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The worth of an education – a metalogue

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To be prepared against surprise is to be trained. To be prepared for surprise is to be educated.” – James Carse in Finite and Infinite Games.

Prospective student: How much do your students earn after they graduate?

Teacher: I’m sorry, I don’t know.

Prospective student: What do you mean? Don’t you keep track of what students do after they complete the program?

Teacher: Yes, we do, but we don’t ask them how much they earn. It is not the kind of question one asks….

Prospective student:  I know that, but shouldn’t you be gathering hard data on outcomes?

Teacher:  Of course, and we gather a range of data including the roles they get on graduation and how they progress in their careers.

Prospective student:  OK, but that doesn’t help me. How do I know that your degree is worth the hefty fees you charge?  If I’m investing that much, I must be sure of a decent return.

Teacher:  One cannot be certain about anything in life, there are never any guarantees. However, it’s perfectly reasonable to expect value from your education, so let me ask you – what would you consider a decent return?

Prospective student:   Hmm, a good job, I guess.

Teacher: Well, you heard from some of our alumni this evening.  They are currently employed as professionals in the field, and most of them got their jobs while studying or soon after completing their degree.

Prospective student: Yes, but you are unlikely to invite those who would say negative things about the program or those who have failed to get jobs.

Teacher: Fair enough, there are a few of those.

Prospective student: Well, that’s just my point. How do I know it will work for me?

Teacher:  You don’t!  Getting a job is an indirect effect of a good education.

Prospective student: I’m not sure I follow.

Teacher: An analogy might help clarify what I mean by indirect, Bill Gates did not become a multi-billionaire by setting out to become one. He became one by following his interests, his passion.

Prospective student: [a tad irritated] I still don’t get it.

Teacher: The objective of education is not to train you for a vocation…although, if you do things right, you are almost certain to get a job, and a good one at that. The aim of education is personal transformation, to broaden your perspective and thus enable you to look anew at the things you do at work and, possibly, even in life. Another aim is to prepare you to become that buzzword: a lifelong learner. No university can teach you those things, but they can help you learn them.

Prospective student: “personal transformation” sounds wonderful but terribly vague. Could you give me an example?

Teacher: Well, you’ve just heard from a few of our alumni and I could tell you many more stories. The thing is, I don’t think it is helpful to hear second-hand stories – their journeys are theirs, not yours. The story I’m interested in right now is yours: what you’re thinking, what you do and where you want to go.

Prospective student: OK. I’m a financial analyst [Editor’s note: feel free to substitute your current profession here] and I want to be a data scientist. Will this course enable me to become one?

Teacher: It could, but whether or not it actually does depends largely on you.

Prospective student: That’s not an answer.

Teacher: It is, and it’s an honest one. No course will make you a data scientist. And if any university tells you they can, they’re lying. What a good university course will do is help you learn the technical and non-technical skills that will enable you to become a data scientist.  Whether you learn or not depends on you. The responsibility for your personal transformation lies largely with you. All we can do is show you the way.

Prospective student: so, do you cover … [student recites a litany of data science languages and techniques].

Teacher: Yes, we cover them.

Prospective student:  Won’t doing those make me a data scientist?

Teacher: No. If tech skills are all you are after, I’d strongly suggest you don’t join our program…or any other university program for that matter. Instead, head off to one of the good online data science education providers and save yourself a whole lot of money.

Prospective student: Huh?

Teacher:  A good face-to-face program at a university covers a whole lot more than tech. For example, there are certain tacit skills and dispositions that are critical to becoming a good data scientist. These skills have to do with problem finding rather than technical adeptness or problem solving.  A good university course will give you opportunities to gain experience in doing that.

Prospective student; Problem finding? What’s that?

Teacher: In university assignments you’re given readymade problems that you can go off and solve. In real life, however, you are rarely given a problem. More often, you are presented with a situation from which you must extract or formulate a problem before you solve it. That’s not always a straightforward process because every situation is unique in its details.

Prospective student: If every situation is unique then there is no formula to deal with it.

Teacher: Exactly! These skills have to be taught indirectly – by putting students in safe-to-fail situations in which they can learn how to deal with the ambiguity inherent in them

Prospective student: But won’t that be throwing students into a situation they’re unprepared for.

Teacher: Although they may not admit it, most consultants – even experienced ones – rarely feel totally in control when dealing with new clients. It’s good to experience that kind of ambiguity early in one’s career, even if it is a second career. Every consulting engagement is a learning experience. This ties in with what I mentioned earlier – becoming a lifelong learner.

Prospective student: So how do you prepare students to deal with these types of scenarios?

Teacher: Through carefully crafted technical and non-technical subjects, with assignments that make them think rather than just do. To do well in the assignments you will have to think things through, try different approaches and even make judgement calls.

Prospective student: Judgement calls?

Teacher:  Yes, that’s right. You will find that the biggest issues when doing data science in the real world are not technical, rather they are about dealing with ambiguous situations in which you don’t have a well-defined problem or adequate data.  Then there are ethical issues that are becoming ever more important today. There are big corporations that completely ignore the ethical implications of what they do. Just because you can do something, it doesn’t mean you should.  All these issues involve judgement calls in which data is of little or no help.

Prospective student: Hmm, I didn’t realise there were so many facets to being a data scientist. Thanks, you’ve given something to think about.

Teacher: No worries…and good luck, I hope you find what you’re looking for.

 

 

Afterword:

HR gurus and consultants continually pontificate about the future of work. The ground reality for many mid-career professionals is that the future of their work is highly uncertain, much of the uncertainty being fuelled by the perception that data-related technologies are going to “disrupt” established industries. Among other things, this has led to an unprecedented demand for courses that teach data-related skills.

What is often left unsaid, however, is the transition to data science – or any profession for that matter – involves more than just picking up technical skills. The biggest missing piece (in my opinion) is the ability to make sense of ambiguous situations. This is a tacit skill that is difficult, if not impossible, to teach but can be learnt given the right environment and attitude. The university ought to provide the environment, the student the attitude.

Note: A metalogue is a dialogue that unfolds in such a way that the structure of the conversation turns out to be illustrative of the issue being discussed. The anthropologist Gregory Bateson coined the term.  Here is a metalogue written by him.

Written by K

February 18, 2020 at 5:40 am

Posted in Metalogues, Organizations, sensemaking

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