Section 2.4 Addressing Risk and Uncertainty
A third strategy for identifying data problems is to find out about risk and uncertainty. If you read the previous chapter you may remember that a basic function of information is to reduce uncertainty. It is often valuable to reduce uncertainty because of how risk affects the things we all do. At work, at school, at home, life is full of risks: making a decision or failing to do so sets off a chain of events that may lead to something good or something not so good. It is difficult to say, but in general we would like to narrow things down in a way that maximizes the chances of a good outcome and minimizes the chance of a bad one. To do this, we need to make better decisions and to make better decisions we need to reduce uncertainty. By asking questions about risks and uncertainty (and decisions) a data scientist can zero in on the problems that matter. You can even look at the previous two strategies asking about the stories that comprise professional wisdom and asking about anomalies/unusual cases in terms of the potential for reducing uncertainty and risk.
In the case of the farmer, much of the risk comes from the weather, and the uncertainty revolves around which countermeasures will be cost effective under prevailing conditions. Consuming lots of expensive oil in smudgepots on a night that turns out to be quite warm is a waste of resources that could make the difference between a profitable or an unprofitable year. So more precise and timely information about local weather conditions might be a key focus area for problem solving with data. What if a live stream of national weather service doppler radar could appear on the farmer’s smartphone? Let’s build an app for that...
You have attempted of activities on this page.
