Effectively optimizing data in insurance

Insurance has always been a data-driven business, from ancient waybills recorded on papyrus under the Code of Hammurabi, to the charters of the first property insurers after the Great Fire of London. What has changed in today’s insurance world is the velocity, volume, and variety at which data arrives and accumulates. Insurers committed to becoming truly data-driven enterprise have realized that nothing less than their future depends upon it.

Data modernization has been at the top of most insurers’ corporate priorities for several years. Macro trends such as rising customer experience expectations, demographic shifts in potential insurance buyers, the disruptive rise of insurtech, and an emphasis on digital transformation have converged to increase the pressure on the insurance industry to up its data game. To that end, many insurers have been investing heavily in new data architectures, models, platforms, and the talent required to implement and manage modernization efforts. So, why does the industry still seem to be behind the curve when it comes to creating true data-driven enterprises?

Drowning in Data
One of the core issues insurers continue to face is the rapid proliferation of data. Every activity of the insurance value chain – marketing, sales, policy and claims management, actuarial, and financials – creates data, and lots of it. This is especially true for legacy insurers that have accumulated massive amounts of data spanning decades or even centuries of activities. Finding the right data in the right place and at the right time, and presenting it in a consumable format, can be a daunting task for many insurer IT divisions. However, the lost opportunity costs for failing to leverage these untapped data assets far outweigh the effort and expense involved in creating an effective data management program.

As decades of experience creating and implementing data strategies has demonstrated, the first steps on the road to data modernization can be difficult. For many insurers, this first step can have less to do with the actual data than it does with identifying the strategic use cases required to demonstrate the value of a sound data management program. Insurers must decide what information is the most critical to ongoing and future strategic goals and business operations, even if the data doesn’t exist in their current data stores. This can be difficult for insurers that over-value existing data and processes. It’s important to employ structured thinking combined with practical data modernization experience – internal and external – to envision a desired future where the strategic use of data supports strategic planning, proactive decision-making, and efficient and effective business processing. The end result is a roadmap to guide an insurer toward market success and profitability.

Converting Data to Opportunity
The whole point of becoming a data-driven enterprise is to for data to generate value for an insurer and its key stakeholders. For customers, that value means better product choices and a better customer experience across touchpoints like marketing, purchasing, claims, and billing. For agents, that value means better product training, underwriting, and relationship management. And for the insurer, that value means better customer and agent insights, market and financial forecasting, and strategic and operational performance. 

More specifically, making the data-driven transformation requires an enterprise-level commitment to the priorities, investments, and resources necessary for success. It’s a business-driven effort that uses technologies and platforms to create the foundation for data modernization. It’s a structured and cohesive effort as opposed to siloed solutions and fragmented goals. Insurers who have had success with their data modernization programs have employed an iterative approach that uses proof-of-concept projects to test new ideas and approaches and scale, if successful.  

The insurers transitioning to a data-driven enterprise have created architectures and analytic platforms that leverage vast amounts of existing unstructured data. They recognize that the new data analysis gold mines will be comprised of videos, social media posts, photos, presentations, and voice recordings, and that insurers will need to have the tools and talents required to turn unstructured data into meaningful information for business decision making. 

The Future Began Yesterday
The race to become a data-driven enterprise has already begun across the insurance industry. Some insurers are taking advantage of hybrid cloud infrastructures to meet regulatory data requirements while providing increased data storage and analytical processing power. Others are creating new data architectures to support the creation of data lakes, which can be used to diversify the data available for analysis and decision making and support machine learning and AI initiatives.

However, the insurance industry lags behind several other verticals – including financial, retail, and manufacturing – in using data to create new market opportunities and meet rapidly changing customer expectations. The industry also has work to do in the areas of data governance and security. As data-driven decision-making increases, insurers must continue to build consumer trust and confidence in their ability to responsibly steward private information. Unfortunately, consumer confidence only lasts through the most recent data breach, so it’s critical for insurers, and the industry, to make the investments required to secure their data.

Strategy + Execution = Success
Strategies are only as good as their execution, and to become a data-driven enterprise, insurers must take it one step at a time. Defining what a successful data-driven future looks like, in terms of business process and top-line growth, is a good place to start. Once a vision is established, the insurer can create the data architectures required to achieve the future state. Following that, they can begin to implement the necessary data infrastructure, including hybrid cloud data stores; analytical tools for internal and external data sources, both structured and unstructured; and machine learning and AI platforms to process and present actionable data for informed decision making.

Of course, this process is the ideal scenario. To succeed, it’s important for insurers to continue to prioritize investments in data modernization and the talent required to implement, maintain, and scale new data and analytics platforms. Emphasis should be placed on business problem use cases that enable enhanced data access and quality capabilities while providing learning opportunities for future data efforts.