Note: As a standard practice, I will include a plagarism detection link in all the articles on my blog site. You can use the online tool below to evaluate any article published on the web for plagarized content. All you have to do is to paste the url of the article there and it will go through every sentence in the article and flag any plagarism. I believe as a follower of “writer’s integrity” I should include this tool in all my articles:
I prefer hard copy versions of books vs Digital versions. And since I collect a lot of books, and those books are slowly taking over our basement (which is also my study), it irritates my wife. She thinks this a hypocrisy on my part that on one hand I champion Digital Transformation on the internet yet on the other hand, I prefer hard copy books.
The costly side of curiosity
But the primary reason I prefer reading from hard copy books is to avoid the trap my curiosity sets for me. My mind wanders a lot when I start reading a book these days. So if I am reading about an algorithm, I may think about a related application in Supply Chain, and then go on a tangent from there. Or I may read about a specific Supply Chain case study, and then start researching what the company in the case study did after the time period mentioned, with whatever they were implementing etc. etc. An hour later, I may be reading something related to the book yet completely different in perspective.
This is the primary reason that despite being a Digital enthusiast, I buy hard copy books. Otherwise, with a digital book, as soon as I find something interesting, my curiosity gets the best of me. I will open a browser and start researching and then one topic leads to another. I may never be able to go beyond few pages of a Digital book. The only way for me to finish a book or a chapter is to stay away from screen, with hard copy of the book in my backyard garden or my son’s room. If I want to finish a relatively long White paper in one sitting, I print it out. (I don’t do this for long research papers though- in those scenarios, I just skim through it and jot down important aspects .)
But is “look just for relevant stuff” a bad thing from a learning perspective ?
But one positive aspects of these random wanderings is that it sometimes leads to some decent ideas from which I cull aspects that I believe I can leverage. Most of them may not be implementable (currently) but it is fun to draw such insights from wandering.
The key is, “wandering” is my way of quickly collecting relevant insights about a topic, without going through the 68 remaining pages in the chapter, and in the process, also collect information that was not even there. And the same goes for any academic programs or certifications I have pursued as an experienced professional.
For example, as some of you may know, I am pursuing an online MSc. in Artificial Intelligence. The purpose was to be able to cull out ideas to develop AI enabled solutions in Supply Chains (I already have two masters so was not exactly passionate about a third one). Now-unlike most other folks, my approach to academic programs, since I started working (i.e since I was no longer a “full time” student), has been different. Whether it is a graded program or not, I aim to extract facts that I can use and “browse” through the content. This helps me in two aspects:
(1) Unlike the kids working towards these diplomas or degrees, I don’t need to chase gpa. Going through each and every topic and word, or preparing for an exam, in order to score” high” is a not a smart move for someone who is pursuing the course from a different perspective. Browsing allows me to see what information is useful for me and what is not (despite it being part of the coursework).
For example- I am currently studying Natural Language Processing (NLP). Now as someone who will never actually code a production level spam detector myself- do I need to go throught the coding tutorial so many times so that I can reproduce it ? Not in my perspective. I will skim it- I should know which Python libraries are involved, what the algorithm is actually doing. Learn that and move over !
Now, I hear from some that having ” in depth” knowledge will help if the model is performing weird. Think realistically- if the model is performing weird due to the code, and if a Machine learning engineer wrote the code, probability of someone who learnt the code out of interest being able to troubleshoot it is low. I should be able to decipher the results to understand if aspects like feature maps could have been done differently. But if I have not risen through the ranks programming as my profession, I may not be able to find a programming error a qualified, proficient programmer is making. Heck, I can just request another senior engineer to eyeball and troubleshoot.
The key is to understand what to learn, which topics to learn to a certain depth and what topics you should avoid completely.
(2) My mind wanders a lot when I study a topic these days. So if I am reading about an algorithm, I may think about a related application in Supply Chain, and then go on a tangent from there. And this is also not a bad thing (if you ignore “finishing the book” aspect). I believe our mental boundaries come into play only because we research things too deep but not broad. The reason 80% of PhD papers never transformed into actionable solutions/products was because of the topic intensity of research. Highly integrated nature of processes today mean that there are so many aspects coming into play that obsession of digging a rabbit hole with one topic is suicidal in my opinion.
Totally unrelated- Reminds me of an example. I had a PhD as a manager in one of my projects. On a call, where I asked for his help to escalate a data dump I had received, saying that data that I have has more than 5 million transactions, whereas we know it should have been less than 2 million, he first asked me “Exactly” how many transactions does the dataset has. When I mentioned that whether it is 5.1 or 5.2 million may not be relevant, since the crux is we have way more than expected, he got really furuious. We spent the call discussing why I should have known that the exact number was 5,116,300. In my opinion, we should have resolved why we don’t have less than 2 milion. This was a data file we had received, not even a query that we wrote ourselves (in which case we could have checked if we were pulling duplicates or triplicates).
“Agile” is not only a project management methodology but a way of mind, habit and a necessary culture in today’s world of rapid problem solving and solution development. And “wandering” (my definition of it), may be essential.
Cut the cr*p Kumar…what exactly is the Ed Tech goldmine opportunity?
Everytime I go through an online program, most of which are being offered so that professionals working full time can pursue them- are not designed optimally for experienced professionals. And this makes me think about a huge opportunity outside Supply Chain- in Ed Tech. A huge opportunity. Another goldmine that is currently untapped. And I say untapped is not because there are not enough courses out there. There are zillions now (and that is one of the problems), but most of them are not strutured for “quick absorption”. I already shared my approach to learning above but here is an example from someone on LinkedIn in my network, who is currently pursuing another online certificate in AI and he regularly posts the updates.
This person, a very senior guy, despite being in technology, is doing things in this program that he does not need to, must not and should not do. Yet, he is. Because he is pursuing a curiculum that is very generically designed. And if he really really wants to do hands on Kunbernetes conformance programming, then its ok but in all probability, considering the work he does- he does not.
There are four aspects of any Technical topic in my perspective – Context, Theory, Architecture and code (if required). Btw….None of these are defined categories anywhere, I just made these categories myself. And depending on what role you are in, what roles you may tangent into and what your interest areas are, you may not need to be solid in all four. You must not be actually, in my “unconventional” opinion- you will end up micromanaging Tech initiatives, frustrating yourself and your staff. The world today needs Smart problem solving and solutions development.
So, the crux of this rambling here is that there are no exclusively designed Ed Tech platform products for experienced professionals, by domain. And the companies that are trying to build platforms in this segment are going through the conventional route of leveraging Instructional designers to design these courses. The result is either a short term course that participants may not end up leveraging at all or long term programs that have too much noise for a working professional. But whoever cracks the code and develops a perfect “reskilling” , domain specific learning platforms for working professionals (say for SCM or Marketing or Finance), will mine big gold nuggets for quiet a few years.
Views expressed are my own.