Your job is at stake: Artificial Intelligence won’t take over the world anytime soon but Data already did
The Future workforce is not an Army of Data Scientists and Machine Learning Engineers. It’s a data analyst embedded in every job we have in the economy.
The need for understanding trends and patterns in data is dramatic and it’s here to stay.
One of the most prominent headlines you are constantly reading in the midst of all the current events is how many jobs have been lost. The vast majority of these jobs were lost due to the most dramatic consumer demand shock in recent history. However, anecdotally and more personally I think some of the layoffs we are currently experiencing were coming our way all along and were lumped into this crisis because it was the easy way out. Furthermore some of today’s job vacancies and a lot more in the future will remain unfilled due to a different type of skills gap. There is plenty of literature out there explaining how the automation of tasks will lead to long-term structural unemployment. This post is not about that. As sexy as it sounds and as much as I would like to write it about it Artificial intelligence won’t take over the world as much as we think it will at least not yet. However, the need for understanding trends and patterns in data is dramatic and it’s here to stay. It will shift the way we work and people who are unable to adapt and build data literacy skills will be left in the dust.
Undoubtedly the sheer number of digital events and signals that we generate and especially that businesses generate have exploded. There are multiple papers and posts out there estimating and quantifying the amount of data being generated. What this means is that data is now available not just for the few sophisticated technology companies that invested heavily in research and development instead it’s widely available for everyone. From the clicks and events on your website to the times you order food from your phone, the machinery in the average factory, your smart fridge, washer, and coffee machine all have a long digital footprint.
The need for Data Analysis
Even the professions you thought least, now rely heavily in understanding data analysis and signals processing.
Every job will require being a data analyst. I’m not saying that every person in the world would require a degree in statistics or be able to solve quadratic equations instead every discipline regardless of industry will require basic data analysis knowledge. Even the professions you thought least, now rely heavily on understanding data analysis and signal processing.
The following type of jobs are not being replaced by machines, sadly they are being replaced by no one due to the lack of data literacy.
The mechanic of tomorrow must be able to not only know what those codes are and how to fix them, but also ideally be able to do pattern analysis to prevent an issue from happening in the first place.
Allegedly one of the last jobs to be replaced by automation and fancy algorithms are repair technicians. The logic is that the requirements of manual dexterity and nuanced diagnostics required for every piece of equipment is too hard to teach to a machine. Typically the tasks to repair a piece of equipment may include the need to see, touch and obverse overtime all of which cannot simply be modeled and trained with an image processing neural network.
However, the complexity of repairing such devices continues to increase and the need to understand how an electronic control board sends gasoline to your car’s engine makes an adjustment to a valve that affects your air conditioner’s refrigerant, modifies the way your washer machine dries or moves the flaps on an airplane landing is more critical than ever.
So you may be wondering what does a car mechanic with data analysis skills look like? It goes something like this. You may be familiar with a car code reader. In the past, you or someone you know possibly went to a car parts store and one of the clerks connected a device that spitted a bunch of codes back. That’s the simplified way. In practice that machine is reading and analyzing electrical signals from sensors all over your car and those electrical signals are equivalent to some code which is a flaw in the vehicle. The mechanic of tomorrow must be able to not only know what those codes are and how to fix them but also ideally be able to do pattern analysis to prevent an issue from happening in the first place.
The teacher of the future must be able to build an adaptive curriculum
Good teaching requires constant creative taught and adaptability to student’s needs that evolve over time. You can see where I’m going with this, you can create content and make it available online but fostering interest by a student and ensuring those who fall behind to catch up is what defines a memorable teacher from a mediocre one. While these things may also not be automated any time soon via an algorithm, the need to look at all the signals of a student’s level of comprehension and overall progression will require an increasing understanding of data analysis.
Our education system is broken. It’s designed so that one person — the teacher — presents the class with information. It happened already before but as it’s more evident now that each student’s level of knowledge and understanding of any given topic can vary. Excluding instances of learning disabilities an average class will also end up with some sort of normal distribution where the content is not advanced enough for the top of the class, just enough for most of the class but overwhelming and confusing to the bottom of the class. In a traditional teaching environment, it’s very hard to measure progress as it’s often delayed and more importantly it’s hard to separate the effort needed to keep the top students in the class engaged and ensure the bottom students in the class don’t continue to lag behind.
Electronic tools and evaluation applications allow capturing feedback in real-time. But the teacher of the future must be able to build an adaptive curriculum, know how to evaluate student’s performance, and more importantly be able to analyze and take action on all the data that is generated by these activities.
Foodservice is one of the largest components of our economy. As we may be shifting to a reduction of physical dining experiences, the skill sets will also have to evolve. If a large percentage of a restaurant’s business comes from online ordering the skills needed are no longer a gracious amicable server that makes your patrons consume more, instead, it is having a data-savvy chef and manager that can predict demand patterns and keep up with your orders log. Being able to catch and predict seasonality patterns of your restaurant’s food demand is what will set apart one restaurant from another.
Where do we go from here?
It’s clear that data literacy is a must-have skill in the modern economy. The appropriate training across professions needs to bridge this gap in the coming years. I have a few ideas, more on that coming soon.