Is more information better?
In this era of immediate accessibility to information through electronic media and worldwide networks, one misconception that can hamper R2O transitions is that the delivery of more information to the practitioner will lead to the practitioner providing a better deliverable to the ultimate customer. This information may be real-time data, imagery, model output, or some other derived product, but it may also be in the form of training, or as simple as a few extra words on the screen of the workstation. Of course, not all additional information is bad, and sometimes the problem lies more in the way it is presented to the practitioner than the content itself.
The best information, worthy for the practitioner to consider, is concise, relevant, and actionable. For an example of why information meeting these criteria is most ideal, consider the aircraft pilot flying through rough weather approaching a busy airport. There are a number of factors that the pilot must consider, and given the speed at which the plane is flying and surrounding traffic, attention to those factors must be carefully balanced. Suppose a weather satellite image showing a storm ahead arrives in the cockpit, but it is not annotated and the position of the aircraft and flight plan is not obvious. In addition, the image does not have a decent color enhancement, and the pilot does not have significant experience in interpreting satellite imagery. Despite the image showing danger ahead, the amount of time it takes for the pilot to determine that makes the additional information useless.
Granted, not every research product bound for a transition must be perfect in the information it conveys to the practitioner, and research teams should have some latitude for assuming how their product may be used—if they are informed and in touch with the practitioners. In fact, the R2O cycle is best for tuning toward final product because it affords a break from the time pressures of an operational scenario. However, in some circumstances, learning about the lack of viability of a research product once the transition process has commenced is a costly mistake.
Luckily, a mistake of this nature is avoidable with a sufficient discussion between researchers and practitioners prior to any research engagement. While practitioners may not know what they need, it is important for them to articulate their precise challenges and how they use information as part of their operational responsibilities. A field meteorologist, for example, may be looking for a way to better predict storm initiation. Knowing that moisture content is related to instability and storm formation, that practitioner may ask for a better analysis. Without inquiring why another moisture analysis is needed, a researcher may take the request and deliver a total precipitable water product for transition. This is where the quantity-value paradox begins.
On one hand, the field meteorologist now has another analysis that could contribute information. But it will not immediately help with near-surface dew points that control surface-based instability, an ingredient for storm formation, the origin of the challenge. Total precipitable water and near-surface dew points are related, but only loosely. The new product is a piece of additional information for the practitioner but it is not the most concise, relevant, and actionable that it could be. Any number of different moisture analyses will not ultimately meet the practitioner need. Instead, reviewing such an analysis will extract time from operational responsibilities, if the transition completes. More information is not more valuable in this example.
For this reason, the practitioners should always provide the “what” and the “why” early in R2O project discussions. It is up to the researchers to develop the “how”. In order to ensure the “how” solution is narrowly tailored, the practitioner is responsible for specifying their challenges with sufficient detail. While practitioners may have an idea on the solution, it is important to assure that those types of suggestions do not enter the challenge narrative and preclude the researchers from completely understanding the “what” and the “why” up front.
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