Decision making in a world of live data
How decision making can be improved in a world of live data feeds — what types of outcomes can we enhance?
What if we could use data and the process of informing to help individuals and organizations make better decisions. Imagine if live data feeds informed us — in real time — of options that we could be rewarded for taking. Incentives and rewards could be financial, social, or personal. And through transparent processes and supply-chains, we’d be able to see and attribute the effects of our decisions.
This can be as simple as seeing the emissions or water you’ve saved through a simple choice such as not eating meat for a day or choosing to ride with an electric car. So let’s say you can be rewarded for making better decisions, for being healthier, or for consuming something that is produced through less harmful means.
How would such a mechanism work, and how would you be able to benefit directly? We need to think about how our choices affect things by the second or third order.
Let’s look at some possible examples. These are not meant to be universally valid or perfect statements, but potential scenarios that can work in concert with the ideas being discussed.
Healthcare: Would getting people to exercise more lead to a generally healthier population; would this then lead to lower obesity and diabetes rates, longer lifespans, and time spent in hospitals; and would this then lead to lower healthcare costs?
If these things are true, can my choices to be healthier allow me to lower the health insurance premium I pay as a customer? This idea is already in play today. We fill out forms to declare our state of health and habits, but imagine how much more precise and timely the rates and premiums we are offered could be. The savings achieved by public healthcare services, treatment providers, and insurers can be passed on directly to users through the costs of treatment and policies.
How can we test whether this is even an issue?
We might start by asking how and whether something such as a lack of exercise — something within control to most people — affects one’s health (first order) and eventually, one’s financial situation (second order) through a requirement for additional treatment or a more expensive insurance policy.
You can look at people who exercise below a recommended threshold or look at people who do not follow an approved diet, and then determine their increased likelihood of requiring treatment and other resources.
Food and Agriculture: Billions of tons of food are wasted every year. Can live data streams inform producers, distributors, and retailers more precisely? Helping reduce waste and inefficiencies across supply-chains.
What if we could inform retailers exactly what our requirements are and what we plan to buy on a weekly or monthly basis. Retailers could then more precisely manage their inventory with distributors and wholesalers, allowing them to have a better handle on their inventory management. In turn, consumers can be rewarded with a financial incentive in exchange for being proactive and responsible. These incentives could come from savings that would have otherwise represented waste from inefficient forecasting in inventory and demand management.
Imagine a year in which there is short supply of lemons, driving the price of lemons upwards. Now imagine if you could receive recommendations for other citrus fruits that are not as expensive but that would be viable substitutes. Imagine having this capability in the form of a tool that allows you to make such procurement decisions.
Public Transport and Infrastructure: Live data feeds can inform central transportation authorities and operators on which routes or modes of transport are bearing the highest levels of congestion. Can we incentivize drivers to use certain roads and modes of transport, based on congestion, as opposed to disincentivizing them by placing a toll on the use of a road?
For example, I might be happy to take an alternative road or mode of transport when I can see that other options are congested and that I would stand to gain an immediate and tangible benefit.
Imagine a scenario in which you are not very time constrained and — through real-time data — you receive a suggestion to take an alternative highway that will add another 20 minutes to your journey time. In exchange, you receive public transport or toll road credits that can be used during a future journey.
You might also be incentivized by the fact you are having to pay less for choosing a certain option.
Summing up
In principle, many of these concepts existed previously — but accurate, widespread, and interconnected data networks allow them to have a more pronounced effect.
The process can be applied to almost any industry, though it is likely simpler in industries where the costs are borne by stakeholders that represent individuals and organizations. That is what makes incentivizing fossil fuel alternatives difficult — the costs and implications are borne mostly by the environment, the consequences are distant.
First identify the processes that exist within the value chain, and then map where the cost saving and value generation opportunities are. Then ask: can we design better incentives and processes to allocate costs more effectively?