Viewpoint: Becoming a Data-Informed Organization

We hear a lot these days about the virtues of data-driven organizations. It’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 at least history says it does – so we’d better rely on programmatic decision models.

That might be reasonable in some situations, but when you’re working in a complex and nuanced area like property and casualty insurance claims, this highly automated decision-making paradigm can start to break down very quickly. Thousands of different variables come into play. Medical records and accident reports contain subtle details that provide vital clues to potential risks. To complicate matters further, important details are often buried deep within the narrative content.

An experienced claims handler can handle this, provided they have enough time and attention to devote to reviewing the documentation. Can an algorithm accomplish the same thing?

tyler jones

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

Data Driven vs. Data-savvy

The distinction here is of crucial importance. The data-driven paradigm is about automation. It is about shifting responsibility for decision-making from human actors and trusting algorithms to take their place.

A data-driven approach, on the other hand, empowers and helps people make better decisions by flagging potential risks, highlighting anomalies, and monitoring changes that may indicate a need for attention. It is a help, not a replacement.

For claims handlers, this has strong implications. Imagine, for example, that an injured worker missed three consecutive physiotherapy appointments. What does that mean? If the employee no longer feels the need for treatment, this can be a sign that they are ready to return to work, but it can also be an indication that the case has gotten worse. In both cases, an expert must be made aware of the situation so that he can make an appropriate assessment.

In a complex field like claims management, this data-driven approach holds enormous potential to transform organizational culture and processes.

Consider how complaints are handled today in most organizations; adjusters generally follow up on cases “as needed”. Depending on the individual adjuster, this may involve a journal notation, a to-do list, or a collection of sticky notes. Inevitably though, this means manually reviewing bills and medical records as they come in or as the adjuster’s schedule permits.

In a data-driven organization, the adjuster focuses on meaningful and impactful decisions. Because they no longer need to spend their time sifting through files looking for relevant information, they have enough bandwidth to apply their professional judgment on high-priority cases. AI does this work for them.

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

The business opportunity for insurers

The data-driven approach is already operational in a number of large companies around the world. It transforms processes and drives cultural change – but not in the way that many AI skeptics have predicted. Data-driven organizations don’t dehumanize their processes. Rather, they empower and elevate their claims professionals by allowing them to focus on meaningful and impactful work.

The data-driven paradigm is about focusing on the right claims at the right time. This involves spotting correlations and anomalies, identifying potential risks and bringing them to the attention of an experienced claims handler.

The result? A data-informed organization has a shorter claim duration and lower than average total claim costs. Not surprisingly, workers in data-informed organizations also enjoy significantly higher job satisfaction. These companies generate a high return on investment, not by reducing their workforce, but by elevating them to higher value-added activities.

The debate between building and buying

How does an organization achieve this type of transformation? It starts with a readiness for innovation and the 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 belongs to them and no one else owns it. In the world of AI and machine learning, however, more data is usually better. When ML models have access to larger volumes of information from a relatively wide range of sources, they can “learn” faster and more efficiently.

Creating and maintaining these types of high-volume datasets can be extremely expensive and time-consuming. The implication for insurers is that in the build versus buy debate, there is an increasingly powerful argument to go beyond proprietary data and embrace best-in-class platforms to drive the data-driven model.

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

About Tyler Jones


Jones is Chief Client Officer at Clara Analytics, a claims management company based in Santa Clara, California.

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