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Defining the nuances of data, information, and knowledge

Data-driven, big data. Knowledge worker, knowledge management. Information economy, information age. Such terms are tossed around all the time, but what do they really mean? Aren’t data, information, and knowledge pretty much the same thing? Well, not really.

Defining data and knowledge

The raw facts

As I see it, data is the raw facts we collect and track. Information is data that’s been organized and aggregated. Knowledge results from analyzing and drawing conclusions from the information. The distinctions are relevant and critical to insurers’ workflows. Data is fine for machines, but it’s neither good nor bad nor useful, just factual. Without interpretation, it’s static, raw, and unshaped. Insurers need data turned into information, which then can become actionable knowledge.

Data is the foundation of underwriting decisions, especially once it’s been processed into information, acquiring context and structure. Data and information about your customer base is essential to growing your business. You need to know who your customers are and what they value, so you can speak to them in a way they’ll listen. Data turned into information is palatable to your customers, employees, and vendors. The next step is turning that information into useful, actionable, understood knowledge. Many interpretive factors come into play with knowledge, such as our values, previous experiences, and expectations.

Humans as storytellers

Sound, reliable knowledge, however, allows you to create the message for your customers. From oral conversations to cave paintings, hieroglyphics to early alphabets, hot-lead type to texting—human beings have always been storytellers. It’s how we communicate, and the story you tell your market is critical to your business. Your story communicates your brand, conveys your values, and motivates customers to buy your policies. To create a story, you must impart knowledge by taking disparate pieces of information and weaving them together into a meaningful narrative.

Let’s use a general liability risk assessment case as an example. The data is the payroll and number of employees at an insured’s lawn care service business. Analyzing the data provides the information, which is knowing the job functions of the employees: Do they perform more sophisticated services than general lawn maintenance, such as installing water features and using pesticides that require a license to apply? The information then provides actionable knowledge by quantifying that risk with sound underwriting guidelines: Insureds that apply pesticides should be classified as landscape gardeners, which have higher loss costs than lawn care service risks. Now an insurer can develop a story for its customer based on sound data, information, and knowledge.

A strategic asset

Insurers need to understand that data is the starting point, a strategic asset you need to manage. Only data that’s as accurate as possible can anchor the analytic process to transform it into information and effective knowledge. If the data and metrics are flawed, the information derived from it will be inaccurate, and reliable knowledge cannot be developed. Don’t fixate on tracking, measuring, and collecting data, but rather on using data to create a meaningful story for the market.

Data is the basis, not the whole, of your intelligence. Use it to create information, use the information to create knowledge, use the knowledge to craft a compelling story that solves market problems—and create stories that move customers to buy. As renowned marketing guru Peter Drucker said, “Knowledge is power.”

For more information, visit our website.

Tracey Waller, CPCU, is product director for Small Commercial Underwriting at Verisk.

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