Becoming a data-informed organization

Digital data

We hear a lot of talk these days about the virtues of data-driven organizations. That’s certainly reasonable up to a point -- but what does it really mean? When it comes to routine operational decisions, in particular, the current bias seems to favor increased automation over human judgment. The data doesn’t lie -- or so the story goes -- so we’re better off deferring to programmatic decision models.

That may be reasonable for some situations, but when you’re operating in a complex and nuanced domain, take casualty insurance claims for example, that highly automated decision paradigm can begin to fall apart very quickly. Thousands of different variables come into play. Medical records and accident reports contain subtle details that provide vital clues about potential risks. To complicate matters further, important minutiae are often buried deep inside the narrative content.

An experienced claims manager can pick up on that, provided they have adequate time and attention to devote to reviewing the documentation. Can an algorithm accomplish the same thing?

The short answer is yes, but that comes with a vitally important caveat. In complex domains, advanced data analysis should not drive automated decisions; it should inform and empower human beings to make more effective decisions. The most effective artificial intelligence (AI) initiatives in place today are doing exactly that.

Data-Driven vs. Data-Informed

The distinction here is critically important. The data-driven paradigm is about automation. It’s about shifting decision-making responsibility away from human actors and trusting the algorithms to take their place.

A data-informed approach, in contrast, empowers and assists people to make better decisions by flagging potential risks, highlighting anomalies, and monitoring for changes that may indicate a need for attention. It’s a helper, not a replacement.

Looking at my casualty insurance industry example, for claims managers, this has powerful implications. Imagine, for example, that an injured worker has missed three consecutive appointments for physical therapy. What does that mean? If the employee no longer feels a need for treatment, then it may be a sign that they’re ready to return to work, but it could also be an indication that the case has taken a turn for the worse. In either case, an adjuster should be made aware of the situation so they can make a proper assessment.

In a complex domain, this data-informed approach holds tremendous potential for transforming organizational culture and processes.

In a data-informed organization, time can be spent on meaningful, impactful decisions. Because they no longer need to spend their time scanning records in search of salient information, they have sufficient bandwidth available in which to apply their professional judgment on high-priority cases. AI does that legwork for them.

Data-informed organizations can apply their valuable resources toward predictive severity-based workloads. They can focus on claims that need attention today -- based on real-time data. Incoming documents are reviewed and scanned by AI, and claims adjusters are notified when a case requires their attention.

The Business Opportunity

The data-informed approach is already operational in a number of leading companies around the world. It’s transforming processes and driving cultural change -- but not in the way that many AI skeptics have predicted. Data-informed organizations aren’t dehumanizing their processes. On the contrary, they’re empowering and elevating their claims professionals by enabling them to focus on meaningful, impactful work.

The data-informed paradigm is about focusing on the right claims at the right time. It’s about spotting correlations and anomalies, identifying potential risks, and bringing those to the attention of an experienced claims manager.

The result? A data-informed organization has a lower claim duration and lower than average total claim costs. Not surprisingly, workers at data-informed organizations also enjoy substantially higher job satisfaction. These companies are generating high ROI -- not by reducing their workforces but by elevating them to higher value activities.

The Build vs. Buy Debate

How does an organization achieve that kind of transformation? It starts with a predisposition toward innovation and a recognition that advanced data analytics has the potential to transform claims management from an operational perspective.

Conventional wisdom tells us that proprietary data is a differentiated asset. In other words, companies place a high value on their internal data because it’s theirs, and nobody else has it. In the world of AI and machine learning, though, more data is generally better. When ML models have access to higher volumes of information, from a relatively wide array of sources, they can "learn" faster and more effectively.

Building and maintaining those kinds of high-volume data sets can be extraordinarily costly and time-consuming. The implication for insurers is that in the build versus buy debate, there is an increasingly powerful case for moving beyond proprietary data and embracing best-in-class platforms to drive the data-informed model.

This provides for a flexible co-innovation process, enabling insurers to leverage solutions and platforms that have already been proven in the real world, without reinventing the wheel. It’s the fast-track alternative for companies seeking to become data-informed organizations.

Image credit: nevarpp/

Tyler Jones, Chief Customer Officer at CLARA Analytics, has nearly two decades of experience in the insurance and banking industries. He is responsible for directing CLARA’s complete relationship with its customers and driving efforts to assess and elevate experiences at each touch point across the customer journey. His experience as an innovator and strategist includes roles in which he successfully transformed customer experiences and enterprise operations by harnessing the power of data alongside digital apps, social platforms, and artificial intelligence. For more information on CLARA Analytics, the leading provider of artificial intelligence (AI) technology for commercial insurance claims optimization, visit, and follow CLARA Analytics on LinkedIn, Facebook and Twitter.

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