The insurance industry was an early use case in the world of big data. It’s no secret that insurance companies access large volumes of data and leverage complex technology stacks. While this makes insurers perfect candidates for large-scale data insights, it also means that the barriers to accessing those insights are likely to be structurally significant. Companies with large volumes of data often take longer to adapt and evolve their strategy. As a result, big data is as likely to be an Achilles heel as an asset for insurance companies.
Despite barriers, the case for data-driven innovation in the insurance industry grows stronger each year. A McKinsey study found that industry leaders are outpacing competition by “building advanced data and analytics underwriting capabilities that can deliver substantial value.” In an increasingly competitive marketplace, the cost efficiency, business-intelligence, and targeted selling made possible by data-enabled business models cannot be overlooked. And while many insurance industry leaders recognize the value of harnessing and leveraging the power of their own data, few understand exactly where to start.
The organization and proper implementation of different data workstreams (e.g., analytics, intelligence, and decision making) is a cumbersome effort. Additionally, big data technologies like artificial intelligence or machine learning, while buzz-worthy terms, can be difficult to implement and use correctly without the appropriate backend setup and knowledge. Getting insurance wrong has serious ramifications, both for businesses and the lives of the insured. That’s why Hakkoda created the insurer’s guide to building a data innovation strategy.
The True Cost of Insurer Database Silos
Database silos typically lead to operational inefficiencies, but they can also have negative impacts on your bottom line. For insurers, some of the most common consequences of database silos include:
- Dependency on inefficient external reporting applications
- Preventing migration to digital underwriting
- Limiting ability to uncover insights from data
- Reducing manager effectiveness
- Impacting customer service levels
Preventing migration to digital underwriting and limiting the ability to uncover insights from data are huge risks in a fast-moving and demanding digital ecosystem. The main benefit of a migration to digital underwriting is improved efficiency in issuing policies by automating repetitive tasks. However, disconnected data creates a number of new obstacles. With data silos at the base of your architecture, even simple tasks, like collecting relevant data and issuing an insurance policy, become long, arduous activities for your team.
Once the walls between data silos are broken down, it’s easier to uncover and share insights from the data. With the ability to aggregate larger volumes of data, analysts will be able to build a more complete view of the customer, and insurers will have a stronger understanding of their market. Over time, as data-driven decision making becomes more fluid, insurers will provide more accurate policies and ultimately, a better service to the insured.
How Snowflake Changed the Data and Insurance Industry
In recent years, some of the biggest leaps forward in the insurance industry have been powered by cloud adoption, which offers insurance teams enhanced infrastructure agility, data flexibility and scalability, as well as automation. Some of the top players in the cloud computing space are likely to be familiar, and include big names like Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and Snowflake.
Of the major names in cloud computing, Snowflake offers the greatest opportunity and inbuilt ease of use for the insurance industry. Snowflake provides a modern data cloud platform for data engineering, data science, applications, data sharing, and data exchange.
More specifically, Snowflake offers a financial services data cloud that has been designed and built with insurance companies in mind. With the intent to improve insurance underwriting, Snowflake’s Financial Services Data Cloud supports a variety of use cases unique to insurers, leveraging “data science and machine learning to automate traditionally routine tasks.” Included within Snowflake’s robust automation capabilities are jobs like underwriting workflows, monitoring portfolios, and providing accessibility to new and alternative data sources.
By combining a single data platform, third party data, and the power of data science, Snowflake can create a bridge across data silos, connect your data, and help you uncover insights to better serve your customers. A combination of data warehousing plus data analytics can provide key benefits that include a better customer experience, new sources of revenue, and mitigated fraud and risk.
How Hakkoda can help
The transition and evolution to cloud data warehousing and architecture solutions is inevitable. Much like when the world revolved around Excel, or Salesforce became widely adopted as a CRM solution (and then much more), cloud computing and cloud warehousing are the future.
Our mission at Hakkoda is to empower data-driven organizations. We will help bring data-driven innovation, automation, and new opportunities to your business. We’ll help you lead your field for decades to come. Hakkoda offers the expertise from years of experience in data warehousing and data architecture design, and the talent to help your company build it out.
Contact us and let us help you evolve your data!