How are Insurance Carriers Maximizing Their Return on Investment?
The hallmark of business leaders in the 2020s is their capacity to successfully execute digital transformation initiatives. Globally, businesses spent $1.3 trillion on digital transformation in 2020. That number is expected to rise to $2.3 trillion by 2023. For insurance carriers, the primary area of focus has been portfolio and risk realignment, shifting from commoditized products to higher-value offerings or expanding to new markets and territories.
Digital transformation initiatives are generally aimed at four key business objectives. These include increasing productivity and revenue, improving the customer experience, new product and service offerings, reducing risk, and innovative new business models.
To enable that shift, companies have used analytics to better understand their customers, markets, and risk profiles. They are using digitization to build organizational agility, enabling them to add new products or enter new markets rapidly. They are leveraging the Internet of Things (IoT) to better understand and control risk.
The single common factor behind all of these scenarios is data. Unfortunately, though, as data plays a more important role in driving business value, the risk of getting it wrong increases dramatically. To maximize return on investment, insurance carriers must attend to data governance.
Managing Change vs. Growth
Much of the focus for digital transformation initiatives in the insurance industry is on growth. Insurance carriers are investing in digital products and services, analytics, an omnichannel customer experience, and automated operations. As those programs unfold, it’s important that insurers also pay close attention to the impact of those changes on the data and processes they support.
Organizations are using the same tools that have served them in the past to manage operational, regulatory, financial, and even reputational impact. Unfortunately, many of those tools and processes are not capable of effectively supporting the speed of the flexibility required in the context of today’s digital transformation initiatives.
For example, if an insurer’s billing system does not contain accurate information, it can be hard to effectively improve the customer experience. In many cases, there are likely to be financial and regulatory repercussions as well.
Data quality is nothing new for insurers, of course, but as digital transformation accelerates the pace of change and as data plays a more prominent role in driving business value for carriers, the cost of poor data quality is much higher than before.
A Proactive Approach to Data Quality and Governance
In a typical digital transformation initiative, early-stage optimism is often tempered by a drop in the level of trust stakeholders have in their data. That can cause programs to stall or, at the very least, fall short of delivering on their promised results.
By proactively mobilizing an effort to shore up data quality and proactively govern the data early on, insurers can achieve substantially better outcomes, including a 25% reduction in implementation time and a 40% reduction in the development effort. Functional spec re-work can be cut by as much as half.
Precisely has identified three strategies that lead to successful outcomes and maximize insurers’ return on investment for digital transformation projects:
- Align data governance to organizational project objectives to reduce the implementation effort and duration.
- Leverage automated controls for data quality, including balancing and reconciliation, to avoid operational disruptions and maintain regulatory compliance.
- Increase efficiency and capability through a centralized data integrity platform.
Aligning Data Governance with Business Objectives
The fundamental purpose behind data governance is to deliver business outcomes and connect business goals and objectives in a measured way. Stated a bit differently, the value of data governance is in delivering the right data to the right people at the right time to support sound business decisions. At Precisely, we call this the “business first” approach.
The business first approach requires critical steps to ensure a successful data governance program. First and foremost, link the data governance program initiatives to higher-level business goals, stakeholders, and business outcomes. Knowing what you want to govern should be driven by business drivers that matter to data teams and consumers.
Secondly, prioritize critical data that directly impacts those goals. When your business objectives are driving data governance, it leads to a much clearer definition of priorities, especially with respect to which data matters most. At Precisely, we find that about 5% of all data in an organization is used to drive 95% of the business outcomes. Once those priorities are clear, the data can be linked to specific processes and tactical and strategic metrics. That provides clear visibility, connecting data quality and governance to the specific outcomes your organization is aiming to achieve.
One critical error most insurance carriers make is not bridging the gap between strategic, operational, and tactical teams. Building a program that is only designed for one group limits the engagement that can be gained on shared data sets. Describing the value in terms of what matters to each group will solidify your business case and increase collaboration for more complete insights.
Finally, leaning into your culture to embed governance in your everyday tasks and activity will reduce operational friction that prevents adoption. Business first data governance makes it easy for teams to contribute and engage.
Leverage Automated Controls and Centralizing Data Quality
Many insurance carriers are using a hodgepodge of tools to manage data and data quality, in particular. Over the years, as systems and operational processes evolve, many of those tools and processes fall short of delivering the flexibility and consistency that organizations truly need.
In order to practice quality fundamentals, carriers must address data quality proactively. That means increasing the visibility of data quality controls to ensure that the data is trusted. As an organization scales, as the volume of data grows, and as the importance of that data to the business increases, efficiency, automation, and standardization become must-have features.
By centralizing data integrity, carriers can shore up trust in their data. Data governance, data quality, and other factors that contribute to that confidence should be aligned around a common vision of data integrity.
As the global leader in data integrity, Precisely offers a holistic set of tools for data governance, data quality, integration, location intelligence, and data enrichment. Our team of insurance industry experts has helped dozens of insurance carriers generate business value through trusted data and digital transformation. We are proud to count many of the world’s top insurers among our loyal customers.
To learn more about driving business value in your organization, read our eBook, 4 Steps to Successful Insurance Data Governance Programs – A business-first approach delivers better business outcomes.