Why would I choose to focus on this topic? It sounds so boring that I risk losing your attention already at the title.
But no, I haven’t lost my marbles. Lately, we’ve discussed at length the crucial role of the Manager in the Decision-making process, so it brings us naturally to the most crucial component of this process – the interpretation of information. And so it happens that organizing information to facilitate decision-making turns this information into data and the process of working with it into research. This means that every manager is constantly engaged in data interpretation because, as we discussed previously, every manager makes decisions. In fact, I would say that the real potential to unleashing your managerial powers is 100% dependent on how good you are in utilizing data and research in your work.
But this is actually not as easy as it may sound – the fact that everyone is doing it doesn’t mean that everyone is doing it right. The key problem here is that information is initially a mere collection of facts and “There is nothing more deceptive than an obvious fact”, in the words of one eccentric British private detective of the late 19th century :-). If this is correct (and it is!) and the facts comprising the data are open to interpretation even before the data itself – what are we left to work with?
There is no easy answer to this paradox, but we can at least reduce the amount of uncertainty around us by understanding some very basic properties of working with data in a corporate environment. The manager is not a researcher and he’s not supposed to be as it’s a profession in its own right, but he has to understand how the information could be distorted when passing through tenets of social hierarchy. After all, he’s an integral part of this hierarchy.
So, let’s survey the main aspects of how the understanding of data could be influenced by social environment:
Who presents the data – you have to realize the presenter’s agenda, including the simple fact that there is always one present. Relating the agenda to what’s being presented will help you to both interpret the intended message and the real data behind it. Be always aware of the situation and the format you are in. Pay attention to what’s said and what’s not being said.
How the data is presented – make sure that the inner structure of presentation is not swaying you towards a certain choice. When being presented with options A, B and C think: is the choice between the options equal or somehow influenced by the order of their presentation. Do you know that statistically speaking, most of people never go for A? There is a great chance that you’re being manipulated towards choosing B (if C is the opposite of A) or C (last option could easily seem as the best one when there is some linear progression from the first option). There are other numerous techniques to play with human focus and attention, but as long you remember to strip away the distractions they won’t work on you :-).
What is the type of interaction when data is presented – is the exchange of data the real goal here or a person is trying to convey a certain message by illustrating some data? In the first type of interaction the discussion tends to focus on the analysis of the presented data, in the second one – it’s all about who’s right and who’s wrong. If this difference is misunderstood, it would be like two deaf people talking, both angry at each other for not being able to understand. Context is a king. Realize the type of interaction you are in and it will save you from many unnecessary conflicts.
Source and type of the information – double check the origins of the data. Which database, who prepared it, which application was used, what kind information is being presented – personal interpretation, raw data, public opinions, or something else? It could be simply asked without undermining the authority of the presenter. Make it part of your style, so that no one will take it personally from you, knowing that you always do that. It’s as simple as it sounds: “Garbage In Garbage Out”, so make sure you can trust the data you’re working with.
Expert or no expert – so often the word “expert” is taken for granted. Don’t forget to use your own eyes to see if what’s being presented is indeed applicable in your particular context. Even experts learn new things from experience and you don’t want to find yourself looking for excuses and explaining afterwards why your scenario was an exception to advised method. The “expert” can walk away with “thank you, usually if it’s not “A”, then it’s “B”, so it’s good to know that in your case…” But for you it will be already late.
Troubleshooting – things will always go wrong at some point and it’s manager’s responsibility to find out why. In other words, it means that you investigate, analyze and connect the dots, trying to establish the root cause of the problem and perhaps prevent it from happening next time. And that is nothing short of research, as I pointed out in the beginning. Scientists will observe/ask as much people as possible, sometimes 100 and sometimes 100,000. That is not because they are crazy (well, most of the time), but because they want their sample to be representative. Know whom and how many people you should be asking. Know how to formulate the questions, as even their tone or the way they’re phrased can change the conversation upside down.
Aligning language, understanding definitions – do you understand all the meaning of all words in the data that is presented to you? Think about acronyms. There is no week going by that I don’t see some new invention in the acronyms world. The funny part is that there is only so many letters and often the same acronym can mean totally different things. Don’t be afraid to ask, most likely someone else in the room doesn’t know it either. It is not a crime! A crime is making a decision based on something that you are not sure of. Many managers believe that asking questions will belittle them in the eyes of the team, when the opposite is actually true…
Check yourself for biases – are you interpreting the correlation between the events this way because of your understanding of the data or because of wishful thinking? There are so many biases that can impair your judgment, both cognitive and statistical. One could easily become biased due to his set of beliefs and fears. Think about the fear of being called inflexible and not liking change. Innovation is great, but there is a difference between conscious choices of what’s right and believing that if someone did something new and succeeded, it will be right for you too. There is a fashion in ideas too, but fashion comes and goes, and we stay to live the results of the decisions we made. So let’s make sure our incentives for those decisions are the right ones.
The points listed above may sound simple and obvious, but no one could become immune to mistakes just by knowing them. The thing is that if you are not analyzing these points consciously when you are presented with new information, you are always at risk of succumbing to biases.
By mastering the skills needed to overcome those biases: monitoring reactions, recognizing patterns, evaluating, stripping away the non-important and reading the signs as they appear you will be able to ensure that your decisions are systematically correct. Having that as your second nature will allow you to reduce to a minimum the amount of personal resources you’ll be putting in it, freeing your mind for the next part, which is making conclusions and building plans needed to achieve what your mind is set for.
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- How good are you in making decisions? Part 1 – The Process
- How good are you in making decisions? Part 2 – Difference of making decision individually or in a group
- How good are you in making decisions? Part 3 – Manager’s role in the process of making decision
Your arguments are strong and well expressed, as always.
I’d like to take it one step further.
Research and analysis help me build mental models to predict the probable consequences of my planned actions in a given system: what will happen if I choose plan A, B or C?
To make a decision, I have to run the models in my head with the different data sets (A, B or C) and focus on how does it feel?
I choose the option that makes my belly feel fine.
Of course, I never explain my methods, or I would lose my magic.
Fungus, thank you so much for your comment.
You are lucky to have so much trust in your belly, supposedly because it is usually correct, however in this post I actually advised not to go with how it feels, but to listen to what the data actually says. For most people it would be the opposite of what their gut tells them.
Actually, it’s not luck.
First you have to train your brain to read data statistically right and listen carefully to what the data says.
Then, but only after you have built a solid model, you look inside, connect with your feelings and trust your instincts.
One word of advise: Don’t try this at home. I am a trained professional.
If that is the case, then I agree with you.
Because the other way around – firstly trusting (overrated) instincts and only then (maybe) looking into the real state of affairs – brings us the “unexpected” results.
Indeed, professionals are well sought-after:)
About “the other way around” … it may be valid under certain circumstances.
Edward de Bono states that a technique he calls Lateral Thinking builds skills to arrive at intuitive solutions to complex problems, which should always be validated thorough “rock logic”. That is: after you “feel” the right answer, you label it as tentative until you can build a logical road that leads to it.
Fungus I hear what you are saying. If you noticed Lateral Thinking approach calls for NOT jumping into conclusions and Bono’s approach calls for mapping all factors and reasoning as well. I’m not diminishing the importance of creative thinking that is not constrained by judgements; on the contrary it is one of the best ways, but there is a pitfall here and it is our tendency to substitute creativity with feeling. That misleads and contributes to illusion that gut feeling can constitue a good argument in decision process.
I think we are talking on the same approach, just I’m keen on calling things as they are to avoid additional abberations, as human beings have enough of them:)