We live in an era where big data drives industries of all shapes and sizes. For the insurance industry, it is no different. In the course of our everyday lives, we as consumers collectively generate more than 2.5 quintillion bytes of information daily. As an industry itself, big data is expected to grow exponentially over the coming decade, reaching $1 trillion by 2026. Of critical importance to insurance companies and their various distribution channels is the question of access. How do we store and access all that high-value customer data, and how do we use it most effectively to further our distribution database?
We’re going to analyze the differences between key data storage methods and why access to these data storage schemas are so important to insurance professionals.
Databases Vs. Data Lakes
Data comes in all shapes and sizes. Commonly collected data relevant to the insurance industry includes:
- Financial data
- Geographic data
- Risk factors
- Consumer habits
- Claims history
- Carrier history
This is just a tiny cross-section of the infinite range of data points that are potentially valuable to insurance distributors. But how do these data points aggregate? When it comes to the information arena, there are two key terms you’re likely to hear on a regular basis:
- Data lakes
What Is a Database?
A database is a way of collecting and making sense of data systematically. The information contained in a database is called “structured data.” Structured data is information that is highly organized, but moreover, structured data is known, easily quantifiable information such as names and addresses.
It may be helpful to think of a database as a catalog from which specific information is easily retrievable.
What Is a Data Lake?
A data lake, on the other hand, is a relatively new form of big data storage. Much like the surface —and depth— of an actual body of water, the information contained in a data lake mixes and flows. It is raw and unstructured unlike its databased counterparts, and yet, it potentially contains an infinite amount of additional value.
Databases are primarily built for human consumption whereas a data lake is built with AI and machine learning in mind. The data points it deals with are much more minute, but they add up to an extremely valuable, comprehensive picture of consumer behavior chock-full of insight.
Other Data Storage Alternatives
Databases and data lakes are at the forefront of big data transmission. There are other storage modalities that you should be aware of, however.
Data warehouses are a large-scale storage solution on par with data lakes. And while many people use the terms synonymously, there are slight difference between the two. A data warehouse is meant to house large quantities of data much the same as a lake, but the data contained therein is structured like a database.
A data hub is another point of interest in the conversation. Data hubs act as transmission points for structured data to flow to different warehouses and databases.
Why Big Data Is Crucial to the Insurance Industry
Big data is big business, but why does it matter in the insurance distribution arena? In a word, insight.
The confluence of new technology such as cloud-based computing, AI, machine learning, and of course big data, has brought the insurance industry to an inflection point. Distribution models are no longer simple face-to-face interactions. People have come to expect multi-modal access to your insurance products.
The underwriting process also benefits from big data analysis. With vast amounts of structured and unstructured data in the hands of your actuarial professionals, you can offer smarter policies that mitigate risk much more effectively.
Big data is the key to the insurance industry’s continued progress. While this is just a cursory introduction to the world of analytics via storage methods, having a solid base can help you refine your company’s practices by harnessing the wealth of actionable, high-value data available to you. Contact us to learn more.