gaps are revealed. But for simplicity, we want to ascertain if a broker maintains a novice, competent, or expert level of data competency.

Ask yourself, “what is the data state of my brokerage?” using the following descriptors:


Novice Data States – Brokerages considered to be in the Novice Data State would describe their operation as follows:

  • Our data is incomplete, unreliable, and hard to access.
  • The path to packaging insights for our customers is a tedious process.
  • We do not maintain any streamlined connections between ourselves and our carrier partners. 

Competent Data States – Brokerages considered to be at the Competent Data State would describe their operation as follows:

  • We have made strides in cleaning our data.
  • We regularly evaluate our data for cross-selling opportunities. 
  • We employ active and accurate data feeds reflecting major business activities.
  • We have a series of turnkey reports enabling our team to better prepare for the renewal cycle. 

Expert Data States – Brokerages considered to be at the Expert Data State would describe their operation as follows:

  • We are a fully empowered, insight-driven organization.
  • We have eliminated the need to re-key data at all levels of the organization.
  • Our teams monitor data activity daily, weekly, monthly, and annually. 
  • Growth is 100% insight-driven, with data accessible to authorized associates. 
  • We fully connect between our internal and third-party carriers’ systems. 
  • We translate data signals into actional insights resulting in positive outcomes.


Reaching the Expert Data State takes a commitment from all company levels. Most important is that executives set the standard for creating a culture of data integrity. As we learned, even a tiny creep of faulty or incomplete data can be costly. 


However, ask yourself what your brokerage could gain by operating in an “Expert Data State”:

  • How would your client’s satisfaction level change?
  • How would your team’s job satisfaction level shift?
  • How would your brokerage better gauge performance? 
  • How much stronger would carrier relationships become? 
  • How might your value proposition change for your clients?
  • What type of time and monetary savings could you realize?
  • What would your team aspire to achieve in an Expert Data State?


The answers to these questions should excite you. Digital transformation touches every aspect of a broker’s organization, and when done correctly, the results can make how you used to do business unrecognizable.  



Achieving the Expert Data State

Determining your data state can be a very humbling process. Yet, so many challenges standing in the way of reducing friction between brokers, carriers, and clients lie within your data. These hurdles are why analyzing your data and creating a data quality report is the most intensive and vital step within the digital transformation process.

Our partner broker, mentioned above, was entering a competent data state and realized how much further they had to go. Producing a data quality report was our first step. For every data point, regardless of its significance, we computed the broker’s completeness and conformance across the entire organization. Additionally, we included quality metrics that vary per field based on the semantics of the data model.

Once reporting was completed, we provided the roadmap to normalize and enrich their data. The result was a complete, malleable, and agile dataset enabling the broker to access many new analytics and insights. Data became deployable instantly, and their internal processing speeds increased exponentially.

Just the ability to reign in their data and use it efficiently and meaningfully is a game-changer. Yet, the most significant advancement is the broker’s new ability to integrate and automate many of the broker’s most complex and time-consuming processes. The Highwing platform is now allowing our partner broker to:

  • Obtain carrier appetite data in real time.
  • Eliminate the rekeying of data among multiple carriers.
  • Reduce the time needed to create complete and error-free submissions. 
  • Simultaneously and electronically send multiple submissions into the market.
  • Access underwriter submission notes and commentary in a central location.
  • Automatically identify coverage gaps and cross-sell opportunities. 
  • Obtain quotations and declinations from markets faster. 
  • Bind coverage in a fraction of the time. 


The measurable benefits for our partner broker have been more than impressive. But perhaps just as notable are the deepening their relationships have become with both their carriers and clients.



Your Digital Transformation is Within Reach

Data, both internal and accessible through your partners, is your brokerage’s most valuable asset. But incomplete information living in monolithic, siloed systems prevents brokers from unleashing its true value and power. Any meaningful innovation is handcuffed until data becomes complete, normalized, and easily accessible.

The foundation of Highwing’s marketing platform is built upon decades of examining insurance data and analyzing all its potential. Data is the key to unlocking the speed required to thrive in the industry’s next era of digital. 


Contact us today to learn more about how Highwing can use data to power speed and lead digital collaboration between brokers and carriers.

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