The constructive approach is to then ask “Why are we having this problem at all?” until you’ve worked the whole way back to the initial problem, and compare it to the solutions proposed or already in place, stripping out all the middle problems. And when “We have trouble acquiring qualified personnel” is being answered with “use more Gaffer tape” or “we need to buy a cart”, everybody should know there’s something wrong and start doing their work.

My two cents: For platform owners such as Apple, Amazon, Google or Microsoft, the ‘art’ is to close the door only so much that the input-providing participants don’t feel uncomfortable squeezing through it, and keep providing stuff (apps and content), because the consuming participants will only start switching once they realize the restrictions applied lead to a perceived lack thereof. Angry nerds won’t fix it. Unless they invent a different thing that restarts the cycle.

Jumping on a problem happens when the solving process is one promising a bit of fun or distraction from other problems along the way. That’s why some people are very eager to help others solve their problems so they don’t have to mind their own. In this case, solving a problem implies ignoring another one. And the only reason we ignore problems is they make us uncomfortable.

So it turns out solving problems has two basic dimensions, effort and benefit. And there is of course a certain fallacy that comes with our expectations because so far we have left out one crucial step. The fallacy is that we expect a problem regardless of its level of complexity to be solved immediately and permanently, and it should be done effortless but the result be beneficial for all. Though these four goals are not contradictory, it’s the unlikelier they will be achieved the higher the complexity of the problem is.

Which is why we really need to think about the complexity of a problem before we start solving it. The point is that more often than not we think we know how to solve any problem because we merely know the first step(s) of the process. As a matter of course, this suffices for simple problems, but it’s already a pitfall for problems with a low level of complexity, because we tend to only consider the good-case scenario. In problems with high levels of complexity or even complicated ones, this will get you into serious trouble along the way.

The only thing we can do to avoid this is to outline the current situation, define when the problem is solved and work different solution scenarios by breaking the solution process down into single steps and extrapolate future efforts and benefits. I’ll tell you in a second why this matters. While we’re working down the list of steps we’ve chosen, we need to monitor the process and if necessary readjust the parameters which lead our decisions, and we can only do this on evidence. This is important. Your stomach is a bad advisor in this case. If it has any relevance, it should lead you to sift through all your accumulated data so far and try to find evidence for your assumptions. If there is none, it’s only because you feel now that you have a negative effort/benefit ratio.

At the beginning all of this is not fun, especially when it seems to complicate problems you thought to be solved easily. In this case, the process helps you not to walk into a dead end, saving you from both frustration and the impression of incompetence. Not too bad a deal. In case of very complex problems, it takes away your petrification in face of an impregnable challenge.

It’s all in your head. Now get to it!

First, there are simple problems. Hanging a picture on the wall.
Abstract solution requires: Knowing how to make some protrusion.
Practical solutions: Drive in a nail./Bore a hole, insert a plug./Bore a hole, insert a plug, insert a screw./Glue a plug or hook to the wall./Chisel out an alcove./Build a pedestal.
These are “whatever works” problems, and they are characterized by a low level of complexity, their solution rarely requires more than 2 or 3 steps (though Yak shaving is possible), and there is little at stake, both financially and emotionally. You don’t need a plan, and it’s easy to sell someone on the solution you prefer.

Next, there’s complex single-subject problems like Measuring the efficiency of your production process.
This requires you to break the problem down to simple problems/questions. What are the single processes the whole thing is made up of? Who’s involved at what stage? This doesn’t mean you have to analyse each and every single process itself, since this is about the overall thing.
These problems are characterized by an intermediate level of complexity and their solution requires several steps, often over a certain period of time. In that sense they don’t have to be actual problems, but questions to determine if you have a problem, and where. With this kind of problems, there is not necessarily something at stake financially (though it might be), yet they’re easily being perceived as a threat with a lot of downside (e.g. feeling scrutinised) and little upside (not seeing one’s own personal benefit from the solution proposed). Hence the (pragmatic) solution is facing opposition very easily, combined with the escape question whether the present system should better be replaced with an entirely different one (the one you switched from last time). And the solution might need to be readjusted itself because, for instance, you realize you’re focusing on the wrong issues. Yak shaving is a welcome distraction to avoid cutting to the core.

Next, there are complex multi-subject problems such as running a project, for example building a website. This requires a set of different skills and the ability to tackle the seemingly big lump from various sides, then drilling down each approach to find out how much substance there is. For a website this might be design (which again can be split up into colour scheme, typography, imagery), legal issues (possible copyright infringements), technical issues (programming) and providing content.
Whatever the solution is, when you’re trying to sell someone on it, the standard reply is often whether your approach was the right one to choose at all. Depending on the project, there can be a high financial risk. On the emotional side, it’s more likely to be dealing with primal behaviour, determining who is the strongest. Also here the solution might need to be readjusted because after a certain amount of time it’s been shown not to fulfil its purpose as it should have. Yak shaving is common.

And finally, there are complicated problems: Having a business. Causes. Political campaigning and lobbying.
The complication results from various factors: Solutions will need to be constantly readjusted because decision parameters change. Decision criteria themselves change. People involved in the process change their minds. High financial risk. Circumstances assumed and results expected are permanently subject to change. It’s like a theme park on a raft, somewhat chaotic. The main skill required for this kind of problem is knowing when to stick to a decision and when to quit. There’s an upside too: At any given point in time, the problem is only a complex multi-subject one. It’s only in the long perspective that it’s complicated. Yak shaving is compulsory.

Special case: Vanity problems. This is Yak shaving. These are solutions for one of the four previous degrees, but there’s no problem.
This ranges from believing you need a new hairstyle (simple) to building an underground railway station when you have one on the ground (complicated).

So what’s the point? Am I shaving the Yak? No, it’s just the setup for the next post: Solutions. That’s what I really want to talk about.

• There is no correlation between the amount or quality of data and the amount or quality of information. Example: “I don’t use a DSLR camera for filming because it’s not good enough.” This opinion (information, subjective) does not require anybody to know all the facts. Everybody has a different threshold when they feel having sufficient “information” (it’s data) to make a decision, change or define their worldview and tell themselves a story. The data which is not included in the story becomes irrelevant. Trying to persuade them differently is unlikely to yield success then.
• On Air Promotion trailers: The less data you need make the viewer feel informed, the better. The worst thing you can do is give the audience too much data. The factual data should no more than name and time of the show. Everything else better be a story people can connect to (and for this to happen, any emotional reaction is what you want, not only positive ones). Don’t give them data, because then they’ll start putting all the input into context themselves and stop listening. You don’t want that, of course. This applies to advertising in general as well.
• The previous part also applies to presentations. The purpose of a presentation is to give information by present data in a narrowed context. This means you (the presenter) need to boil down the data to a level as simple (yet still correct) as possible. Do not show complicated charts or graphs (and worse, reading them to the audience). What’s on the screen or whiteboard is only supplemental to your verbal argumentation. It is not the information.
• Do we really need compilations of references? At least their compiler should have the decency not to label it a “Guide” or similar. Because it’s not. It’s a directory, index, compilation, collection. Yeah I know. People love Guides and How-To’s. People love “not getting scammed” even more.
• We need to train and force ourselves to decide on the spot more often, because it is often that we do have enough data and information to decide on the spot, but we’ve so gotten into the habit of “digesting” and “sleeping over it” we’re just too slow.

Last week, German newspaper Die Zeit published an article citing a study among managers and their biggest issues on the job, resulting in ten rules a good manager, according to the paper, should follow. Let’s just say the author would have better read some books on the topic, yet he seemed to prefer the blather. But that’s not why I’m writing this.

The study revealed that one of the most pressing problems of managers is the amount of decisions, which requires a lot of information for each of them. Obvious. To decide, you need information. Now, what is information?

I like Fredmund Malik’s definition that information is knowledge that leads to action. Now, if you think about it for a few seconds, how much of the things that enter you brain in the course of a day do lead to action? Indeed, very little. What’s the overwhelming majority then? It’s data. When you look up the definition of data and information at Wikipedia it’s all there, though I don’t agree that a book with all data about Mt. Everest automatically becomes information. Data only becomes information when it is put in a context that leads to an action on your behalf.

Looking at all the bits and pieces we’re dealing with daily that way, it’s plain to see why making decisions is such a massive time-consuming process. It’s not information we’re dealing with on the input side, it’s data that we must put in perspective, be it an analytics report, a movie clip, the latest news. It’s not that we (as humans) were producing ever more information, we’re just producing ever more data which in turn we must filter out to obtain information.

Now, is what I’ve been writing about in these few lines data or information to you? Since we will keep producing ever more data, the ability to distill data to information will become key to future success for anyone, because all success depends on the ability to make decisions. This necessity requires not only organizations of every kind to teach their employees how to get better at this, it also requires schools to switch from teaching young people to learn everything from a limited resource (i.e., a school book) to learning the process of filtering out the irrelevant data from an unlimited resource (i.e., the Internet). For the careful reader, the previous sentence has turned data into information. Thank you for reading.

The idle learns what he needs to learn because it is required.

The pro learns what she can learn, because there is no reason not to.