The Decision Orientation

Decision Orientations pose their own special challenges for folks used to delivering situational awareness artefacts.

Typically bounded by time, involve prospection, and contain a weighing of preferences,

Concretely:

• Time Bounded: Planning for a major strategy roll out may take quarters. Deciding to discount a SKU may take only a few seconds. Time horizons vary. And they matter.
• Propsection: thinking about outcomes in the future. How much do you expect to gain from a decision? How much do you expect to lose?
• Preference of the people making them and those around them: Is that future something that you believe is desirable?

Optimistically, you can view these facts as opportunities. Pessimistically, you can view these facts as constraints.

Let’s view them as both, because there’s good reason too [1].

There’s a core difference between information for the sake of situational awareness, information for the sake of supporting an opportunity (business case), and information for the sake of making a decision.

The approaches to each type of situation vary.

The decision orientation can be amongst the best and most frustrating. Decisions aren’t always completely data driven, even amongst the most purest of scientific managers.

The ability think prospectively is a skill. It’s likely normally distributed in the population. The majority of people are competent and can function. Some are excellent short-run managers for their ability to think quickly and immediately. Others are far better long-run managers for their ability to prospect and plan accordingly. Since it’s a skill, it can be improved. There’s nothing deterministic about it.

When expectations conflict with the already latent preferences of those involved, you have recipe for sub-optimal results.

Optimization is a desirable goal.

There are realistic boundaries in there. Sometimes the best answer isn’t always the right one for that situation. Some futures are just better to some groups of people, personally.

However, facts, prospection, and mapping preferences are three major areas that digital analytics can (and should) contribute outside of the regular situational reporting cycles.

[1] See Kingdon (1984) on Policy Streams, if you’re interested in a complete, formal model. I’ll gladly engage in a debate about Kingdon with anybody who wants to go there. It matters.

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I’m Christopher Berry
I’m at Authintic.