10. Data is the New Gold? We Think That’s All Wet

Brendan McLoughlin, President of e123, is participating in an executive education course at the Massachusetts Institute of Technology on Artificial Intelligence (AI) and its implications for business strategy. This is the tenth in a series of blog posts where he shares the insights he is gaining and how they apply to health insurance distribution.

With apologies to SalesForce and @MathewMcConaughey, the idea that data is the new gold misses the true power of Artificial Intelligence (AI), and can lead to massive waste of time, money and resources. As AI becomes a more important competitive tool, it’s important that health insurance executives understand what makes data valuable, how that value is best realized, and what well-intentioned initiatives can destroy the value of their data. In fact, thinking of your data as a water source, rather than a vein of gold, can lead to faster implementation and more value creation from your data.

”There’s Gold in Them-There Databases!”

It’s tempting to think of data as gold. After all, the data floating around health insurance carriers is probably the single greatest untapped source of value creation for these companies. Without a doubt, data IS valuable, but the value comes from how data is used, not how it is collected and stored. This kind of “data is gold” thinking encourages behavior that actually limits the value data can contribute to an organization. Like modern-day prospectors, IT organizations across the health insurance industry are in a mad rush to gather all the data they can, believing that value from the data will naturally emerge when it’s all brought together, standardized, labeled and warehoused.

Welcome to the Data Warehouse

The idea of a data warehouse was once a vital concept in realizing value from data. In the past, doing anything valuable with data was impossible unless it was centralized, categorized and cataloged, allowing structured queries to be written that extract information out of the data. And the prevailing wisdom was that until the data was properly warehoused, any attempt to use that data would be a failure. So major players in the health insurance industry have spent years and billions of dollars in the quest to build the ultimate data warehouse - a single source of truth that will unlock the competitive advantage of their data.

For a period of time, data warehousing was the only reasonable way to build actionable advantage from a company’s data, but even when well executed, data warehouses suffer from weaknesses that constantly undermine their usability, and as a result, their value to the organization.

1. Investment - building a data warehouse is a major, resource-intensive and time-consuming initiative. Whereas gold is gold is gold, data in its natural state, does not like to conform and warehouses run on conformity. Like a modern-day Amazon distribution center, data warehouses can only be effective if everything is exactly in its place. Slight inconsistencies can cause the entire system to shut down, stopping all useful work until the anomaly is addressed. Given the fluid, ever-changing nature of health insurance, this means that any attempt to warehouse data is almost instantly obsolete. For instance, a major player in health insurance has devoted years to building a comprehensive data warehouse - absorbing thousands of staff hours and millions of investment dollars with little to show. One executive confided that after all that investment, “we are probably 7.5% of the way there”, and I suspect they were being generous.
2. Inflexibility - a data warehouse is only as useful as the architecture behind it - data stored in the warehouse must conform to a certain structure. But unlike gold, data is fluid. And unlike a precious commodity, new sources of data are forming every day. Semi-structured and unstructured data might be the most valuable source of advantage to a health insurance company. Data like customer social media posts, agent/customer interactions and agent reactions to new commission plans can enable powerful decision-making by insurance marketers. However, these types of data are difficult to “put in boxes” and store in a warehouse.
3. Timeliness - given the huge amount of upfront, on-going work to load and maintain a data warehouse, it’s practically impossible that insights gained from analyzing that data can be anywhere near real-time. While gold holds its value while it sits on the shelf, data tends to become less valuable as time goes on. Nowhere is this more evident than in health insurance distribution. One health insurance executive commented that they did receive all the data they needed to make real-time marketing decisions… six months after open enrollment was over. This problem is ubiquitous, and in a recent survey, more than half of senior marketing executives said that their organizations’ data analytics projects did not have the kind of impact they expected.

Go Jump in a (Data) Lake!

Rather than thinking of data as gold - a commodity that has intrinsic value that doesn’t change much over time - the data scientists at e123 suggest we think about data how we think about water. Water is fluid, it is varied. Some of it pristine and clean, some of it dirty and polluted. Water comes from multiple sources that range from falling from the sky to bubbling up from underground to everything in between. Water, in and of itself has only limited value, but it’s in its use that it becomes a tool for competitive advantage. Water can be harnessed to grow crops or generate power. It can be used as an ingredient to make more valuable products or can be sold on its own. Water is, arguably, the most valuable resource on earth and your data, arguably, is your most valuable strategic asset.

It Doesn't Make Sense to Warehouse Water

The idea of standardizing, boxing and cataloging water is nonsensical, and with the advent of advanced AI, forcing data into this kind of conformity is no longer necessary. A modern data lake is a scalable repository that allows organizations to store all their structured and unstructured data at any scale. Unlike traditional, highly structured data warehouses, data lakes store raw data in its native format until it’s needed. And with modern AI and Business Intelligence (BI) tools, this data accessibility can mean significant competitive advantage for the health insurance players that get it right.

The result of this flexible approach to data is faster, more meaningful decision-making. Freed from the constraints and delays associated with massive warehouses, your data is now available to make real-time decisions that actually impact your business. Imagine rather than waiting six months for a report after open enrollment, to be able to get your hands on relevant and timely insights on the first day for data-based decisions that can impact the remainder of the critical open enrollment period.

Conclusion

The notion of data as the new gold may be appealing, but it fails to capture the true potential and nature of data in today's health insurance industry. Instead, thinking of data as water provides a more accurate and beneficial perspective. Unlike gold, which is static and inherently valuable, data is dynamic, fluid, and its value is realized through its application. Traditional data warehouses, while once essential, are now often too rigid, costly, time-consuming, and outdated for the needs of modern health insurance companies. They struggle with the volume, variety, and velocity of contemporary data. In contrast, modern data lakes offer a flexible, scalable, and real-time solution for managing data, enabling health insurance companies to leverage advanced AI and BI tools to derive meaningful insights and make faster, more impactful decisions. By embracing this approach, health insurance companies can transform their data into a powerful strategic asset, driving competitive advantage and better outcomes for their business and customers.

Want to learn more about the future of AI in insurance distribution? Get in touch here. For prior posts in this series, click here or below: